Patent Publication Number: US-11389102-B2

Title: Systems, devices, components and methods for detecting the locations of sources of cardiac rhythm disorders in a patient&#39;s heart

Description:
RELATED APPLICATIONS 
     This application is related to, and claims priority and other benefits from: (a) U.S. Provisional Patent Application Ser. No. 62/784,605 entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart” to Ruppersberg filed Dec. 24, 2018 (hereafter “the ‘605 patent application”), and (b) U.S. Provisional Patent Application Ser. No. 62/875,452 entitled “Methods, Systems, Devices and Components for Detecting and/or Classifying Atrial Fibrillation in a Patient&#39;s Heart Using Non-invasive Body Surface Electrodes and Machine Learning Methods” to Haeusser et al. filed Jul. 17, 2019 (hereafter “the ‘452 patent application”). 
     This application is also a continuation-in-part of, and claims priority and other benefits from: (a) U.S. Patent Application Ser. No. 16,168,235 to Ruppersberg filed on Oct. 23, 2018 (now U.S. Pat. No. 10,806,343), which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart” (hereafter “the ‘235 patent application”). The &#39;235 patent application claims priority and other benefits from: (b) U.S. patent application Ser. No. 15/258,410 to Ruppersberg filed on Sep. 7, 2016 (now U.S. Patent No. 10,143,374), which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart” (hereafter “the ‘410 patent application”). The ‘410 patent application claims priority and other benefits from: (c) International Patent Application PCT/EP2015/001801 to Ruppersberg filed on Sep. 7, 2015, which is entitled “Elongated Medical Device Suitable for Intravascular Insertion and Method of Making an Elongated Medical Device Suitable for Intravascular Insertion” (hereafter “the ‘001801 patent application”), and (d) International Patent Application PCT/EP2015/001803 to Ruppersberg filed on Sep. 7, 2015, which is entitled “Elongated Medical Device Suitable for Intravascular Insertion and Method of Making an Elongated Medical Device Suitable for Intravascular Insertion” (hereafter the ‘001803 patent application”).
         This application is further a continuation-in-part of, and claims priority and other benefits from: (e) U.S. patent application Ser. No. 15/548,671 to Ruppersberg filed on Aug. 3, 2017 (now U.S. Pat. No. 10,201,277), which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart” (hereafter “the ‘671 patent application”). The ‘671 patent application claims priority and other benefits from: (f) International Patent Application PCT/IB2016/001273 to Ruppersberg filed on Sep. 7, 2016, which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart” (hereafter “the ‘1273 patent application”). In turn, the ‘1273 patent application claims priority and other benefits from the ‘001801 and ‘001803 patent applications.       

     This application is also a continuation-in-part of, and claims priority and other benefits from: (g) U.S. patent application Ser. No. 15/923,286 to Ruppersberg filed on Mar. 16, 2018 (now U.S. Patent No. 10,820,800), which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart and Classifying Same” (hereafter “the ‘286 patent application”). The ‘286 patent application claims priority and other benefits from: (h) U.S. Provisional Patent Application Ser. No. 62/473,137 filed on Mar. 17, 2017, which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart Using Optical Flow” (hereafter “the ‘137 patent application”), and (i) U.S. Provisional Patent Application Ser. No. 62/486,387 filed on Apr. 17, 2017, which is entitled “Systems, Devices, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient&#39;s Heart Using Optical Flow” (hereafter “the ‘387 patent application”). The respective entireties of each of the ‘605, ‘452, ‘235, ‘410, ‘001801, ‘001803, ‘671, ‘1273, ‘286, ‘137, and &#39;387 patent applications are hereby incorporated by reference herein. In addition, this patent application claims priority and other benefits from each of the ‘605, ‘452, ‘235, ‘410, ‘001801, ‘001803, ‘671, ‘1273, ‘286, ‘137, and ‘387 patent applications. 
    
    
     FIELD OF THE INVENTION 
     Various embodiments described and disclosed herein relate to the field of medicine generally, and more particularly to diagnosing and treating cardiac rhythm disorders in a patient&#39;s heart using electrophysiological mapping and/or electrographic flow (EGF) techniques, as well as in some embodiments using imaging, navigation, cardiac ablation and other types of medical systems, devices, components, and methods. Various embodiments described and disclosed herein also relate to systems, devices, components and methods for discovering with enhanced precision the location(s) of the source(s) of different types of cardiac rhythm disorders and irregularities in a patient&#39;s heart, such as, by way of example, active rotors, passive rotors, areas of fibrosis, breakthrough points and focus points. 
     BACKGROUND 
     Persistent atrial fibrillation (AF) is assumed to be caused by structural changes in atrial tissue, which can manifest themselves as multiwavelet re-entry and/or stable rotor mechanisms (see, e.g., De Groot M S et al., “Electropathological Substrate of Longstanding Persistent Atrial Fibrillation in Patients with Structural Heart Disease Epicardial Breakthrough,” Circulation, 2010, 3: 1674-1682). Radio frequency (RF) ablation targeting such host drivers of AF is generally accepted as the best therapeutic approach. RF ablation success rates in treating AF cases are currently limited, however, by a lack of diagnostic tools that are capable of precisely determining the source (or type), and location, of such AF drivers. Better diagnostic tools would help reduce the frequency and extent of cardiac ablation procedures to the minimum amount required to treat AF, and would help balance the benefits of decreased fibrillatory burden against the morbidity of increased lesion load. 
     One method currently employed to localize AF drivers is the TOPERA® RhythmView® system, which employs a basket catheter having 64 electrodes arranged in an 8×8 pattern from which the system records unipolar electrograms or electrogram signals (EGMs). The RhythmView® algorithm creates a propagation map of the 64 electrodes through a phase analysis of EGM peaks after improving the signal to noise ratio through filtering and subtraction of a simulated compound ECG artifact. The RhythmView® algorithm detects where peak sequences between electrodes show a circular pattern candidate for a re-entry cycle and indicates those locations in a Focal Impulse and Rotor Map (FIRM) using A 1  to H 8  chess field coordinates for the electrodes. The resolution of the TOPERA system is limited by the spacing of the electrodes and consequently does not show the details of the AF drivers. In particular, the TOPERA system cannot show if a circular EGM wavefront is actively generated by a re-entry mechanism and is therefore is a driver of AF (i.e., an active rotor), or whether a circular EGM wavefront simply represents turbulence passively generated by an EGM wavefront hitting a barrier (i.e., a passive rotor). In addition, the TOPERA system does not show the direction of AF wavefront propagation, and does not provide the spatial or temporal resolution required to detect singularities associated with the generation of an active rotor. 
     A recent independent multicenter study (“OASIS, Impact of Rotor Ablation in Non-Paroxysmal AF Patients: Results from a Randomized Trial,” Sanghamitra Mohanty, et al. and Andrea Natale, J Am Coil Cardiol. 2016) reported that the results obtained using TOPERA FIRM technology were inferior to those provided by non-specific ablation of the posterior wall of the left atrium. Moreover, the results suggested that FIRM based ablation is not sufficient for therapeutic success without pulmonary vein isolation (PVI) being performed in parallel. Although there are some questions about the methodology of this trial, many experts are convinced that the resolution and interpretability of the TOPERA system need to be improved. 
     In another approach to the problem, Toronto scientists recently presented a strategy to analyze EGM wave propagation using “Omnipolar Mapping,” which seeks to measure beat-by-beat conduction velocity and direction (see, e.g., “Novel Strategy for Improved Substrate Mapping of the Atria: Omnipolar Catheter and Signal Processing Technology Assesses Electrogram Signals Along Physiologic and Anatomic Directions,” D. Curtis Deno et al. and Kumaraswamy Nanthakumar, Circulation. 2015; 132:A19778). This approach starts with the time derivative of a unipolar EGM as measured by a set of electrodes having known distances to one other. Assuming constant velocity, the velocity and direction representing the best fit for a spatial derivative of the measured EGM are calculated and used to represent an estimate of the E field. According to a communication by Dr. Nanthakumar at the 2016 CardioStim Convention in Nice, France, however, this method remains incapable of dealing successfully with complex data sets, such as those obtained during an episode of AF. 
     AF is the most common supraventricular tachyarrhythmia worldwide and is associated with a significant health burden. Catheter ablation of pulmonary veins (PV) has been established as a therapeutic option for patients with symptomatic drug-refractory paroxysmal AF and results in high clinical success. However, the treatment of persistent and long-standing persistent AF is still challenging. A large number of patients present with recurrence of atrial tachyarrhythmia during mid- and long-term follow up. To achieve higher success rates, different ablation strategies have been reported, such as targeting additional AF sources. The initial results of focal impulse and rotor (FIRM) mapping for guiding catheter ablation of AF seemed to be promising. However, currently available systems for AF driver identification still have significant limitations, such as limited spatial resolution and difficulties in discriminating between active and passive rotors. 
     What is needed are improved means and methods of acquiring and processing intracardiac electrogram signals that reliably and accurately yield the precise locations and sources of cardiac rhythm disorders in a patient&#39;s heart. Doing so would enable cardiac ablation procedures to be carried out with greater locational precision, and would result in higher rates of success in treating cardiac rhythm disorders such as AF. 
     SUMMARY 
     Electrographical flow (EGF™) mapping represents a novel and highly useful advance in atrial fibrillation detection and analysis technology. EGF allows a full spatial and temporal reconstruction of electrographic potentials to be derived from endocardial unipolar electrogram data collected with a basket catheter. EGF can also be used to reconstruct electrographic potentials, and in particular the locations of focal impulses and rotors, using body surface electrodes. EGF allows AF drivers to be identified based on flow analysis, where sources of excitation occurring during AF can be characterized and monitored. 
     In one embodiment, there is provided a system configured to detect at least one location of at least one source of at least one cardiac rhythm disorder in a patient&#39;s heart, the system comprising: (a) at least one computing device; (b) at least one data acquisition device operably connected to the at least one computing device or configured to provide as outputs therefrom body surface electrogram signals; (c) a plurality of body surface electrodes configured to generate body surface electrogram signals and for placement on the patient&#39;s body surface, the plurality of body surface electrodes being operably connected to the at least one data acquisition device, and (d) a display or monitor operably connected to the at least one computing device and configured to visually display to the user one or more vector maps generated by the at least one computing device; wherein the computing device comprises at least one non-transitory computer readable medium configured to store instructions executable by at least one processor to determine the at least one location of the at least one source of the at least one cardiac rhythm disorder in the patient&#39;s heart, the computing device being configured to: (i) receive the body surface electrogram signals from the plurality of body surface electrodes located on the patient&#39;s body, where amplitudes of the body surface electrogram signals received by the at least one computing device have been at least one of conditioned, amplified, normalized, filtered, and adjusted by the data acquisition device before being provided to the computing device; (ii) assign or relate positional data corresponding to predetermined positions of the body surface electrodes on the patient&#39;s body to their respective corresponding body surface electrogram signals and body surface electrodes; (iii) generate at least one spatial map of the body surface electrode positions; (iv) for each or selected discrete times over which the body surface electrogram signals are being processed, process the amplitude-adjusted body surface electrogram signals to generate a plurality of electrogram surfaces or data grids, each such surface or data grid corresponding at least partially to the at least one spatial map, at least one surface or data grid being generated for each such time, and (v) process the plurality of electrogram surfaces or data grids through time to generate at least one vector map corresponding at least partially to the spatial map, the at least one vector map being configured to reveal the at least one location of the at least one source of the at least one cardiac rhythm disorder, the at least one vector map being shown to the user on the display or monitor. 
     In another embodiment there is a provided a method of detecting at least one location of at least one source of at least one cardiac rhythm disorder in a patient&#39;s heart using a system comprising at least one computing device, the computing device comprising at least one non-transitory computer readable medium configured to store instructions executable by at least one processor to determine the at least one location of the at least one source of the at least one cardiac rhythm disorder in the patient&#39;s heart, the system further comprising a plurality of body surface electrodes operably connected to the computing device through a data acquisition device and a monitor or screen operably connected to the computing device, the method comprising: (a) acquiring body surface electrogram signals using the body surface electrodes located on one or more body surfaces of the patient; (b) using at least one of the computing device and the data acquisition device, at least one of conditioning, filtering, normalizing and adjusting the amplitudes of the acquired body surface electrogram signals; (c) using the computing device, assigning positions or identifiers for each of the body surface electrodes to corresponding individual body surface electrogram signals; (d) using the computing device and the assigned positions or identifiers, providing or generating a spatial map of the body surface electrode positions; (e) using the computing device, for each or selected discrete times over which the body surface electrogram signals are being processed, processing the amplitude-adjusted body surface electrogram signals to generate a plurality of electrogram surfaces or data grids corresponding at least partially to the spatial map, one surface or data grid being generated for each such time, and (f) using the computing device, processing the plurality of electrogram surfaces or data grids through time to generate a vector map corresponding at least partially to the spatial map, the vector map being configured to reveal on the monitor or display to a user the at least one location of the at least one source of the at least one cardiac rhythm disorder. 
     In yet another embodiment, there is provided a system configured to determine and display to a user one or more predictions or results concerning atrial fibrillation in a patient&#39;s heart, the system comprising: (a) at least one computing device; (b) at least one data acquisition device operably connected to the at least one computing device or configured to provide as outputs therefrom body surface electrogram signals; (c) a plurality of body surface electrodes configured to generate body surface electrogram signals and for placement on the patient&#39;s body surface, the plurality of body surface electrodes being operably connected to the at least one data acquisition device, and (d) a display or monitor operably connected to the at least one computing device and configured to visually display to the user the predictions or results concerning the atrial fibrillation generated by the at least one computing device; wherein the computing device comprises at least one processor and at least one non-transitory computer readable medium configured to store instructions executable by the at least one processor to determine, using a trained atrial discriminative machine learning model, the predictions or results concerning atrial fibrillation in the patient&#39;s heart, the computing device being configured to: (i) receive the body surface electrogram signals from the plurality of body surface electrodes located on the patient&#39;s body, where the body surface electrogram signals received by the at least one computing device have been at least one of conditioned, amplified, normalized, filtered, and adjusted by the data acquisition device before being provided to the computing device as conditioned electrogram signals; (ii) assign or relate positional data corresponding to positions or estimated positions of the body surface electrodes on the patient&#39;s body to their respective corresponding body surface electrogram signals and body surface electrodes; (iii) input the conditioned electrogram signals and positional data into the trained atrial discriminative machine learning model, where the trained atrial discriminative machine learning model has been trained at least partially using data obtained from a plurality of other previous patients, where intracardiac electrophysiological (EP) mapping signals for the other patients have been processed using electrographic flow (EGF) methods to detect at least one of: (I) the presence of sources of atrial fibrillation in the other patients&#39; hearts; (II) the locations of sources of atrial fibrillation in the other patients&#39; hearts; (III) the activity levels of sources of atrial fibrillation in the other patients&#39; hearts; (IV) the spatial variability levels of sources of atrial fibrillation in the other patients&#39; hearts; (V) the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts; and (VI) the classification of patients as at least one of types A, B and C; where paired data sets of body surface electrogram signals and the intracardiac EP mapping signals have been acquired simultaneously from at least some of the plurality of other patients and the paired data sets have been correlated to one another using the atrial discriminative trained machine model, and the trained atrial discriminative machine learning model is further configured to generate one or more of the following predictions or results for the patient using the conditioned electrogram signals and positional data corresponding to the patient: (1) Does the patient have atrial fibrillation or not? (2) If the patient has atrial fibrillation, determining at least one of the spatial variability level, the activity level, and the flow angle stability level associated with one or more sources detected in the patient&#39;s heart; (3) If the patient has atrial fibrillation, determining the locations of one or more sources detected in the patient&#39;s heart; (4) If the patient has atrial fibrillation, whether one or more activation sources detected in the patient&#39;s heart are characterized by chaotic flow; and (5) classification of the patient as one of types A, B and C; and further wherein the computing device is configured to: (iv) process the conditioned electrogram data and positional data in the trained machine learning model to generate the one or more predictions or results; and (v) display the one or more predictions or results on the display or monitor to the user. 
     In still another embodiment, there is provided a method of determining and displaying to a user one or more predictions or results concerning atrial fibrillation in a patient&#39;s heart using a system comprising at least one computing device, at least one data acquisition device operably connected to the at least one computing device or configured to provide as outputs therefrom body surface electrogram signals, a plurality of body surface electrodes configured to generate body surface electrogram signals and for placement on the patient&#39;s body surface, the plurality of body surface electrodes being operably connected to the at least one data acquisition device, and a display or monitor operably connected to the at least one computing device and configured to visually display to the user the predictions or results concerning the atrial fibrillation generated by the at least one computing device, the computing device comprising at least one processor and at least one non-transitory computer readable medium configured to store instructions executable by the at least one processor to determine, using a trained atrial discriminative machine learning model, the predictions or results concerning atrial fibrillation in the patient&#39;s heart, the method comprising: (a) acquiring body surface electrogram signals using the body surface electrodes located on one or more body surfaces of the patient; (b) using at least one of the computing device and the data acquisition device, at least one of conditioning, filtering, normalizing and adjusting the amplitudes of the acquired body surface electrogram signals; (c) using the computing device, assigning positions or identifiers for each of the body surface electrodes to corresponding individual body surface electrogram signals; (d) using the computing device and the assigned positions or identifiers, providing or generating a 2D or 3D spatial map of the body surface electrode positions; (e) using the computing device and the trained atrial discriminative machine learning model, inputting the conditioned electrogram signals and positional data into the trained atrial discriminative machine learning model, where the trained atrial discriminative machine learning model has been trained at least partially using data obtained from a plurality of other previous patients, intracardiac electrophysiological (EP) mapping signals for the other patients have been processed using electrographic flow (EGF) methods to detect at least one of the presence of sources of atrial fibrillation in the other patients&#39; hearts, the locations of sources of atrial fibrillation in the other patients&#39; hearts, the activity levels of sources of atrial fibrillation in the other patients&#39; hearts, the spatial variability levels of sources of atrial fibrillation in the other patients&#39; hearts, the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts, and the classification of patients as at least one of types A, B and C, where paired data sets of body surface electrogram signals and intracardiac electrophysiological (EP) mapping signals have been acquired simultaneously from at least some of the plurality of other patients and the paired data sets have been correlated to one another using machine learning, and the trained atrial discriminative machine learning model is to generate one or more of the following predictions or results: (1) Does the patient have atrial fibrillation or not? (2) If the patient has atrial fibrillation, determining at least one of the spatial variability level, the flow angle stability level, and the activity level associated with one or more sources detected in the patient&#39;s heart; (3) If the patient has atrial fibrillation, determining the locations of one or more sources detected in the patient&#39;s heart; (4) If the patient has atrial fibrillation, whether one or more sources detected in the patient&#39;s heart are characterized by chaotic behavior; (f) processing the conditioned electrogram data and positional data in the trained atrial discriminative machine learning model generate the one or more predictions or results; and (g) displaying the one or more predictions or results on the display or monitor to the user. 
     Further embodiments are disclosed herein or will become apparent to those skilled in the art after having read and understood the claims, specification and drawings hereof. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. 
       Different aspects of the various embodiments will become apparent from the following specification, drawings and claims in which: 
         FIG. 1( a )  shows one embodiment of a combined cardiac electrophysiological mapping (EP), pacing and ablation system  100 ; 
         FIG. 1( b )  shows one embodiment of a computer system  300 ; 
         FIG. 2  shows an illustrative view of one embodiment of a distal portion of catheter  110  inside a patient&#39;s left atrium  14 ; 
         FIG. 3  shows an illustrative embodiment of a mapping electrode assembly  120 ; 
         FIG. 4  shows one embodiment of a method  200  of detecting a location of a source of at least one cardiac rhythm disorder in a patient&#39;s heart; 
         FIG. 5( a )  shows a simple rotor model; 
         FIG. 5( b )  shows sensed artifacts in electrogram signals; 
         FIG. 5( c )  shows the artifacts of  FIG. 5( b )  superimposed on simulated ECG signals; 
         FIG. 5( d )  shows a box plot corresponding to an 8×8 array of 64 electrode signals; 
         FIG. 5( e )  shows the data of  FIG. 5( d )  after they have been subjected to an electrode signal normalization, adjustment and filtering process; 
         FIG. 5( f )  shows a surface generated from the data shown in  FIG. 5( e ) ; 
         FIG. 5( g )  shows wavefront velocity vectors; 
         FIGS. 6( a ) through 6( c )  show details regarding one embodiment of a method  200  shown in  FIG. 4 ; 
         FIGS. 7( a ) through 7( j )  show the results of processing simulated atrial cardiac rhythm disorder data in accordance with one embodiment of method  200 ; 
         FIGS. 8( a ) and 8( b )  show velocity vector maps generated from actual patient data using different time windows and method  200 ; 
         FIG. 9  shows another vector velocity map generated from actual patient data using of method  200 ; 
         FIGS. 10( a ) through 10( d )  show further results obtained using actual patient data; 
         FIG. 11( a )  shows one embodiment of an example of EGF data processing and analysis  600 ; 
         FIG. 11( b )  shows EGF results obtained in a Type-A patient before and after intra-cardiac ablation has been performed on a detected leading source. 
         FIG. 11( c )  shows some EGF results obtained in a pilot study; 
         FIGS. 11( d ) through 11( f )  show EGF results obtained in selected patients from EGF studies; 
         FIGS. 11( g ) and 11( h )  summarize EGF results and conclusions from EGF studies; 
         FIGS. 12( a ) and 12( b )  illustrate two different embodiments of a combined extracorporeal body surface electrode EGF and/or cardiac electrophysiological mapping (EP), pacing and ablation system  100 ; 
         FIGS. 12( c ) and 12( d )  show respective anterior and posterior views of a patient&#39;s thorax with vest  420  worn on or attached thereto. 
         FIG. 13  shows a generalized method  500  of employing EGF techniques in conjunction with body surface electrodes  430 ; 
         FIGS. 14( a ) and 14( b )  show examples of EGF analysis carried out using body surface electrodes  430  and EGF techniques 
         FIG. 15  shows some benefits accruing to one embodiment of an ABLACON EGF analysis system  700 ; 
         FIG. 16  shows one embodiment of an ABLACON diagnosis and treatment system  800 ; 
         FIG. 17  shows one embodiment of a simplified machine learning system  900  and a corresponding generalized machine learning workflow, and 
         FIG. 18  shows a block diagram and data flow diagram according to one embodiment of a body surface and intra-cardiac electrode machine learning system that employs an atrial discriminative training (ADT) machine learning model (MLM) that works in combination with a loss or cost function module (LM). 
     
    
    
     The drawings are not necessarily to scale. Like numbers refer to like parts or steps throughout the drawings. 
     DETAILED DESCRIPTIONS OF SOME EMBODIMENTS 
     Described herein are various embodiments of systems, devices, components and methods for diagnosing and treating cardiac rhythm disorders in a patient&#39;s heart using electrophysiological mapping or electrographic flow (EGF) techniques, as well as imaging, navigation, cardiac ablation and other types of medical systems, devices, components, and methods. Various embodiments described and disclosed herein also relate to systems, devices, components and methods for discovering with enhanced precision the location(s) of the source(s) of different types of cardiac rhythm disorders and irregularities. Such cardiac rhythm disorders and irregularities, include, but are not limited to, arrhythmias, atrial fibrillation (AF or A-fib), atrial tachycardia, atrial flutter, paroxysmal fibrillation, paroxysmal flutter, persistent fibrillation, ventricular fibrillation (V-fib), ventricular tachycardia, atrial tachycardia (A-tach), ventricular tachycardia (V-tach), supraventricular tachycardia (SVT), paroxysmal supraventricular tachycardia (PSVT), Wolff-Parkinson-White syndrome, bradycardia, sinus bradycardia, ectopic atrial bradycardia, junctional bradycardia, heart blocks, atrioventricular block, idioventricular rhythm, areas of fibrosis, breakthrough points, focus points, re-entry points, premature atrial contractions (PACs), premature ventricular contractions (PVCs), and other types of cardiac rhythm disorders and irregularities. 
     Various embodiments of EGF techniques, methods, systems, devices, and components are described and disclosed herein, which involve the acquisition of intra-cardiac and/or body surface electrograms, and the subsequent processing and analysis of such electrograms to reveal the locations of sources of cardiac rhythm disorders in a patient&#39;s heart, such as rotors and sources that cause or contribute to AF. That is, many of the various techniques, methods, systems, devices, and components described and disclosed herein may be referred to collectively as pertaining to “EGF.” 
     Systems and methods configured to detect in a patient&#39;s heart a location of a source of at least one cardiac rhythm disorder are disclosed herein. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments or aspects. It will be evident, however, to one skilled in the art that an example embodiment may be practiced without necessarily using all of the disclosed specific details. 
     Referring now to  FIG. 1( a ) , there is illustrated one embodiment of a combined cardiac electrophysiological mapping (EP), pacing and ablation system  100 . Note that in some embodiments system  100  may not include ablation module  150  and/or pacing module  160 . Among other things, the embodiment of system  100  shown in  FIG. 1( a )  is configured to detect and reconstruct cardiac activation information acquired from a patient&#39;s heart relating to cardiac rhythm disorders and/or irregularities, and is further configured to detect and discover the location of the source of such cardiac rhythm disorders and/or irregularities with enhanced precision relative to prior art techniques. In some embodiments, system  100  is further configured to treat the location of the source of the cardiac rhythm disorder or irregularity, for example by ablating the patient&#39;s heart at the detected location. 
     The embodiment of system  100  shown in  FIG. 1( a )  comprises five main functional units: electrophysiological mapping (EP mapping unit)  140  (which is also referred to herein as data acquisition device  140 ), ablation module  150 , pacing module  160 , imaging and/or navigation system  70 , and computer or computing device  300 . Data acquisition, processing and control system  15  comprises data acquisition device  140 , ablation module  150 , pacing module  160 , control interface  170  and computer or computing device  300 . In one embodiment, at least one computer or computing device or system  300  is employed to control the operation of one or more of systems, modules and devices  140 ,  150 ,  160 ,  170  and  70 . Alternatively, the respective operations of systems, modules or devices  140 ,  150 ,  160 ,  170  and  70  may be controlled separately by each of such systems, modules and devices, or by some combination of such systems, modules and devices. 
     Instead of being operably connected (e.g., through Bluetooth signals, a LAN or WAN network, or through the cloud), or directly connected, to computing device  300 , data acquisition device  140  may be configured to provide as outputs therefrom saved or stored body surface electrogram signals, which can be, by way of example, saved or stored on a hard drive, in a memory, on a USB stick, or other suitable storage device, and where the saved or stored body surface electrogram signals are later or subsequently provided as inputs to computing device  300  for processing and analysis. 
     Computer or computing device  300  may be configured to receive operator inputs from an input device  320  such as a keyboard, mouse and/or control panel. Outputs from computer  300  may be displayed on display or monitor  324  or other output devices (not shown in  FIG. 1( a ) ). Computer  300  may also be operably connected to a remote computer or analytic database or server  328 . At least each of components, devices, modules and systems  60 ,  110 ,  140 ,  146 ,  148 ,  150 ,  170 ,  300 ,  324  and  328  may be operably connected to other components or devices by wireless (e.g., Bluetooth) or wired means. Data may be transferred between components, devices, modules or systems through hardwiring, by wireless means, or by using portable memory devices such as USB memory sticks. 
     During electrophysiological (EP) mapping procedures, multi-electrode catheter  110  is typically introduced percutaneously into the patient&#39;s heart  10 . Catheter  110  is passed through a blood vessel (not shown), such as a femoral vein or the aorta, and thence into an endocardial site such as the atrium or ventricle of the heart  10 . 
     It is contemplated that other catheters, including other types of mapping or EP catheters, lasso catheters, pulmonary vein isolation (PVI) ablation catheters (which can operate in conjunction with sensing lasso catheters), ablation catheters, navigation catheters, and other types of EP mapping catheters such as EP monitoring catheters and spiral catheters may also be introduced into the heart, and that additional surface electrodes may be attached to the skin of the patient to record electrocardiograms (ECGs). 
     When system  100  is operating in an EP mapping mode, multi-electrode catheter  110  functions as a detector of intra-electrocardiac signals, while optional surface electrodes may serve as detectors of surface ECGs. In one embodiment, the analog signals obtained from the intracardiac and/or surface electrodes are routed by multiplexer  146  to data acquisition device  140 , which comprises an amplifier  142  and an A/D converter (ADC)  144 . The amplified or conditioned electrogram signals may be displayed by electrocardiogram (ECG) monitor  148 . The analog signals are also digitized via ADC  144  and input into computer  300  for data processing, analysis and graphical display. 
     In one embodiment, catheter  110  is configured to detect cardiac activation information in the patient&#39;s heart  10 , and to transmit the detected cardiac activation information to data acquisition device  140 , either via a wireless or wired connection. In one embodiment that is not intended to be limiting with respect to the number, arrangement, configuration, or types of electrodes, catheter  110  includes a plurality of 64 electrodes, probes and/or sensors A 1  through H 8  arranged in an 8×8 grid that are included in electrode mapping assembly  120 , which is configured for insertion into the patient&#39;s heart through the patient&#39;s blood vessels and/or veins. Other numbers, arrangements, configurations and types of electrodes in catheter  110  are, however, also contemplated. In most of the various embodiments, at least some electrodes, probes and/or sensors included in catheter  110  are configured to detect cardiac activation or electrical signals, and to generate electrocardiograms or electrogram signals, which are then relayed by electrical conductors from or near the distal end  112  of catheter  110  to proximal end  116  of catheter  110  to data acquisition device  140 . 
     Note that in some embodiments of system  100 , multiplexer  146  is not employed for various reasons, such as sufficient electrical conductors being provided in catheter  110  for all electrode channels, or other hardware design considerations. In other embodiments, multiplexer  146  is incorporated into catheter  110  or into data acquisition device  140 . In still further embodiments, multiplexer  146  is optional or not provided at all, and data acquisition device  140 , ablation module  150 , and/or pacing module  160  are employed separately and/or operate independently from one another. In addition, in some embodiments computing device  300  may be combined or integrated with one or more of data acquisition device  140 , ablation module  150 , and/or pacing module  160 . 
     In one embodiment, a medical practitioner or health care professional employs catheter  110  as a roving catheter to locate the site of the location of the source of a cardiac rhythm disorder or irregularity in the endocardium quickly and accurately, without the need for open-chest and open-heart surgery. In one embodiment, this is accomplished by using multi-electrode catheter  110  in combination with real-time or near-real-time data processing and interactive display by computer  300 , and optionally in combination with imaging and/or navigation system  70 . In one embodiment, multi-electrode catheter  110  deploys at least a two-dimensional array of electrodes against a site of the endocardium at a location that is to be mapped, such as through the use of a Biosense Webster® PENTARAY® EP mapping catheter. The intracardiac or electrogram signals detected by the catheter&#39;s electrodes provide data sampling of the electrical activity in the local site spanned by the array of electrodes. 
     In one embodiment, the electrogram signal data are processed by computer  300  to produce a display showing the locations(s) of the source(s) of cardiac rhythm disorders and/or irregularities in the patient&#39;s heart  10  in real-time or near-real-time, further details of which are provided below. That is, at and between the sampled locations of the patient&#39;s endocardium, computer  300  may be configured to compute and display in real-time or near-real-time an estimated, detected and/or determined location(s) of the site(s), source(s) or origin)s) of the cardiac rhythm disorder(s) and/or irregularity(s) within the patient&#39;s heart  10 . This permits a medical practitioner to move interactively and quickly the electrodes of catheter  110  towards the location of the source of the cardiac rhythm disorder or irregularity. 
     In some embodiments of system  100 , one or more electrodes, sensors or probes detect cardiac activation from the surface of the patient&#39;s body as surface ECGs, or remotely without contacting the patient&#39;s body (e.g., using magnetocardiograms). In another example, some electrodes, sensors or probes may derive cardiac activation information from echocardiograms. In various embodiments of system  100 , external or surface electrodes, sensors and/or probes can be used separately or in different combinations, and further may also be used in combination with intracardiac electrodes, sensors and/or probes inserted within the patient&#39;s heart  10 . Many different permutations and combinations of the various components of system  100  are contemplated having, for example, reduced, additional or different numbers of electrical sensing and other types of electrodes, sensors and/or transducers. 
     Continuing to refer to  FIG. 1( a ) , EP mapping system or data acquisition device  140  is configured to condition the analog electrogram signals delivered by catheter  110  from electrodes A 1  through H 8  in amplifier  142 . Conditioning of the analog electrogram signals received by amplifier  142  may include, but is not limited to, low-pass filtering, high-pass filtering, bandpass filtering, and notch filtering. The conditioned analog signals are then digitized in analog-to-digital converter (ADC)  144 . ADC  144  may further include a digital signal processor (DSP) or other type of processor which is configure to further process the digitized electrogram signals (e.g., low-pass filter, high-pass filter, bandpass filter, notch filter, automatic gain control, amplitude adjustment or normalization, artifact removal, etc.) before they are transferred to computer or computing device  300  for further processing and analysis. 
     As discussed above, in some embodiments, multiplexer  146  is separate from catheter  110  and data acquisition device  140 , and in other embodiments multiplexer  146  is combined in catheter  110  or data acquisition device  140 . 
     In some embodiments, the rate at which individual electrogram and/or ECG signals are sampled and acquired by system  100  can range between about 0.25 milliseconds and about 8 milliseconds, and may be about 0.5 milliseconds, about 1 millisecond, about 2 milliseconds or about 4 milliseconds. Other sample rates are also contemplated. While in some embodiments system  100  is configured to provide unipolar signals, in other embodiments system  100  is configured to provide bipolar signals. 
     In one embodiment, system  100  can include a BARD® LABSYSTEM™ PRO EP Recording System, which is a computer and software driven data acquisition and analysis tool designed to facilitate the gathering, display, analysis, pacing, mapping, and storage of intracardiac EP data. Also in one embodiment, data acquisition device  140  can include a BARD® CLEARSIGN™ amplifier, which is configured to amplify and condition electrocardiographic signals of biologic origin and pressure transducer input, and transmit such information to a host computer (e.g., computer  300  or another computer). 
     As shown in  FIG. 1( a ) , and as described above, in some embodiments system  100  includes ablation module  150 , which may be configured to deliver RF ablation energy through catheter  110  and corresponding ablation electrodes disposed near distal end  112  thereof, and/or to deliver RF ablation energy through a different catheter (not shown in  FIG. 1( a ) ). Suitable ablation systems and devices include, but are not limited to, cryogenic ablation devices and/or systems, radiofrequency ablation devices and/or systems, ultrasound ablation devices and/or systems, high-intensity focused ultrasound (HIFU) devices and/or systems, chemical ablation devices and/or systems, and laser ablation devices and/or systems. 
     When system  100  is operating in an optional ablation mode, multi-electrode catheter  110  fitted with ablation electrodes, or a separate ablation catheter, is energized by ablation module  150  under the control of computer  300 , control interface  170 , and/or another control device or module. For example, an operator may issue a command to ablation module  150  through input device  320  to computer  300 . In one embodiment, computer  300  or another device controls ablation module  150  through control interface  170 . Control of ablation module  150  can initiate the delivery of a programmed series of electrical energy pulses to the endocardium via catheter  110  (or a separate ablation catheter, not shown in  FIG. 1( a ) ). One embodiment of an ablation method and device is disclosed in U.S. Pat. No. 5,383,917 to Desai et al., the entirety of which is hereby incorporated by reference herein. 
     In an alternative embodiment, ablation module  150  is not controlled by computer  300 , and is operated manually directly under operator control. Similarly, pacing module  160  may also be operated manually directly under operator control. The connections of the various components of system  100  to catheter  110 , to auxiliary catheters, or to surface electrodes may also be switched manually or using multiplexer  146  or another device or module. 
     When system  100  is operating in an optional pacing mode, multi-electrode catheter  110  is energized by pacing module  160  operating under the control of computer  300  or another control device or module. For example, an operator may issue a command through input device  320  such that computer  300  controls pacing module  160  through control interface  170 , and multiplexer  146  initiates the delivery of a programmed series of electrical simulating pulses to the endocardium via the catheter  110  or another auxiliary catheter (not shown in  FIG. 1( a ) ). One embodiment of a pacing module is disclosed in M. E. Josephson et al., in “VENTRICULAR ENDOCARDIAL PACING II, The Role of Pace Mapping to Localize Origin of Ventricular Tachycardia,” The American Journal of Cardiology, vol. 50, November 1982. 
     Computing device or computer  300  is appropriately configured and programmed to receive or access the electrogram signals provided by data acquisition device  140 . Computer  300  is further configured to analyze or process such electrogram signals in accordance with the methods, functions and logic disclosed and described herein so as to permit reconstruction of cardiac activation information from the electrogram signals. This, in turn, makes it possible to locate with at least some reasonable degree of precision the location of the source of a heart rhythm disorder or irregularity. Once such a location has been discovered, the source may be eliminated or treated by means that include, but are not limited to, cardiac ablation. 
     In one embodiment, and as shown in  FIG. 1( a ) , system  100  also comprises a physical imaging and/or navigation system  70 . Physical imaging and/or navigation device  60  included in system  70  may be, by way of example, a 2- or 3-axis fluoroscope system, an ultrasonic system, a magnetic resonance imaging (MRI) system, a computed tomography (CT) imaging system, and/or an electrical impedance tomography EIT) system. Operation of system  70  be controlled by computer  300  via control interface  170 , or by other control means incorporated into or operably connected to imaging or navigation system  70 . In one embodiment, computer  300  or another computer triggers physical imaging or navigation system  60  to take “snap-shot” pictures of the heart  10  of a patient (body not shown). A picture image is detected by a detector  62  along each axis of imaging, and can include a silhouette of the heart as well as a display of the inserted catheter  110  and its electrodes A 1 -H 8  (more about which is said below), which is displayed on imaging or navigation display  64 . Digitized image or navigation data may be provided to computer  300  for processing and integration into computer graphics that are subsequently displayed on monitor or display  64  and/or  324 . 
     In one embodiment, system  100  further comprises or operates in conjunction with catheter or electrode position transmitting and/or receiving coils or antennas located at or near the distal end of an EP mapping catheter  110 , or that of an ablation or navigation catheter  110 , which are configured to transmit electromagnetic signals for intra-body navigational and positional purposes. 
     In one embodiment, imaging or navigation system  70  is used to help identify and determine the precise two- or three-dimensional positions of the various electrodes included in catheter  110  within patient&#39;s heart  10 , and is configured to provide electrode position data to computer  300 . Electrodes, position markers, and/or radio-opaque markers can be located on various portions of catheter  110 , mapping electrode assembly  120  and/or distal end  112 , or can be configured to act as fiducial markers for imaging or navigation system  70 . 
     Medical navigation systems suitable for use in the various embodiments described and disclosed herein include, but are not limited to, image-based navigation systems, model-based navigation systems, optical navigation systems, electromagnetic navigation systems (e.g., BIOSENSE® WEBSTER® CARTO® system), and impedance-based navigation systems (e.g., the St. Jude® ENSITE™ VELOCITY™ cardiac mapping system), and systems that combine attributes from different types of imaging AND navigation systems and devices to provide navigation within the human body (e.g., the MEDTRONIC® STEALTHSTATION® system). 
     In view of the structural and functional descriptions provided herein, those skilled in the art will appreciate that portions of the described devices and methods may be configured as processes, methods, data processing systems, and/or computer methods. Accordingly, these portions of the devices and methods described herein may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware, such as shown and described with respect to computer system  300  illustrated in  FIG. 1( b ) . Furthermore, portions of the devices and methods described herein may be a process or method stored in a computer-usable storage medium having computer readable program code on the medium. Any suitable computer-readable medium may be utilized including, but not limited to, static and dynamic storage devices, hard disks, optical storage devices, and magnetic storage devices. 
     Certain embodiments of portions of the devices and methods described herein are also described with reference to block diagrams of methods, processes, and systems. It will be understood that such block diagrams, and combinations of blocks diagrams in the Figures, can be implemented using computer-executable instructions. These computer-executable instructions may be provided to one or more processors of a general purpose computer, a special purpose computer, or any other suitable programmable data processing apparatus (or a combination of devices and circuits) to produce a machine, such that the instructions, which executed via the processor(s), implement the functions specified in the block or blocks of the block diagrams. 
     These computer-executable instructions may also be stored in a computer-readable memory that can direct computer  300  or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture including instructions which implement the function specified in an individual block, plurality of blocks, or block diagram. The computer program instructions may also be loaded onto computer  300  or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on computer  300  or other programmable apparatus provide steps for implementing the functions specified in the an individual block, plurality of blocks, or block diagram. 
     In this regard,  FIG. 1( b )  illustrates only one example of a computer system  300  (which, by way of example, can include multiple computers or computer workstations) that can be employed to execute one or more embodiments of the devices and methods described and disclosed herein, such as devices and methods configured to acquire and process sensor or electrode data, to process image data, and/or transform sensor or electrode data and image data associated with the analysis of cardiac electrical activity and the carrying out of the combined electrophysiological mapping and analysis of the patient&#39;s heart  10  and ablation therapy delivered thereto. 
     Computer system  300  can be implemented on one or more general purpose computer systems or networked computer systems, embedded computer systems, routers, switches, server devices, client devices, various intermediate devices/nodes or standalone computer systems. Additionally, computer system  300  or portions thereof may be implemented on various mobile devices such as, for example, a personal digital assistant (PDA), a laptop computer and the like, provided the mobile device includes sufficient processing capabilities to perform the required functionality. 
     In one embodiment, computer system  300  includes processing unit  301  (which may comprise a CPU, controller, microcontroller, processor, microprocessor or any other suitable processing device), system memory  302 , and system bus  303  that operably connects various system components, including the system memory, to processing unit  301 . Multiple processors and other multi-processor architectures also can be used to form processing unit  301 . System bus  303  can comprise any of several types of suitable bus architectures, including a memory bus or memory controller, a peripheral bus, or a local bus. System memory  302  can include read only memory (ROM)  304  and random access memory (RAM)  305 . A basic input/output system (BIOS)  306  can be stored in ROM  304  and contain basic routines configured to transfer information and/or data among the various elements within computer system  300 . 
     Computer system  300  can include a hard disk drive  303 , a magnetic disk drive  308  (e.g., to read from or write to removable disk  309 ), or an optical disk drive  310  (e.g., for reading CD-ROM disk  311  or to read from or write to other optical media). Hard disk drive  303 , magnetic disk drive  308 , and optical disk drive  310  are connected to system bus  303  by a hard disk drive interface  312 , a magnetic disk drive interface  313 , and an optical drive interface  314 , respectively. The drives and their associated computer-readable media are configured to provide nonvolatile storage of data, data structures, and computer-executable instructions for computer system  300 . Although the description of computer-readable media above refers to a hard disk, a removable magnetic disk and a CD, other types of media that are readable by a computer, such as magnetic cassettes, flash memory cards, digital video disks and the like, in a variety of forms, may also be used in the operating environment; further, any such media may contain computer-executable instructions for implementing one or more parts of the devices and methods described and disclosed herein. 
     A number of program modules may be stored in drives and RAM  303 , including operating system  315 , one or more application programs  316 , other program modules  313 , and program data  318 . The application programs and program data can include functions and methods programmed to acquire, process and display electrical data from one or more sensors, such as shown and described herein. The application programs and program data can include functions and methods programmed and configured to process data acquired from a patient for assessing heart function and/or for determining parameters for delivering a therapy and/or assessing heart function, such as shown and described herein with respect to  FIGS. 1-10 ( f ). 
     A health care provider or other user may enter commands and information into computer system  300  through one or more input devices  320 , such as a pointing device (e.g., a mouse, a touch screen, etc.), a keyboard, a microphone, a joystick, a game pad, a scanner, and the like. For example, the user can employ input device  320  to edit or modify the data being input into a data processing method (e.g., only data corresponding to certain time intervals). These and other input devices  320  may be connected to processing unit  301  through a corresponding input device interface or port  322  that is operably coupled to the system bus, but may be connected by other interfaces or ports, such as a parallel port, a serial port, or a universal serial bus (USB). One or more output devices  324  (e.g., display, a monitor, a printer, a projector, or other type of display device) may also be operably connected to system bus  303  via interface  326 , such as through a video adapter. 
     Computer system  300  may operate in a networked environment employing logical connections to one or more remote computers, such as remote computer  328 . Remote computer  328  may be a workstation, a computer system, a router, or a network node, and may include connections to many or all the elements described relative to computer system  300 . The logical connections, schematically indicated at  330 , can include a local area network (LAN) and/or a wide area network (WAN). 
     When used in a LAN networking environment, computer system  300  can be connected to a local network through a network interface or adapter  332 . When used in a WAN networking environment, computer system  300  may include a modem, or may be connected to a communications server on the LAN. The modem, which may be internal or external, can be connected to system bus  303  via an appropriate port interface. In a networked environment, application programs  316  or program data  318  depicted relative to computer system  300 , or portions thereof, may be stored in a remote memory storage device  340 . 
     Referring now to  FIG. 2 , there is shown an illustrative view of one embodiment of a distal portion of catheter  110  inside a patient&#39;s left atrium  14 . As shown in  FIG. 2 , heart  10  includes right atrium  12 , left atrium  14 , right ventricle  18 , and left ventricle  20 . Mapping electrode assembly  120  is shown in an expanded or open state inside left atrium  13  after it has been inserted through the patient&#39;s inferior vena cava and foramen ovalen (“IVC” and “FO” in  FIG. 2 ), and in one embodiment is configured to obtain electrogram signals from left atrium  12  via an 8×8 array of electrodes A 1  through H 8 , which as shown comprises individual electrodes  82 . Mapping electrode assembly and catheter  110  may also be positioned within the patient&#39;s right atrium  12 , left ventricle  18  and right ventricle  20 . 
       FIG. 3  shows an illustrative embodiment of a mapping electrode assembly  120 , which in  FIG. 3  forms a distal portion of a Boston Scientific® CONSTELLATION® full contact mapping catheter. The CONSTELLATION EP catheter permits full-contact mapping of a patient&#39;s heart chamber, and may also be employed to facilitate the assessment of entrainment, conduction velocity studies, and refractory period in a patient&#39;s heart  10 . Mapping electrode assembly  120  shown in  FIG. 3  permits the simultaneous acquisition of longitudinal and circumferential signals for more accurate 3-D mapping, and features a flexible basket design that conforms to atrial anatomy and aids aid in accurate placement. Sixty-four electrodes A 1  through H 8  (or individual electrodes  82 ) can provide comprehensive, real-time 3-D information over a single heartbeat. 
       FIG. 4  shows one embodiment of a method  200  of detecting a location of a source of at least one cardiac rhythm disorder in a patient&#39;s heart. At step  210 , the amplitudes of electrogram signals acquired from electrodes located inside a patient&#39;s heart are normalized or adjusted. At step  230 , positions A 1  through H 8  corresponding to each of the electrodes of mapping electrode assembly  120  are assigned to the individual electrogram signals that have been acquired. At step  230 , a two-dimensional (2D) spatial map of electrode positions A 1  through H 8  is generated or provided. In some embodiments, a three-dimensional (3D) spatial map of electrode positions A 1  through H 8  is generated or provided. (As discussed above, fewer or more than 64 electrodes may be used to measure electrogram signals and/or surface ECGs, and electrode arrays other than 8×8 or rectangular grids are contemplated in the various embodiments.) 
     For discrete or selected times over which the electrogram signals are being analyzed and processed, at step  240  the amplitude-adjusted electrogram signals are processed to generate a plurality of three-dimensional electrogram surfaces (which according to one embodiment may be smoothed electrogram surfaces) corresponding at least partially to the 2D (or 3D) map, one surface being generated for each such discrete time. At step  250 , the plurality of three-dimensional electrogram surfaces that have been generated through time are processed to generate a velocity vector map corresponding at least partially to the 2D (or 3D) map, which can also be called a three-dimensional electrographic flow or EGF map. The velocity vector map or EGF map is configured to reveal the location of the source of the at least one cardiac rhythm disorder. In a subsequent optional step (not shown in  FIG. 4 ), method  200  further comprises ablating patient&#39;s heart  10  at the location of the source of the cardiac rhythm disorder indicated by the velocity vector map. 
     Method  200  outlined in  FIG. 4  presents one embodiment of a method of processing electrogram signals provided by one or more mapping catheters so as to transform time domain waveform information into space domain information, and then calculate velocity vector maps that correspond to normalized space potential profile movements for each point in space. For reasons that are explained below, method  200  has the advantages that it is robust against artifacts and provides a virtual resolution that is higher than the actual electrode density employed to acquire the EP mapping data through the use of a fitting method that determines the most likely mean spatial velocity map derived from hundreds of individual samples of amplitude patterns recorded by the mapping electrodes. 
     As described above, in step  210  of  FIG. 4  the amplitudes of electrogram signals acquired from electrodes located inside the patient&#39;s heart are normalized or otherwise adjusted. In step  240 , the amplitude-adjusted electrogram signals are processed across a 2D or 3D map to generate a plurality of three-dimensional electrogram surfaces, one surface being generated for each such discrete time. In one embodiment, the resulting individual time-slice surfaces can be strung together sequentially to provide a time-varying depiction of electrical activation occurring over the portion of the patient&#39;s heart that has been monitored. According to embodiments that have been discovered to be particularly efficacious in the field of intracardiac EP monitoring and data processing and analysis, at least portions of the electrogram surfaces are found to correspond to estimated wave shapes, and are generated using Green&#39;s function, which in some embodiments, and by way of non-limiting example, may be combined with two- or three-dimensional bi-harmonic spline interpolation functions to generate such surfaces. 
     In one embodiment, electrogram signal data acquired from the patient&#39;s heart  10  are not equidistantly sampled. For example, in one such embodiment, electrogram signal data acquired from the patient&#39;s heart  10  are not equidistantly sampled by mapping electrode assembly  120 , and instead are assigned their respective chessboard locations A 1  through H 8  as approximations of electrode locations in a cylindrical 2D projection of a grid representative of the interior surface of the patient&#39;s heart that is being mapped. In many applications, it has been discovered that such approximations of electrode locations yield perfectly useable and accurate results when steps  230  through  250  are carried out after steps  210  and  230 . 
     In another embodiment, when superimposing the acquired electrogram signal data onto a 2D or 3D map or grid in step  230 , the electrogram signal data may be associated with their actual or more accurately estimated positions in the 2D projection of the grid using positional data provided by, for example, imaging or navigation system  70 . Resampling of electrogram signals on the grid may also be carried out. Gridding may also be carried out such as by convolution-type filtering, Kriging, and using splines. Most gridding techniques operate on an equidistant grid and solve the equations governing the gridding process with either finite difference or finite element implementations. 
     One approach that has been discovered to work particularly well with electrogram signal data is to determine the Green&#39;s function associated with each electrogram value assigned to a given chessboard location, and then construct the solution as a sum of contributions from each data point, weighted by the Green&#39;s function evaluated for each point of separation. Biharmonic spline interpolation, which as described above may be employed in conjunction with Green&#39;s function, has also been discovered to work especially well in the context of processing and analyzing electrogram signal data. In some embodiments, undesirable oscillations between data points are removed by interpolation with splines in tension, also using Green&#39;s function. A Green&#39;s function technique for interpolation and surface fitting and generation of electrogram signal data has been found to be superior to conventional finite-difference methods because, among other things, the model can be evaluated at arbitrary x,y locations rather than only on a rectangular grid. This is a very important advantage of using Green&#39;s function in step  240 , because precise evenly-spaced-apart grid locations, resampling of electrogram signals, and finite-difference gridding calculations are not required to generate accurate representations of electrogram surfaces in step  240 . 
     In one embodiment, Green&#39;s function G(x; x′) is employed in step  240  for a chosen spline and geometry to interpolate data at regular or arbitrary output locations. Mathematically, the solution is w(x)=sum {c(i) G(x′; x(i))}, for i=1, n, and a number of data points {x(i), w(i)}. Once the n coefficients c(i) have been calculated, the sum may be evaluated at any output point x. A selection is made between minimum curvature, regularized, or continuous curvature splines in tension for either 1-D, 2-D, or 3-D Cartesian coordinates or spherical surface coordinates. After removing a linear or planar trend (i.e., in Cartesian geometries) or mean values (i.e., spherical surfaces) and normalizing residuals, a least-squares matrix solution for spline coefficients c(i) may be determined by solving the n by n linear system w(j)=sum-over-i {c(i) G(x(j); x(i))}, for j=1, n; this solution yields an exact interpolation of the supplied data points. For further details regarding the methods and mathematics underlying Green&#39;s function, see, for example: (1) “Moving Surface Spline Interpolation Based on Green&#39;s Function,” Xingsheng Deng and Zhong-an Tang, Math. Geosci (2011), 43:663-680 (“the Deng paper”), and (2) “Interpolation with Splines in Tension: A Green&#39;s Function Approach,” Paul Wessel and David Bercovici, Mathematical Geology, 77-93, Vol. 30, No. 1, 1998 (“the Wessel paper”). The respective entireties of the Deng and Wessel papers are hereby incorporated by reference herein. 
     Still further details regarding the use of Green&#39;s function in interpolating and generating surfaces may be found in: Interpolation by regularized spline with tension: I. Theory and implementation, Mitasova, H., and L. Mitas, 1993, Math. Geol., 25, 641-655; Parker, R. L., 1994, Geophysical Inverse Theory, 386 pp., Princeton Univ. Press, Princeton, N.J.; Sandwell, D. T., 1987, Biharmonic spline interpolation of Geos-3 and Seasat altimeter data, Geophys. Res. Lett., 14, 139-142; Wessel, P., and J. M. Becker, 2008, Interpolation using a generalized Green&#39;s function for a spherical surface spline in tension, Geophys. J. Int, 174, 21-28, and Wessel, P., 2009, A general-purpose Green&#39;s function interpolator, Computers &amp; Geosciences, 35, 1247-1254. Moving Surface Spline Interpolation Based on Green&#39;s Function, Xingsheng Deng, Zhong-an Tang, Mathematical Geosciences, August 2011, Volume 43, Issue 6, pp 663-680. 
     Note, however, that a number of different surface smoothing, surface fitting, surface estimation and/or surface/data interpolation processing techniques may be employed in step  240  of  FIG. 4 , which are not limited to Green&#39;s function, or use in conjunction with Green&#39;s function, and which include, but are not limited to, inverse distance weighted methods of interpolation, triangulation with linear interpolation, bilinear surface interpolation methods, bivariate surface interpolation methods, cubic convolution interpolation methods, Kriging interpolation methods, Natural Neighbor or “area-stealing” interpolation methods, spline interpolation techniques (including bi-harmonic spline fitting techniques and “spline with barriers” surface interpolation methods), global polynomial interpolation methods, moving least squares interpolation methods, polynomial least square fitting interpolation methods, simple weighted-average operator interpolation methods, multi-quadric biharmonic function interpolation methods, and artificial neural network interpolation methods. See, for example: “A brief description of natural neighbor interpolation (Chapter 2),” in V. Barnett. Interpreting Multivariate Data. Chichester: John Wiley. pp. 21-36), and “Surfaces generated by Moving Least Squares Methods,” P. Lancaster et al., Mathematics of Computation, Vol. 37, No. 155 (July, 1981), 141-158). 
     As described above, in step  250  of  FIG. 4 , the plurality of three-dimensional electrogram surfaces may be processed through time to generate a velocity vector map or EGF map corresponding at least partially to the 2D (or 3D) map, the velocity vector map being configured to reveal the location of the source of the at least one cardiac rhythm disorder. According to embodiments that have been discovered to be particularly efficacious in the field of intracardiac EP monitoring and subsequent data processing and analysis, at least portions of the velocity vector map are generated using one or more optical flow analysis and estimation techniques and methods. Such optical flow analysis techniques may include one or more of Horn-Schunck, Buxton-Buston, Black-Jepson, phase correlation, block-based, discrete optimization, Lucas-Kanade, and differential methods of estimating optical flow. From among these various optical flow estimation and analysis techniques and methods, however, the Horn-Schunck method has so far been discovered to provide superior results in the context of processing and analyzing cardiac electrogram signals, for reasons that are discussed in further detail below. 
     Two papers describe the Horn-Schunck method particularly well: (1) “SimpleFlow: A Non-Iterative, Sublinear Optical Flow Algorithm,” Michael Tao et al., Eurographics 2012, Vol. 31 (2012), No. 2 (“the Tao paper”), and (2) “Horn-Schunck Optical Flow with a Multi-Scale Strategy,” Enric Meinhardt-Llopis et al., Image Processing On Line, 3 (2013), pp. 151-172 (“the Meinhardt-Llopis paper”). The respective entireties of the Tao and Meinhardt-Llopis papers are hereby incorporated by reference herein. 
     In “Determining Optical Flow,” by B. K. P. Horn and B. G. Schunck, Artificial Intelligence, Vol. 17, pp. 185-204, 1981, the entirety of which is also hereby incorporated by reference herein, a method for finding an optical flow pattern is described which assumes that the apparent velocity of a brightness pattern varies smoothly throughout most of an image. The Horn-Schunck method assumes smoothness in flow over most or all of an image. Thus, the Horn-Schunck method attempts to minimize distortions in flow and prefers solutions which exhibit smoothness. The Horn-Schunck method of estimating optical flow is a global method which introduces a global constraint of smoothness to solve the aperture problem of optical flow. 
     A description of some aspects of conventional application of the Horn-Schunck method is set forth in U.S. Pat. No. 6,480,615 to Sun et al. entitled “Motion estimation within a sequence of data frames using optical flow with adaptive gradients,” the entirety of which is also hereby incorporated by reference herein. As described by Sun et al., the Horn-Schunck computation is based on the observation that flow velocity has two components, and that a rate of change of image brightness requires only one constraint. Smoothness of flow is introduced as a second constraint to solve for optical flow. The smoothness constraint presumes there are no spatial discontinuities. As a result, Horn and Schunck excluded situations where objects in an image occlude or block one another. This is because at object boundaries of an occlusion in an image, discontinuities in reflectance appear. 
     In conventional optical flow analysis, image brightness is considered at pixel (x,y) in an image plane at time t to be represented as a function I(x,y,t). Based on initial assumptions that the intensity structures of local time-varying image regions are approximately constant under motion for at least a short duration, the brightness of a particular point in the image is constant, so that dl/dt=0. Based on the chain rule of differentiation, an optical flow constraint equation (I) can be represented as follows:
 
 lx ( x,y,t )· u+ly ( x,y,t )· v+lt ( x,y,t )=0,
 
     where 
     lx=∂l(x,y,t)/∂x=horizontal spatial gradient of the image intensity; 
     ly=∂l(x,y,t)/∂y=vertical spatial gradient of the image intensity; 
     lt=∂l(x,y,t)/∂t=temporal image gradient of the image intensity; 
     u=dx/dt=horizontal image velocity (or displacement); and 
     v=dy/dt=vertical image velocity (or displacement). 
     The above optical flow equation is a linear equation having two unknowns, (i.e., u and v). The component of motion in the direction of the brightness gradient is known to be lt/(lx2+ly 2)½. However, one cannot determine the component of movement in the direction of the iso-brightness contours at right angles to the brightness gradient. As a consequence, the optical flow velocity (u,v) cannot be computed locally without introducing additional constraints. Horn and Schunck therefore introduce a smoothness constraint. They argue that if every point of the brightness pattern can move independently, then there is little hope of recovering the velocities. However, if opaque objects of finite size are undergoing rigid motion or deformation, neighboring points on the objects should have similar velocities. Correspondingly, the velocity field of the brightness patterns in the image will vary smoothly almost everywhere. 
     Advantages of the Horn-Schunck method include that it yields a high density of flow vectors, i.e., the flow information missing in inner parts of homogeneous objects is filled in from the motion boundaries. On the negative side, the Horn-Schunck method can be sensitive to noise. 
     The foregoing discussion regarding how the Horn-Schunck optical flow technique typically focuses on conventional applications, where the brightness or intensity of an object changes over time (which is where the term “optical flow” is derived from). Here, the brightness or intensity of an object is not the issue at hand. Instead, the amplitudes of electrogram signals, and how they change shape and propagate in time and space over a patient&#39;s heart, are sought to be determined. One underlying objective of method  200  is to produce a vector velocity map, which is a representation of electrographical flow (and not optical flow) within a patient&#39;s heart. Instead of looking for differences or changes in optical brightness or intensity, changes in the velocity, direction and shape of electrical signals (i.e., changes in electrographical flow) across a patient&#39;s heart are determined. That is, method  200  does not process optical measurement data corresponding to intensity or brightness, but processes electrical measurement data corresponding to amplitude, potential shape, and/or voltage. 
     One reason why method  200  works so well in detecting the locations of the sources of cardiac rhythm disorders and irregularities is that ion channels in a patient&#39;s heart produce action potential voltages that are relatively constant (except in areas of fibrosis). As described above, the Horn-Schunck method assumes “brightness constancy” as one of its key constraints. The normalized/amplitude-adjusted electrogram signals provided by step  210  help satisfy this key constraint of the Horn-Schunck method so that this method may be applied successfully in step  250 . 
     In addition, because of the stability imparted to electrographical flow solutions determined using the Horn-Schunck method, artifacts and noise are generally low in velocity vector maps generated in step  250 . In fact, it is believed that the Horn-Schunck method may generally be applied with greater success to electrographical flow data than to optical data because of the unique nature of action potential signals in the human heart, and the manner in which electrogram signals are processed and conditioned before an optical flow analysis is performed on them as described and disclosed herein. 
     Method  200  described and disclosed herein also does not employ spatial derivatives of electrical potentials (as is done by Deno et al. and Kumaraswamy Nanthakumar using “omnipolar” signals) or time derivatives of electrogram signals (as is done in the TOPERA system). Time derivatives of signals are known to increase noise. Method  200  has as its key inputs the potentials of electrogram signals (not their derivatives). As a result, method  200  is notably free from the effects of spurious noise and artifacts introduced by time-derivative data processing techniques, including in step  250 . 
     In another embodiment, the velocity vector map of step  250  is generated using the Lucas-Kanade optical flow method, which is a differential method for optical flow estimation developed by Bruce D. Lucas and Takeo Kanade. It assumes that the flow is essentially constant in a local neighbourhood of a pixel under consideration, and solves the basic optical flow equations for all the pixels in that neighborhood using least squares criteria. By combining information from several nearby pixels, the Lucas-Kanade method can often resolve the inherent ambiguity of the optical flow equation. It is also less sensitive to image noise than point-wise methods. On the other hand, since it is a purely local method, it cannot provide flow information in the interior of uniform regions of the image. See “An Iterative Image Registration Technique with an Application to Stereo Vision,” Bruce D. Lucase, Takeo Kanade, Proceedings of Imaging Understanding Workshop, pp. 121-130 (1981), the entirety of which is hereby incorporated by reference herein. 
     In yet another embodiment, various aspects of the Horn-Schunck and Lucas-Kanade methods are combined to yield an optical flow method that exhibits the local methods inherent in Lucas-Kanade techniques and the global methods inherent in the Horn-Schunck approach and its extensions. Often local methods are more robust under noise, while global techniques yield dense flow fields. See, for example, “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods,” Andrés Bruhn, Joachim Weickert, Christoph Schnörr, International Journal of Computer Vision, February 2005, Volume 61, Issue 3, pp 211-231, the entirety of which is hereby incorporated by reference herein. 
     Various embodiments of method  200  feature several advantages with respect to prior art systems and methods that generate intracardiac images and attempt to detect the locations of cardiac rhythm disorders or irregularities. A key underlying assumption of signal processing techniques that employ Hilbert Transform, Discrete Fourier Transforms (DFTs) or Fast Fourier Transforms (FFTs) is that the signal to be transformed is periodic. As is well known in the field of digital signal processing, this underlying basic assumption is frequently incorrect, and can lead to problems such as spectral leakage. Contrariwise, in some embodiments of method  200 , an underlying assumption is that the electrical activity in a patient&#39;s heart is based upon ion channel activation, which is a stochastic and non-periodic process, and so strictly periodic behaviour is not assumed or required in subsequent data processing and manipulation steps. 
     Indeed, none of steps  210 ,  230 ,  240 , or  250  of method  200  absolutely requires the use of Hilbert or Fourier transforms to process data. Instead, in some embodiments each of these steps can be carried out in the time domain without the need for frequency domain or quadrature conversion. For example, in step  210  the amplitudes of the various traces or electrograms can be normalized or adjusted in the time domain according to a selected standard deviation. In another example, rotors detected by method  200  are not assumed to be singularities in a phase map (as is assumed in techniques based upon frequency domain or Hilbert transform signal processing). This key difference also explains why the rotational direction of a rotor can be revealed or detected accurately by method  200  (and not at all, or very unsatisfactorily, using the frequency domain or Hilbert transforms of other methods employed to detect rotors). Note that in some embodiments, however, Hilbert, DFT and/or FFT signal processing components may be or are included in the data processing flow of method  200  (e.g., DSP filtering, deconvolution, etc.). 
     Referring now to  FIG. 5( a ) , there is shown a simple rotor model. This model was used to generate simulated ECG signals sensed by an 8×8 array of virtual electrodes. The simple rotor model shown in  FIG. 5( a )  is from “Chaste: An Open Source C++ Library for Computational Physiology and Biology, “Gary R. Mirams, et al. PLOS Computational Biology, Mar. 14, 2013, Vol. 9, Issue 3, e1002970, the entirety of which is hereby incorporated by reference herein. 
       FIG. 5( b )  shows artifacts in electrogram signals derived from actual patient data, where 400 msec. traces were recorded using a 64-electrode basket catheter located in the left atrium of a patient suffering from atrial fibrillation. As shown in  FIG. 5( b ) , the sensed artifacts in the electrogram signals include DC offsets of several millivolts that shift with time, a common far-field ventricular depolarization superimposed on the local potentials sensed by individual electrodes, and noise. Moreover, the amplitudes of the various sensed electrogram signals shown in  FIG. 5( b )  will be seen to vary considerably. These amplitude variations result at least in part on from varying degrees to which individual electrodes touch, or are physically coupled to, the patient&#39;s endocardial surface. Electrogram signals corresponding to electrodes in loose, poor or no contact with a patient&#39;s endocardium may be an order of magnitude smaller than those where electrodes are well coupled to the endocardial surface. 
       FIG. 5( c )  shows the artifacts of  FIG. 5( b )  superimposed on the simulated ECG signals generated from the rotor model of  FIG. 5( a ) .  FIG. 5( d )  shows a box plot corresponding to the 8×8 array of 64 electrode signals shown in  FIG. 5( a )  at a selected common time for all traces. Because of the artifacts from  FIG. 5( b )  introduced into the electrogram signals of  FIG. 5( c ) , the box plot of  FIG. 5( d )  appears quite irregular and chaotic, and the original spiral shape of the underlying rotor of  FIG. 5( a )  is not discernable to the eye. 
     The data shown in  FIG. 5( c )  were used to perform an analysis in accordance with method  200 , which was carried out in three main steps: (1) normalization/adjustment/filtering of electrogram signals; (2) generating three-dimensional smoothed electrogram surfaces for discrete times or time slices from the normalized/adjusted/filtered electrogram signals generated in the first main step, and (3) generating a velocity vector map based on the smoothed electrogram surfaces generated in the second main step. 
     Described now is one embodiment and illustrative example of the first main step of the method  200  (normalization/adjustment/filtering of electrogram signals). Referring now to  FIG. 5( e ) , there are shown the data of  FIG. 5( d )  after they have been subjected to one embodiment of an electrode signal normalization, adjustment and filtering process. After normalization and filtering, the simple rotor structure shown in  FIG. 5( a )  becomes visible in  FIG. 5( e ) . Uniform electrode signal amplitude minima and maxima were first calculated and then applied to individual electrogram signals to generate individual amplitude equalized electrogram signals. Unwanted artifacts such as ventricular depolarization signals were removed from the individual equalized electrogram signals by first averaging all electrogram signals to generate a common electrogram artifact signal, which was then subtracted from each of the equalized individual electrogram signals. The resulting equalized artifact-compensated electrogram signals were then high-pass filtered between 5 and 20 Hz to remove DC offsets from the electrogram signals such that the resulting filtered electrogram signals were approximately zeroed around the X (time) axis. These results are shown in  FIG. 5( e ) . 
     Next, a sliding time window ranging between about 0.1 seconds and about to 1 second in length was applied to each filtered electrogram signal to generate individual amplitude-adjusted electrogram signals. (In some embodiments, the length of the sliding time window corresponds to, or is less than, the slowest repetition frequency expected to be present.) The resulting sliding-window amplitude-adjusted electrogram signals were then stored for later use to generate image backgrounds in velocity vector maps, where they could be used to show low amplitude areas indicative of valve defects/artifacts, loose electrode contact, and/or areas of fibrosis in the patient&#39;s myocardium. In the sliding-window amplitude-adjusted electrogram signals, the respective minima and maxima of each position of the sliding time window were used to normalize the amplitude values of all signals between zero and one (or 0 and 255 on an 8-bit integer numeric scale). Because the maximum and minimum values occurred at different time points for electrodes placed in different locations, this process yielded spatial information regarding action potential wave patterns for each sampled time point (more about which is said below). 
     Now I describe one embodiment and illustrative example of the second main step of the method  200  (generating three-dimensional electrogram surfaces for discrete times or time slices, or estimation of spatial wave shapes). The second step of method  200  takes the spatial distributions of all electrodes and their normalized voltage values at discrete times (e.g., the data represented by the box plots corresponding to selected discrete times within the selected time window over which electrogram signals were acquired and measured), and estimates or generates from such data or box plots corresponding to given discrete times respective continuous voltage surfaces (or action potential waveform estimates) in space. Because the electrode pattern density is limited, and depending on the method that is used to generate the estimated voltage surfaces, the estimated surfaces typically deviate to some extent from “true” surfaces. Such deviations are usually relatively small in magnitude, however, since the spatial size of the action potential wave given by its velocity (e.g., 0.5 to 1 m/sec.) times the action potential duration (e.g., 0.1 to 0.2 sec.) is much larger (e.g., 0.05 m) than the electrode spacing (e.g., about 1 mm to about 10 mm), and thus spatial aliasing generally does not occur. The electrode grid provided by catheter  110  thus permits relatively good estimates of action potential wave shapes or wavefronts in the form of smoothed electrogram surfaces to be obtained as they propagate across the myocardium. On the other hand, because of the fast sampling rate (which can, for example, range between about 0.25 milliseconds and about 8 milliseconds, and which in some embodiments is nominally about 1 millisecond), changes in the spatial shape or expression of the action potential wavefront from one sample to the next are typically relatively small (e.g., about 1 mm) compared to the electrode distances (which in some embodiments nominally range between about 2 mm and about 7 mm). Thus, method  200  is capable of detecting spatial changes in action potential wavefronts or wave shapes using time domain information (i.e., small amplitude changes between time samples) to estimate changes in the spatial domain (where relatively small shifts in action potentials occur at given electrode measurement locations). 
     One embodiment of a method for estimating action potential wavefronts or wave shapes employs an 8×8 rectangular electrode grid (e.g., TOPERA®-like) model, which operates in two principal steps. First, each electrode/electrogram signal value at a discrete moment in time defines the height of its respective box in the “chess field” box plots shown in  FIGS. 5( d ) and 5( e ) . Second, a smoothed electrogram surface is generated for each box plot (or discrete slice of time) by calculating for each horizontal x-y point (typically on a 300×300 grid) an average of neighboring z-values (or electrical potentials) in the box plot. In 3D models that take assumed or actual electrode positions and spacing into account (using, e.g., information from a navigation or imaging system), smoothed electrogram surfaces are generated using 2D biharmonic spline interpolation techniques in combination with Green&#39;s function. Using the foregoing simple averaging approach, the smoothed electrogram surface of  FIG. 5( f )  was generated from the data shown in  FIG. 5( e ) . As shown in  FIG. 5( f ) , a spatial wave shape estimate of a rotor appears prominently in the forward center portion of the resulting smoothed surface, which tracks closely the original spiral wave shown in  FIG. 5( a ) . 
     Described now is one embodiment and illustrative example of the third main step of method  200  (generating a velocity vector map based on the electrogram surfaces). The third main step of method  200  uses the action potential wave shape estimates or electrogram surfaces generated at discrete times or time splices provided by the second main step to calculate a velocity vector map. For each sample interval a spatial wave shape or smoothed surface is calculated according to the second main step described above. Since the wave shapes differ only by a small delta between individual samples, and minimum and maximum values are normalized, shift vectors can be calculated at a spatial resolution that is higher than the spatial resolution of the electrodes (e.g., 30×30 samples). Since individual shifts between samples may differ according to random error, a velocity vector fit can be generated using 40 to 100 samples, where an average of observed shift vectors of the action potential wave shape care calculated. If the angle of a rotating wavefront is shifted by a few degrees per sample, the vector arrows will exhibit a circular pattern and in fact can resolve circles that are much smaller than inter-electrode distances. In one embodiment, the third main step of the method employs a vector pattern equation that best fits the observed movement of the evaluated spatial element or wavefront. In one embodiment that has been discovered to provide excellent results, and as described above, the velocity vector map is calculated using the Horn-Schunck optical flow method described above. That is, in one embodiment the Horn-Schunck optical flow method is used in the third main step of method  200  to estimate the velocity and direction of wavefronts or wave shapes between sampled times. Velocities of 40 to 100 samples are typically averaged to yield the most stable results. 
       FIG. 5( g )  shows the resulting wavefront velocity vectors, which are shown in  FIG. 5( g )  and elsewhere in the Figures as arrows  40  having directions and magnitudes associated therewith, calculated from a series of 60 averaged time slices of smoothed surfaces samples corresponding to the data shown in  FIG. 5( f ) . An active rotor is distinctly visible in the right-hand central portion of  FIG. 5( g ) , where arrows are flowing tightly in a counterclockwise direction. In  FIG. 5( g ) , action potential wavefronts are seen to be moving outwardly away from the detected active rotor (as would be expected in the case of an active rotor)). 
     Referring now to  FIGS. 6( a ), 6( b ) and 6( c ) , and with further reference to  FIG. 4 , there are shown some of the individual steps corresponding to the three main steps  230 ,  240  and  250  carried out according to one embodiment of method  200  disclosed and described herein. 
       FIG. 6( a )  shows one embodiment of steps  202  through  212  of main step  210  of  FIG. 4  (“normalize/adjust amplitudes, filter electrogram signals). In  FIG. 6( a ) , step  202  is shown as comprising receiving a data file corresponding to the EP recording of electrogram signals from a basket or other type of EP recording catheter positioned in a patient&#39;s heart  10 . The time interval over which such electrogram signals are recorded inside the patient&#39;s heart  10  may, of course, vary according to, among other things, the requirements of the diagnosis, examination, monitoring and/or treatment that is to be performed, and/or the suspected or known cardiac rhythm disorder from which the patient suffers. Illustrative, but non-limiting, examples of such time intervals range between about a second and one minute or more. Bad or poor fidelity traces or electrograms may be selectively removed or edited at this stage. 
     At step  204 , a high-pass filter is applied to the acquired EP data to remove DC offsets, as well as other undesirable low-frequency noise. In one embodiment, a 5 Hz high-pass filter is applied, although other filters, including band-pass filters, are contemplated, including, but not limited to, 10 Hz high-pass filters, 5-20 Hz band-pass filters, and 5-50 Hz band-pass filters. Notch- and low-pass filtering may also be applied in step  204 . Hanning, trapezoidal and other digital filtering and/or Fast Fourier Transform (FFT) filtering techniques may also be applied. 
     At step  206 , an average far-field electrogram signal is generated by stacking and averaging all electrogram traces. In the case of atrial EP recordings, the resulting estimate of a far-field ventricular depolarization is subtracted from each trace individually, thereby removing or at least reducing the far-field component therefrom. 
     At step  208 , the amplitudes of individual filtered electrogram signals are normalized with respect to a given standard deviation occurring over a predetermined time window (e.g., a moving window of 200 samples around a time value “x”). 
     At step  212 , a complete filtered sample array from the grid or basket catheter is provided as an output from first main step  210 . 
     Referring now to  FIG. 6( b ) , there is shown one embodiment of the second main step  230  of method  200  shown in  FIG. 4  (processing amplitude-adjusted electrogram signals across the 2D or 3D representation, map or grid to generate a plurality of three-dimensional electrogram surfaces, one surface being generated for each selected or predetermined discrete time or time slice). 
     In  FIG. 6( b ) , second main step  240  is shown as including steps  241  and  243 , which according to one embodiment are performed in parallel or near-parallel. At step  241 , digitally sampled and processed electrogram signals from step  212  of  FIG. 6( a )  are provided, and at step  242  an array of 200×200 empty 3D data points are generated, which correspond to the 2D or 3D representation, map or grid which is to be generated (or has already been generated). In one embodiment, such a representation, map or grid is formed by making a cylindrical projection representation, map or grid that corresponds to an approximate estimate or calculated map of the region of the patient&#39;s myocardial wall where the electrogram signals were acquired and measured (see step  243 ) by catheter  110 . Positional data from imaging or navigation system  70  can be provided at this stage to improve the positional accuracy of the individual locations within such grid where electrogram signals were acquired. In one embodiment, for each time slice or sampled time, a Z-value or electrical potential corresponding to the normalized, adjusted and/or filtered measured voltage of each individual electrogram is assigned a location in the representation, map or grid. 
     At step  244 , Green&#39;s function, or another suitable surface generating method, is used to generate a surface of Z-values for each time slice or sampled time (more about which is said below). In one embodiment, the surface corresponding to the Z-values is smoothed. 
     At step  245 , the calculated surface corresponding to each time slice or sampled time is provided as an output, with, for example, a 200×200 array of smoothed data points corresponding to the smoothed surface being provided for each time slice or sampled time. Note that in some embodiments the intervals at which time slices are selected, or the individual time slices themselves, may be predetermined, or may be selected automatically or by the user. 
       FIG. 6( c )  shows step  250  corresponding to one embodiment of the third main step of  FIG. 4  (processing the plurality of three-dimensional electrogram surfaces generated through time to generate a velocity vector map corresponding at least partially to the 2D or 3D map) carried out, by way of non-limiting example, using optical flow analysis and estimation techniques described and disclosed elsewhere herein. In  FIG. 6( c ) , third main step  250  is shown as including step  251 , which in one embodiment entails sequentially accessing the individual surfaces generated for selected time slices and/or discrete times in step  240 . At steps  252  and  253 , adjacent time slices are analyzed and processed sequentially. In step  254 , a spatial gradient corresponding to each point of the representation, map or grid is calculated say over, for example, the last 100 time slices. At step  255 , a continuous graphical output of calculated flow vectors can be provided as a real-time or near-real-time output. At step  256 , the most likely flow vector magnitude (or velocity) and direction for each point that minimizes energy is calculated. At step  257 , X (or time) is incremented, and the foregoing calculations are repeated and refined, the final output of which is a vector velocity map of the type shown, by way of non-limiting example, in  FIGS. 5( g ), 7( e ), 7( i ), 7( j ), 7( k ), 7( l ) ,  8 ,  9 ,  10 ( a ),  10 ( c ), and  10 ( e ). 
       FIGS. 7( a ) through 7( j )  show the results of processing simulated atrial cardiac rhythm disorder data using the methods and techniques described and disclosed above, where the concept of analyzing complex rotor structures was applied to a data set of simulated data. The simulated data shown in  FIG. 7( a )  primarily comprised stable active and passive rotors, as described in Carrick et al. in “Prospectively Quantifying the Propensity for Atrial Fibrillation: A Mechanistic Formulation,” R. T. Carrick, P. S. Spector et al.; Mar. 13, 2015, PLOS ONE, DOI:10.1371, journal.pone.0118746, the entirety of which is hereby incorporated by reference herein. From Carrick, et al.&#39;s video corresponding to the foregoing publication, and referring now to  FIG. 7( a )  herein, stable rotor data were recorded for a frame delineated by the indicated blue square, where there are seven rotors. The recording was accomplished using the luminance of the video frame in an 8×8 matrix with an 8-bit signal depth, thereby to simulate electrogram signal data acquired using a conventional 64-electrode 8×8 basket catheter. The overall video comprised 90 frames. All data shown n  FIG. 7( a )  were taken from frame  60 . Signal amplitudes from frame  60  are shown in the chess field and box plots of  FIGS. 7( b ) and 7( c ) , respectively. 
     In  FIG. 7( a ) ,  7  rotors are shown as green circles  45  lying within the blue rectangle. In  FIG. 7( b ) , a box plot of 8×8 matrix amplitudes is shown having amplitudes corresponding to frame  60 .  FIG. 7( d )  shows the estimated wavefront or smoothed surface corresponding to frame  60 .  FIG. 7( e )  shows the vector velocity map generated from the data corresponding to  FIG. 7( a )  (which was generated on the basis of all 90 frames or times slices). Reference to  FIG. 7( e )  shows that seven active rotors (marked as green circles  45 ) are apparent, as are two passive rotors (marked as red stars  46 ). 
     Referring now to  FIGS. 7( b ) and 7( c ) , it will be seen that the 2D and 3D box patterns shown therein provide rough estimates of the spatial wavefronts shown in  FIG. 7( a ) . In  FIG. 7( d ) , however, the original data shown in  FIG. 7( a )  are reproduced fairly accurately, and also provide a good input to the vector velocity map of  FIG. 7( e )  (which nicely reveals the 7 active rotors visible in  FIG. 7( a ) ). The yellow vector arrows in  FIG. 7( e )  not only show the rotational centers of the individual rotors, but also show that active rotors indicated by green circles are driving sources of the wave fronts because the calculated vectors of the active rotors always point centrifugally away from the rotor centers. In contrast, the two red stars shown in  FIG. 7( e )  indicate the locations of passive rotors or flow turbulences that, while circular in shape, have centripetal vector directions to at least on one side of the rotor centers associated therewith. 
     Discrimination between active and passive rotors is critical to making proper therapeutic decisions regarding the delivery of ablation therapy, which should only target structures underlying the drivers of atrial fibrillation (namely, active rotors only, and not passive rotors). 
     Next, the effects of typical artifact disturbances on the signals of the 64 channels of data shown In  FIGS. 7( a ) through 7( d )  were determined by introducing simulated variable amplitude DC-offset noise and artifacts into the electrogram signals. The objective was to test the extent to which such artifacts and noise might impair or disable the ability of method  200  to detect rotors in the data. 
       FIGS. 7( f ) and 7( g )  show the same box plot data as  FIGS. 7( b ) and 7( c ) , respectively, but with the foregoing-described superimposed and introduced artifacts. That is,  FIGS. 7( f ) and 7( g )  show the chess field and box plots of the disturbed electrogram signals corresponding to frame  60 . After filtering and normalization in step  210 , the original rotor structure shown in  FIG. 7( a )  once again becomes visible in  FIG. 7( h )  following completion of the main second step  240  of the method. 
     Upon applying smoothed surface calculations and fitting (as shown in  FIG. 7( i ) ), method  200  is seen to detect only five of the seven active rotors shown in  FIG. 7( a ) . One additional active rotor, however, was detected at a different location (see  FIG. 7( i ) ). 
     The largest variation in results was seen at positions where the introduction of the artifacts and noise reduced relative amplitude values by the greatest amount, as indicated by the white areas shown in  FIG. 7( j ) . The white areas shown in  FIG. 7( j )  were generated by using the sliding-window amplitude-adjusted electrogram signal techniques described above, where electrograms processed using sliding-window techniques were used to generate the image background (including the white areas) shown in the velocity vector map of  FIG. 7( j ) . The white areas in  FIG. 7( j )  thus correspond to low amplitude areas potentially indicative of valve defects or artifacts, loose electrode contact, and/or areas of fibrosis in the patient&#39;s myocardium. It is important to point out that the low-amplitude areas shown in white in the various velocity vector maps presented herein are not calculated using Green&#39;s function or optical flow data processing techniques. Instead, and as described above, these low-amplitude regions or areas may be detected by assessing the relative amplitudes of electrogram signals in step  210 . 
     In the white areas of  FIG. 7( j ) , the resulting velocity vector map shows that the active rotors indicated therein are slightly moved closer together than in  FIG. 7( i ) , and on the left center side of  FIG. 7( j )  two rotors appearing in  FIG. 7( i )  are revealed as a single active rotor n  FIG. 7( j ) .  FIGS. 7( a ) through 7( j )  show that there are limits to the resolution that can be achieved using a conventional 8×8 array of sensing electrodes in a basket catheter having standard inter-electrode spacing. Thus, higher electrode densities and more recording channels could increase the resolution and accuracy of the results obtained using method  200 . 
     After confirming that method  200  was capable of detecting complex rotor structures accurately in a patient&#39;s myocardium—even in the presence of strong artifacts and noise—method  200  was applied to different time portions of the actual patient data shown in  FIG. 5( b )  so as to test further the method&#39;s efficacy and accuracy. A velocity vector map corresponding to data acquired between 4,700 milliseconds and 5,100 milliseconds in the original EP recording of  FIG. 5( b )  is shown in  FIG. 8( a ) . 
     As shown in  FIG. 8( a ) , four rotors indicated by circles  1 ,  2  and  3  and a star  4  were detected. Circles  1  and  2  in  FIG. 8( a )  appear to denote active rotors that are interacting with one another. Circle ( 3 ) in  FIG. 8( a )  may be an active rotor, but exhibits some centripetal components. Star  4  in  FIG. 8( a )  clearly corresponds to a passive rotor. Next, a velocity vector map corresponding to the same data set for data acquired between samples 0 seconds and 400 milliseconds was generated, the results of which are shown in  FIG. 8( b ) . 
     Differences between the results shown in  FIGS. 8( a ) and 8( b )  permit a deeper insight into the true rotor structure of this patient&#39;s myocardium, as best shown in  FIG. 8( b ) . In the earlier time interval (0 msec. to 400 msec.) of  FIG. 8( b ) , the two associated rotors  1  and  2  shown in  FIG. 8( a )  are not yet active, while there is only a single active rotor  5  in  FIG. 8( b )  located between the positions of rotors  1  and  2  shown in  FIG. 8( a ) . Rotors  1  and  2  in  FIG. 8( b )  show up at slightly different positions, but now appear clearly as passive rotors representing likely turbulences generated at the border of a mitral valve artifact. 
     Thus, a health care professional can select differing time windows over which to apply method  200  to an EP mapping data set as a means of gaining a better understanding of the behavior of active and passive rotors, fibrotic regions, areas affected by valve defects or artifacts, breakthrough points and areas or defects that are at work in the patient&#39;s myocardium. The velocity vector maps generated by method  200  permit a health care professional to identify such cardiac rhythm disorders in a patient&#39;s myocardium with a degree of precision and accuracy that has heretofore not been possible using conventional EP mapping and intravascular basket or spline catheter devices and methods. 
     Referring now to  FIG. 9 , there is shown another example of a vector velocity map generated from actual patient data using method  200 . In  FIG. 9 , arrows  40  correspond to action potential wavefront velocity vectors, which as illustrated have differing magnitudes and directions associated herewith. As shown in  FIG. 9 , various cardiac rhythm defects and disorders become apparent as a result of the generated vector velocity map. The defects and disorders revealed by the vector velocity map of  FIG. 9  include an active rotor (where the active rotor propagation direction is indicated in the bottom right of  FIG. 9  by green circle  43  rotating in a clockwise or centrifugal direction), a breakthrough point in the bottom left of  FIG. 9 , fibrotic areas indicted by low-amplitude white areas in the lower portion of  FIG. 9 , and a mitral valve defect indicted by the white area in the upper portion of  FIG. 9 . 
     Referring now to  FIGS. 10( a ) through 10( d ) , there are shown further results obtained using actual patient data. The raw data corresponding to  FIGS. 10( a ) through 10( d )  were acquired from a single patient&#39;s right atrium using a 64-electrode basket catheter and corresponding EP mapping/recording system. Data were acquired at a 1 millisecond rate over a time period of 60 seconds in all 64 channels.  FIGS. 10( a ) and 10( b )  correspond to one selected 2 second time window, and  FIG. 10( d )  corresponds to another time window from the same data set.  FIG. 10( d )  shows the color-schemes employed in  FIGS. 10( a ), 10( b ), and 10( d ) . 
     The vector velocity map of  FIG. 10( a )  generated using method  200  clearly reveals an active rotor located at chess board position D/E, 2/3. The vector velocity map of  FIG. 10( b )  was also generated using method  200 , but using data acquired from only 16 electrodes in grid D-G, 2-5. As shown in  FIG. 10( b ) , the active rotor evident in  FIG. 10( a )  is nearly equally evident in  FIG. 10( b )  despite the significantly more sparse data grid employed to produce the velocity vector map. These remarkable results obtained using a sparse electrode grid are due in large part to the robustness, stability and accuracy of method  200 , as it has been applied to electrographical flow problems. 
       FIG. 10( d )  shows another example of results obtained using method  200  and EP mapping data obtained from the same patient as in  FIGS. 10( a ) and 10( b ) , but over a different time window. Note also that  FIG. 10( d )  shows that method  200  has successfully detected one active rotor (at chess board location F2/3), three active focus points, and one passive rotor (at chess board location F8). 
     It will now be seen that method  200  provides not only rotational direction information, but also provides high-resolution spatial information regarding the presence and location of rotors despite the use of sparse electrode grid spacing. Rotors can also move over time in a patient&#39;s myocardium, even during the time interval over which EP mapping is being carried out. The increased spatial and temporal resolution of method  200  permits such shifts in rotor location to be detected. 
     In some embodiments, and as described above, multiple or different types of EP mapping and ablation catheters can be used sequentially or at the same time to diagnose and/or treat the patient. For example, a 64-electrode CONSTELLATION basket catheter can be used for EP mapping in conjunction with a PENTARAY16- or 20-electrode EP mapping catheter, where the PENTARAY EP mapping catheter is used to zero in on, and provide fine detail regarding, a particular region of the patient&#39;s myocardium that the basket catheter has revealed as the location of a source of a cardiac rhythm disorder or irregularity. In addition, catheter  110  or any other EP mapping catheter used in system  100  may be configured to provide ablation therapy (in addition to EP mapping functionality). The various catheters employed in system  100  may also include navigation elements, coils, markers and/or electrodes so that the precise positions of the sensing, pacing and/or ablation electrodes inside the patient&#39;s heart  10  are known. Navigational data can be employed by computer  300  in method  200  to provide enhanced estimates of the locations of the electrodes in the representations, maps or grids generated thereby, which in turn increases the accuracy and efficacy of the resulting velocity vector maps generated in method  200 . 
     In another embodiment, computing device/system  300  is operably connected to a storage medium such as a hard drive or non-volatile memory located in, or operably connected to, data acquisition device  140 , where computing device  300  is configured to trigger an external switch operably connected to data acquisition device  140  which permits the upload of conditioned electrogram signal data from data acquisition device  140  to computing device  300 . According to such a configuration, computing device  300  and data acquisition device  140  can remain galvanically isolated from one another, and the need to physically swap USB memory sticks between data acquisition device  140  and computing device  300  is eliminated. This, in turn, permits system  100  to operate more efficiently and quickly, and to provide vector velocity maps to the health care professional in near-real-time while the EP mapping procedure is being carried out within the patient&#39;s heart  10 . 
     In method  200 , electrogram signals and processed data may be delivered or communicated to system  100 , e.g., via a data carrier, after they have been acquired by the electrodes and stored for later processing. The various steps recited in the claims, and the sub-steps recited in each step, need not necessarily be carried out in the precise order in which they are recited. 
     The various systems, devices, components and methods described and disclosed herein may also be adapted and configured for use in electrophysiological mapping applications other than those involving the interior of a patient&#39;s heart, more about which is said below. These alternative applications can include internal or external EP mapping and diagnosis of a patient&#39;s epicardium or other portions of the patient&#39;s heart, a patient&#39;s spinal cord or other nerves, or a patient&#39;s brain or portions thereof. 
     Referring now to  FIGS. 1( a ) through 10( d ) , and also referring to the foregoing portions of the specification, it will now be seen that various embodiments of EGF techniques, methods, systems, devices, and components are described and disclosed herein, which can be employed to reveal the locations of sources of cardiac rhythm disorders in a patient&#39;s heart, including, but not limited to, rotors and sources that cause or contribute to AF. 
     Moreover, note that the various data processing steps described and disclosed above explicitly in connection with  FIGS. 1( a ) through 10( d )  in furtherance of providing useful EGF results can be carried out using numerous permutations, combinations, variations, adaptations, and modifications, and signal conditioning and/or filtering techniques, which though they may not be specifically or explicitly mentioned in the specification hereof, are nonetheless contemplated and fall within the spirit and scope of the various inventions described and disclosed herein. By way of example, a variety of two-dimensional digital filtering, image filtering, and/or digital signal processing (DSP) techniques are also contemplated that may be employed in one or more the data conditioning and/or processing steps disclosed and described above, such as phase correlation, block-based methods, motion estimation, measuring visual motion, autocorrelation, cross-correlation, convolution, deconvolution, adaptive deconvolution, wavelet deconvolution, source deconvolution, and/or median filtering. By way of further example, at least some of these alternative signal processing techniques may be used to effect or aid in surface and/or data grid processing steps, and/or optical flow processing steps. 
     There are now described in greater detail various embodiments of, and details concerning, electrographic flow (EGF) analysis, which broadly may employ some or many of the techniques and concepts described above. EGF analysis and mapping provides novel methods to identify Atrial Fibrillation (AF) drivers based on modeling electrical potential surfaces and subsequent flow analysis. Sources of excitation during AF can be characterized and monitored. Some of EGF embodiments described and disclosed below employ some of the data acquisition, processing and interpretation techniques described above, which are applied to the problem of efficiently and cost-effectively screening patients for atrial fibrillation without undertaking costly invasive medical procedures, such as EP mapping using intra-cardiac basket catheters. 
     Recent work in the field of atrial fibrillation using EGF techniques, conducted to further validate and test the various inventions described and disclosed herein, has revealed highly useful results. Described below in detail are recent results obtained using raw intra-cardiac EP mapping data that were originally obtained using conventional EP mapping techniques and systems (i.e., the TOPERA® cardiac arrhythmia mapping system), but which were subsequently processed and analyzed using the novel EGF techniques disclosed and described herein (hereafter “the EGF studies”). The original intra-cardiac EP mapping data were acquired from 108 patients in three different studies in hospitals located in Hamburg, Germany, Berlin, Germany, and Rotterdam, The Netherlands, using a conventional basket catheter system; namely, the aforementioned TOPERA system utilizing FIRMap catheters and RhythmView workstations. In the original TOPERA-based studies, focal impulse and rotor-mapping (FIRM) were performed in addition to pulmonary vein isolation. In the studies, one-minute epochs of unipolar electrograms were recorded using a 64-pole FIRMap basket catheter in both atria. 
     The aim of the retrospective EGF-based studies described herein was to evaluate the correlation between EGF velocities around given sources and their corresponding spatial variabilities (SV) and stabilities (SST). SST was calculated as a percentage of time over which a source was detected. AF sources identified with EGF mapping showed a wide range of SV and SST. Less stable AF sources with high spatial variability showed reduced excitation propagation velocity, while very stable AF sources displayed a high average velocity in their vicinity. Catheter ablation was shown to reduce the stability of sources and velocities, suggesting a role of these parameters in guidance of ablation. 
     More about these retrospective EGF studies is said below. However, some details regarding recent EGF-based studies may be found in the following publications:
         (a) “Velocity characteristics of atrial fibrillation sources determined by electrographic flow mapping before and after catheter ablation” to Bellmann et al., International Journal of Cardiology, Jul. 1, 2019, is Volume 286, Pages 56-60, DOI: https://doi.org/10.1016/j.ijcard.2019.02.006 (hereafter “the Bellman I publication);   (b) Identification of active atrial fibrillation sources and their discrimination from passive rotors using electrographical flow mapping, Clinical Research in Cardiology, May, 2018, DOI: 10.1007/s00392-018-1274-7 (hereafter “the Bellman II publication), and   (c) “Electrographic Flow Mapping—A Novel Technology for Endocardial Driver Identification in Patients with Persistent Atrial Fibrillation” to Bellmann et al., Poster Session IV, C-PO004-76, S356 Heart Rhythm, Vol. 14, No. 5, May Supplement 2017 (hereafter “the Bellman III publication);       

     Each of the foregoing Bellmann I, Bellmann II, and Bellmann III publications is hereby incorporated by reference herein, each in its respective entirety. 
     The EGF studies described and disclosed herein show that EGF techniques and analysis can be used as a powerful tool to characterize AF sources, and to sort patients into different types; namely, A-type patients, B-type patients, and C-type patients. A- and B-type patients are characterized by well-defined and steady EGF patterns and sources in the atria, which in some embodiments, and as described above, can be presented as 2D or 3D EGF maps. Contrariwise, C-type patients exhibit chaotic EGF patterns that do not exhibit stationary or stable sources in the atria. In the EGF studies, FIRM data from 108 patients with known outcomes were processed and analyzed using EGF technology. 
     The best predictor for outcome (freedom from AF) turns out to be EGF Source Activity at the end of each procedure (Final Activity). Source Activity is defined as the percent of active time per minute (or other suitable unit of time) that a leading source has been detected. EGF steadiness, E/s (or electrodes/second), is a parameter relevant for the characterization of flow, and therefore finds use in patient stratification or classification (e.g., into A, B, or C types). Flow angle stability (FAS) over time, or flow angle stability per suitable unit of time, is another parameter that can be used to evaluate outcome or freedom from AF, and has proved to be a more useful predictor of outcome (freedom from AF) than steadiness or E/s. 
     EGF Source Activity, and how it is computed, requires no further explication. Obviously, different time periods can be used to estimate EGF Activity. Source Activity is the percentage of time a source is detectable. Final activity is the activity that is measured after all intra-cardiac ablation and PVI procedures have been completed. 
     Steadiness (or E/s) is essentially a measure of velocity (or the rate at which a detected signal moves from one electrode to another), and the consistency or steadiness thereof. If E/s is low, then detected flow signals are low in amplitude or inconsistent. If E/s is high, then detected flow signals are strong. Examples of low E/s values range between about 0 and about 5. Examples of high E/s values are those greater than about 10 or 15. 
     Flow angle stability (FAS) is computed by determining, for a given pixel or point in a flow or vector field, the angular displacement that occurs between successive time samples for the same pixel or point, over a given recording period (e.g., one minute, with each pixel being sampled, e.g. at a rate of 1 msec.). The maximum angular displacement that can occur between successive time samples in a sequence of vector fields, given a reference direction, is equal to π radians (or 180 degrees). FAS can be computed for each pixel or point in a flow field to produce an FAS map. Some examples of FAS values produced using 1 minute recordings and 1 msec. sample rates are: (a) about 0 to about π/4 for a high FAS (i.e., a very stable source that does not move much), and (b) about π/4 to about π/2 for an unstable, chaotic, or highly variable source. 
     EGF Activity levels combined with, for example, one or more of the number detected sources, the activity of detected sources, the flow angle stability of detected sources, and the steadiness of detected sources permits a classification of patients into three types (A, B and C). A- and B-type patients (e.g., 56% of analyzed patient population) significantly benefited from source ablation in addition to PVI, and exhibited an increase in freedom from AF after 12 months, rising from 19% to 81% (p value=0.0000016). C-type patients (44% of analyzed patient population) showed no significant source ablation benefit after successful PVI. Moreover, in C-type patients, source ablation had no significant effect, and on average these patients exhibited 73% freedom from AF after 12 months. In other words, there is no need or benefit for most C-type patients to undergo either: (a) invasive intra-cardiac basket catheter EP mapping procedures, or (b) intra-cardiac ablation procedures (more about which is said below). 
     The EGF studies and the corresponding EGF technology employed therein are now described in further detail. 
       FIG. 11( a )  shows one embodiment of an example of EGF data processing and analysis  600 , as will become apparent after having read and understood the preceding portions of the specification and accompanying Figures, and with further reference to the portions of the specification and accompanying Figures that follow. Method  600  of  FIG. 11( a )  was employed in the EGF studies. Moving from left to right in  FIG. 11( a ) , at step  601  intra-cardiac electrogram signals are first pre-processed, and as described above in detail, minimal energy isochrones voltage maps are generated at step  603 . Next electrographic flow vector flow maps are generated at step  605 , followed by the detection of sources and rotors at step  607 , which as shown in  FIG. 11( a )  can involve, by way of non-limiting example, the generation of source identification maps, active source prevalence maps, streamline-plot maps, and/or passive rotor prevalence maps. Although  FIGS. 11( a ) through 11( h )  refer to and are based upon intra-cardiac basket catheter data acquired during the aforementioned EGF studies (and subsequent corresponding EGF data processing and analysis), the same or similar techniques can also be applied to electrograms acquired from at or near the external body surface of a patient (more about which is said below). 
       FIG. 11( b )  shows EGF results obtained in a Type-A patient involved in the studies before and after intra-cardiac ablation has been performed on the detected leading source. As shown in  FIG. 11( b ) , activity has dropped from 41.7% to 15.8%, and steadiness has decreased from 19 E/s to 11 E/s post-ablation. In other words, the detected leading source has essentially been neutralized and eliminated by ablation, as shown by the displayed EGF results. 
       FIG. 11( c )  shows some EGF results obtained in a pilot study forming a portion of the EGF studies that was conducted in Hamburg. In the pilot study, 24 patients were sorted and classified into A-, B- and C-type groups according to Steadiness (left panel) and Final Activity (right panel), which both showed clear correlations with outcome. 1 A-type patients exhibited Final Activity &gt;25%, (the highest specificity for recurrence). B-type patients exhibited Steadiness ≤10.0 E/s (the highest sensitivity for recurrence). These threshold values were subsequently confirmed in validation studies conducted with 78 further patients from Berlin and Rotterdam (as described above, and which also comprised portions of the EGF studies).  1  In one embodiment, steadiness means EGF velocity in the source vicinity during an active 2 s segment; other such active segments are also contemplated, including, but not limited to, about 1 second, about 2 seconds, about 3, seconds, about 4 seconds, about 5 seconds, between about 0.5 seconds and about 10 seconds, and between about 1 second and about 5 seconds. Spatial Variability or Steadiness means surface coverage of 80% of a detected activity over a one minute time period. Other such time periods for detected activities are contemplated, including, but not limited to, about 10 seconds, about 20 seconds, about 30 seconds, about 40 seconds, about 50 seconds, about 70 seconds, about 80 seconds, about 90 seconds, between about 20 seconds and about 3 minutes, between about 30 seconds and about 2 minutes, and between about 30 seconds and about 90 seconds. Changing the selected active segment percentages and/or the detected activity time periods can also lead to changes in calculated Steadiness and Final Activity values. 
     Tables 1 and 2 below set forth further data and statistics regarding the pilot study conducted in Hamburg. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Recurrence of AF or AT after 3, 6 or 12 months 2   
               
            
           
           
               
               
               
               
            
               
                   
                 Type 
                 Patient 
                 Steadiness 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 A 
                 Pat19 
                 39.0 
               
               
                   
                 A 
                 Pat23* 
                 24.80 
               
               
                   
                 B 
                 Pat10* 
                 18.10 
               
               
                   
                 A 
                 Pat22 
                 16.70 
               
               
                   
                 A 
                 Pat16 
                 14.90 
               
               
                   
                 A 
                 Pat24 
                 12.20 
               
               
                   
                 B 
                 Pat08* 
                 11.50 
               
               
                   
                 C 
                 Pat14* 
                 11.50 
               
               
                   
                 A 
                 Pat09* 
                 11.40 
               
               
                   
                 B 
                 Pat01* 
                 11.30 
               
               
                   
                 B 
                 Pat05 
                 11.20 
               
               
                   
                 A 
                 Pat18 
                 11.20 
               
               
                   
                 C 
                 Pat20 
                 11.20 
               
               
                   
                 C 
                 Pat06* 
                 10.70 
               
               
                   
                 B 
                 Pat21 
                 10.00 
               
               
                   
                 C 
                 Pat11* 
                 9.65 
               
               
                   
                 A 
                 Pat02 
                 9.50 
               
               
                   
                 C 
                 Pat07* 
                 8.90 
               
               
                   
                 C 
                 Pat12 
                 8.90 
               
               
                   
                 C 
                 Pat25 
                 7.90 
               
               
                   
                 B 
                 Pat03 
                 7.40 
               
               
                   
                 B 
                 Pat13* 
                 7.30 
               
               
                   
                 C 
                 Pat04 
                 6.20 
               
               
                   
                 C 
                 Pat17 
                 5.00 
               
               
                   
                   
               
               
                   
                   2 *paroxysmal AF; $source in PV ablated by PVI to termination 
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Freedom from AF or AT after 3, 6 and 12 months 3   
               
            
           
           
               
               
               
               
            
               
                   
                 Type 
                 Patient 
                 Final Activity 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 A 
                 Pat23* 
                 93.00 
               
               
                   
                 A 
                 Pat16 
                 64.40 
               
               
                   
                 A 
                 Pat18 
                 55.50 
               
               
                   
                 A 
                 Pat09* 
                 49.40 
               
               
                   
                 A 
                 Pat19 
                 49.30 
               
               
                   
                 B 
                 Pat10* 
                 32.20 
               
               
                   
                 B 
                 Pat21 
                 29.20 
               
               
                   
                 A 
                 Pat22 
                 28.50 
               
               
                   
                 B 
                 Pat05 
                 24.70 
               
               
                   
                 C 
                 Pat25 
                 22.40 
               
               
                   
                 B 
                 Pat08* 
                 21.90 
               
               
                   
                 C 
                 Pat04 
                 21.80 
               
               
                   
                 C 
                 Pat20 
                 20.70 
               
               
                   
                 B 
                 Pat03 
                 20.30 
               
               
                   
                 C 
                 Pat06* 
                 20.10 
               
               
                   
                 C 
                 Pat07* 
                 20.00 
               
               
                   
                 C 
                 Pat12 
                 19.75 
               
               
                   
                 B 
                 Pat13* 
                 19.50 
               
               
                   
                 A 
                 Pat02 
                 19.00 
               
               
                   
                 C 
                 Pat14* 
                 18.70 
               
               
                   
                 C 
                 Pat11* 
                 17.60 
               
               
                   
                 C 
                 Pat17 
                 14.80 
               
               
                   
                 B 
                 Pat01* 
                 14.31 
               
               
                   
                 A 
                 Pat24 
                 9.10 
               
               
                   
                   
               
               
                   
                   3 *paroxysmal AF; $source in PV ablated by PVI to termination 
               
            
           
         
       
     
     In the validation studies conducted with 78 patients in Berlin and Rotterdam, patients were once again sorted and classified according to Final Activity, and the following results were obtained: 
     Final Activity &gt;25%:
         19% free of AF after 3+6+12m   53% free of AF after 12m.       

     Final Activity 25% to 20%:
         55% free of AF after 3+6+12m   80% free of AF after 12m   (p=0.04).       

     Final Activity &lt;20%:
         77% free of AF after 3+6+12m   95% free of AF after 12m   (p=0.0007).       

     Tables 3 and 4 show some data from the validation studies. From among the 78 patients in the validation study, 45 patients were classified as A- or B-type patients (58% of the validation study population). Table 3 data represent 29 patients where: (a) FIRM ablation was not effective (Final Activity &gt;25% and Steadiness &gt;10); (b) 14% of patients exhibited freedom from AF/AT after 3, 6 and 12 months, and 69% of patients suffered a recurrence of AF/AT after 12 months. Table 4 data represent 16 patients where: (a) FIRM ablation was effective (Final Activity &lt;25% or Steadiness &lt;10); (b) 75% of the 16 patients were free from AF/AT after 3, 6 and 12 months (p=0.00004); and (c) 0% of the 16 patents suffered a recurrence of AF/AT after 12 months (p=0.00015). 
     In the validation studies, patients were classified as A-, B- or C-type patients according to the following criteria: 
     Type-A or B Patients: 
     Maximal Activity of leading source &gt;25% and Steadiness &gt;10 E/s
         A-type: single source only   B-type: two or more sources       

     Type-C: 
     Maximal Activity of leading source &lt;25% or
         Spatial Variability or Steadiness &lt;10 E/s).       

     The data in Tables 3 and 4 indicate that source ablation of A- and B-type patients significantly improves outcomes. Moreover, the validation studies confirm the strong correlation discovered in the pilot study between outcome and Final Activity. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Patients in the validation study where FIRM ablation was ineffective 
               
            
           
           
               
               
               
               
            
               
                   
                   
                   
                 Recurrence AF/AT after 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Activity 
                 3 and 6 
                 or 12 
               
            
           
           
               
               
               
               
               
               
            
               
                 Type 
                   
                 Max 
                 Final 
                 months* 
                 months** 
               
               
                   
               
               
                 A 
                 Bp19 
                 93.50 
                 93.50 
                 1 
                 0 
               
               
                 A 
                 Case83 
                 79.90 
                 79.90 
                 1 
                 1 
               
               
                 A 
                 Case39 
                 71.70 
                 71.70 
                 1 
                 1 
               
               
                 A 
                 BP03 
                 81.50 
                 67.20 
                 1 
                 0 
               
               
                 B 
                 Case06 
                 58.30 
                 58.30 
                 0 
                 0 
               
               
                 A 
                 Case23 
                 79.80 
                 57.00 
                 1 
                 1 
               
               
                 A 
                 Case47 
                 54.90 
                 54.90 
                 1 
                 1 
               
               
                 A 
                 Case13 
                 54.40 
                 54.40 
                 1 
                 1 
               
               
                 A 
                 Case33 
                 53.60 
                 53.60 
                 0 
                 0 
               
               
                 A 
                 Case69 
                 65.50 
                 51.90 
                 1 
                 1 
               
               
                 A 
                 Case50* 
                 50.80 
                 50.80 
                 1 
                 0 
               
               
                 B 
                 Case87 
                 50.30 
                 50.30 
                 1 
                 1 
               
               
                 C 
                 Case76 
                 49.70 
                 49.70 
                 0 
                 0 
               
               
                 B 
                 Case40 
                 60.20 
                 60.20 
                 1 
                 1 
               
               
                 B 
                 Case70 
                 44.30 
                 44.30 
                 1 
                 1 
               
               
                 A 
                 Case36 
                 41.90 
                 41.90 
                 1 
                 1 
               
               
                 B 
                 Case07 
                 33.90 
                 33.90 
                 1 
                 0 
               
               
                 A 
                 Case77 
                 33.60 
                 33.60 
                 1 
                 1 
               
               
                 B 
                 Case32 
                 33.00 
                 33.00 
                 1 
                 1 
               
               
                 A 
                 Case67 
                 46.20 
                 30.70 
                 1 
                 0 
               
               
                 C 
                 BP17 
                 30.57 
                 30.57 
                 0 
                 0 
               
               
                 B 
                 Case25 
                 30.40 
                 30.40 
                 0 
                 0 
               
               
                 B 
                 Case18 
                 30.20 
                 30.20 
                 1 
                 1 
               
               
                 C 
                 Case56 
                 29.90 
                 29.90 
                 1 
                 0 
               
               
                 A 
                 Case74 
                 29.80 
                 29.80 
                 1 
                 0 
               
               
                 A 
                 Case35 
                 58.30 
                 29.30 
                 0 
                 0 
               
               
                 B 
                 Case17 
                 29.20 
                 29.20 
                 1 
                 0 
               
               
                 B 
                 Case84 
                 28.50 
                 28.50 
                 1 
                 1 
               
               
                 A 
                 Case12 
                 66.30 
                 27.80 
                 1 
                 0 
               
               
                 C 
                 Case58 
                 33.20 
                 27.50 
                 0 
                 0 
               
               
                 A 
                 Case59 
                 32.20 
                 27.50 
                 1 
                 1 
               
               
                 C 
                 Case03 
                 27.40 
                 27.40 
                 0 
                 0 
               
               
                 C 
                 Case24 
                 26.60 
                 26.60 
                 2 
                 0 
               
               
                 B 
                 Case15 
                 26.30 
                 26.30 
                 1 
                 1 
               
               
                   
                 Case52 
                 25.90 
                 25.90 
                 1 
                 0 
               
               
                 B 
                 Case80 
                 25.00 
                 25.00 
                 1 
                 1 
               
               
                   
               
               
                 *Final activity: 25-100%. Freedom from AF/after 3 and 6 months = 19% 
               
               
                 **Final activity: 25-100%. Freedom from AF after 12 months = 53% 
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Patients in the validation study where FIRM ablation was effective 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Recurrence AF/AT after 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Activity 
                 3 and 6 
                 or 12 
               
            
           
           
               
               
               
               
               
               
            
               
                 Type 
                   
                 Max 
                 Final 
                 months* 
                 months** 
               
               
                   
               
               
                 B 
                 Case19 
                 28.20 
                 24.80 
                 2 
                 0 
               
               
                 C 
                 Case51 
                 24.50 
                 24.50 
                 1 
                 1 
               
               
                 C 
                 BP04 
                 24.40 
                 24.40 
                 0 
                 1 
               
               
                 C 
                 Case48* 
                 24.40 
                 24.40 
                 0 
                 0 
               
               
                 B 
                 Case20 
                 33.60 
                 23.60 
                 0 
                 0 
               
               
                 A 
                 BP05 
                 41.70 
                 23.10 
                 0 
                 1 
               
               
                 B 
                 BP11 
                 26.55 
                 23.10 
                 1 
                 1 
               
               
                 A 
                 BP12 
                 98.90 
                 22.80 
                 0 
                 1 
               
               
                 B 
                 Case41 
                 28.60 
                 22.70 
                 0 
                 0 
               
               
                 B 
                 Case45 
                 43.80 
                 22.70 
                 0 
                 1 
               
               
                 C 
                 Case78 
                 22.70 
                 22.70 
                 0 
                 0 
               
               
                 C 
                 Case81 
                 22.70 
                 22.00 
                 1 
                 1 
               
               
                 C 
                 Case72 
                 22.00 
                 21.70 
                 1 
                 0 
               
               
                 C 
                 Case05 
                 21.70 
                 21.70 
                 0 
                 1 
               
               
                 C 
                 Case82 
                 23.60 
                 21.21 
                 1 
                 1 
               
               
                 C 
                 BP14 
                 22.28 
                 21.20 
                 1 
                 1 
               
               
                 C 
                 Case88 
                 38.60 
                 20.60 
                 0 
                 0 
               
               
                 C 
                 Case49 
                 51.20 
                 20.40 
                 1 
                 1 
               
               
                 C 
                 Case64 
                 20.40 
                 20.30 
                 0 
                 1 
               
               
                 B 
                 Case44 
                 36.40 
                 18.97 
                 0 
                 0 
               
               
                 C 
                 BP15 
                 19.77 
                 18.85 
                 0 
                 0 
               
               
                 C 
                 Case16 
                 22.80 
                 18.85 
                 1 
                 0 
               
               
                 C 
                 Casel8 
                 18.85 
                 18.80 
                 0 
                 1 
               
               
                 A 
                 Case38 
                 36.50 
                 18.60 
                 0 
                 0 
               
               
                 C 
                 Case29 
                 21.10 
                 18.20 
                 0 
                 0 
               
               
                 C 
                 Case43 
                 32.40 
                 17.20 
                 1 
                 0 
               
               
                 C 
                 Case04 
                 17.20 
                 17.20 
                 0 
                 0 
               
               
                 B 
                 Case63 
                 34.20 
                 16.80 
                 0 
                 1 
               
               
                 C 
                 Case61 
                 20.00 
                 16.60 
                 1 
                 0 
               
               
                 C 
                 Case28 
                 16.60 
                 16.50 
                 0 
                 0 
               
               
                 C 
                 BP08 
                 21.40 
                 16.40 
                 1 
                 1 
               
               
                 B 
                 Case16 
                 29.60 
                 16.03 
                 0 
                 0 
               
               
                 B 
                 BP24 
                 41.65 
                 16.00 
                 0 
                 0 
               
               
                 C 
                 BP06 
                 16.00 
                 15.50 
                 0 
                 0 
               
               
                 C 
                 BP01 
                 27.10 
                 15.30 
                 1 
                 1 
               
               
                 C 
                 Case71 
                 15.30 
                 15.00 
                 0 
                 0 
               
               
                 C 
                 Case85 
                 30.20 
                 13.70 
                 0 
                 0 
               
               
                 B 
                 Case37 
                 68.80 
                 12.90 
                 0 
                 0 
               
               
                 C 
                 BP02 
                 23.60 
                 12.90 
                 0 
                 0 
               
               
                 C 
                 Case30 
                 16.20 
                 12.90 
                 0 
                 0 
               
               
                 B 
                 Case68 
                 25.70 
                 11.60 
                 0 
                 0 
               
               
                 C 
                 Case46 
                 24.20 
                 11.60 
                 0 
                 0 
               
               
                   
               
               
                 *Final activity: 20-25%. Freedom from AF/after 3 and 6 months = 55% 
               
               
                 **Final activity: 25-25%. Freedom from AF after 12 months = 80% 
               
               
                 *Final activity: 10-20%. Freedom from AF/after 3 and 6 months = 77% 
               
               
                 **Final activity: 10-20%. Freedom from AF after 12 months = 95% 
               
            
           
         
       
     
       FIGS. 11( d ) through 11( f )  show EGF results obtained in selected patients from the EGF studies, where EGF techniques were employed to provide the displayed velocity vector maps. (Note that other types of vector maps may also be generated using EGF techniques.) 
       FIG. 11( d )  shows one example or embodiment of EGF results obtained in an “A-type” patient involved in the studies, where high maximum activity exceeding 25% and a steadiness value exceeding 10 E/s are detected for a single source. 
       FIG. 11( e )  shows one example or embodiment of EGF results obtained in an “B-type” patient involved in the studies, where high maximum activity exceeding 25% and a steadiness value exceeding 10 E/s are detected, but where multiple sources (not a single source, as in A-type patients) switch on and off. 
       FIG. 11( f )  shows one example or embodiment of EGF results obtained in an “C-type” patient involved in the studies, where low activity less than 25% and a steadiness value less than 10 E/s are detected, and where multiple sources exhibiting chaotic variability are detected. 
       FIG. 11( g )  summarizes EGF results and conclusions from the EGF studies; namely, that EGF analysis can be used to define the therapy a particular patient receives. A- and B-type patients will generally profit or benefit from targeted intra-cardiac ablations of stable atrial sources. C-type patients are generally cured by pulmonary vein isolation (PVI) only, and do not additionally profit or benefit from targeted ablation. This means that in comparison to invasive surgical procedures such as intra-cardiac EP mapping and catheter ablation, significantly less expensive and significantly less time-consuming extracorporeal methods can first be employed, where practicable, to prescreen AF patients. Such an extracorporeal pre-classification method (e.g., employing body surface electrodes in combination with EGF techniques, as described below, for example) can be used to funnel some patients towards no invasive treatment, others towards rapid PVI procedures using only single-shot devices, and still others towards full-scale basket catheter EGF-guided procedures. Such pre-screening extracorporeal methods can save time, money, and improve outcomes. 
     Additionally, and referring to  FIGS. 11( g ) and 11( h ) , comparative outcome data show that using EGF technology for therapy guidance improved TOPERA FIRM outcomes by about 25% (19% vs 81% treatment success of the AB1 patients who represent 40% of the 108 patient cohort). Still further, and as shown in  FIG. 11( h ) , a comparison of A/B-type patients to C-type patients shows that PVI alone can cure EGF C-type patients, while A/B-type patients are best treated with EGF-guided source ablation. Finally, in the validation studies, 25 A/B-type patients were found to have been treated with insufficient FIRM ablation (or 32% of the study population). These patients would very likely have benefited significantly from EGF-guided therapy. 
     Referring now to  FIGS. 12( a ) through 14( b ) , there are shown and illustrated various aspects of extracorporeal or body surface electrode EGF systems, devices, components, and methods, which may be employed, by way of non-limiting example, to pre-screen AF patients as described above, or as diagnostic tools for determining whether a patient has AF or AT before more complicated, involved, invasive, and/or time-consuming procedures might be employed (e.g., employing an intra-cardiac basket catheter to map a patient&#39;s atrium).. 
       FIGS. 12( a ) and 12( b )  illustrate two different embodiments of a combined extracorporeal body surface electrode EGF and/or cardiac electrophysiological mapping (EP), pacing and ablation system  100 . System  100  shown in  FIGS. 12( a ) and 12( b )  shares many aspects and features with system  100  shown in  FIG. 1( a ) , where certain portions thereof may be interchanged, such as, by way of example, intra-cardiac pacing or ablation catheter  110  with external extracorporeal electrode vest  420 , or may be removed, such as, by way of example, ablation module  150 , pacing module  160 , etc., depending of course on the particular application at hand. There is no need to repeat all the descriptions of the portions of systems  100  and  300  that are described above in connection with  FIGS. 1( a ) and 1( b ) , but that are shown in  FIGS. 12( a ) and 12( b ) . 
     In  FIGS. 12( a ) and 12( b )  there is shown a patient  5  wearing a body surface electrode vest  420  comprising a plurality of body surface electrodes  430 , which are operably connected through electrical connection  410  to multiplexer  146 , and thence to modules  140  and  300 . Body surface electrodes  430  are configured to sense ECGs or body surface electrogram signals originating from the patient&#39;s heart. Module  140  is configured to receive such ECGs or electrogram signals through electrical connection or cable  410 , and to condition such signals for further processing by computer  300 . In some embodiments, electrical connection or cable  410  is replaced by a wireless connections, such as BLUETOOTH® connection. 
     In  FIG. 12( a ) , there are shown  64  body surface electrodes  430  mounted on the anterior portion of vest  420 , which in turn is worn on or attached to the thorax of patient  5 . In some embodiments, another 64 body surface electrodes  430  may be mounted on the posterior surface of vest  420  (not shown in  FIG. 12( a ) ). 
     In  FIG. 12( b ) , there are shown  32  body surface electrodes  430  mounted on the anterior portion of vest  420 , which in turn is worn on or attached to the thorax of patient  5 . In some embodiments, another 32 body surface electrodes  430  may be mounted on a posterior surface of vest  420  (not shown in  FIG. 12( a ) ). 
     Continuing to refer to  FIGS. 12( a ) and 12( b ) , any suitable number of body surface electrodes  430  may be employed in system  100 . Generally the more body surface electrodes  430  employed the better so as to improve resolution and avoid, for example, spatial aliasing of electrical signals originating from patient&#39;s heart  10  arriving at the surface of the patient&#39;s thorax. Other numbers, arrangements, configurations, and types of body surface electrodes  430  are also contemplated, however. In some embodiments, at least some body surface electrodes  430  and vest  420  are together configured to detect cardiac activation or electrical signals, and to generate electrocardiograms or body surface electrogram signals, which are then relayed by electrical conductors in cable  410  from the individual electrodes  430  to data acquisition device  140 . 
     It is further contemplated that body surface electrodes  430  may be mounted, attached or coupled to the patient&#39;s thorax by means other than a vest, such as electrode strips, individually, or by other means known in the art. For example, electrode strips manufactured by Goltec GmbH of Cremlingen, Germany can be used. Carbon and metal body surface electrode strips are available from Goltec GmbH. Carbon electrode strips have the advantage of being radio-translucent, i.e., being transparent or substantially transparent during X-ray imaging. 
     Electrodes may be provided only on the anterior portion of the patient&#39;s thorax, only on the posterior portion of the patient&#39;s thorax, on side or lateral portions of the patient&#39;s thorax, or on any suitable combination of anterior, posterior and/or lateral portions of the patient&#39;s thorax. 
     Continuing to refer to  FIGS. 12( a ) and 12( b ) , and as mentioned above, electrodes  430  are configured to sense electrical activity originating in patient&#39;s heart  10 . In addition to sensing electrodes  430 , other types of devices and/or transducers, such as ground electrodes, navigation patches, position markers, or other devices may be configured to operate in conjunction with, be incorporated into, or form a portion of vest  420 , electrodes  430 , and/or system  10 . Electrodes  430  may be reusable or disposable, unipolar or bipolar, and may be configured for use with MRT/MRI, X-Ray, and/or CAT scanning imaging systems or other types of imaging systems  60 . Imaging and/or navigation system  60  may be employed used to help identify and determine the precise positions of the various electrodes  430  or position markers included in vest  430 . Gels, adhesives, and liquids may be employed to improve electrical coupling of electrodes  430  with the patient&#39;s body, as is well known in the art. 
     Note that in some embodiments, system  100  of  FIGS. 12( a ) and 12( b )  may not include multiplexer  146 , ablation module  150 , pacing module  160 , imaging and/or navigation system,  60 , or other modules or components shown in  FIGS. 12( a ) and 12( b ) . Among other things, the embodiments of system  100  shown in  FIGS. 12( a ) and 12( b )  are configured to detect and reconstruct cardiac activation information acquired from a patient&#39;s heart relating to cardiac rhythm disorders and/or irregularities, and is further configured to detect and discover the location of the source of such cardiac rhythm disorders and/or irregularities with enhanced precision relative to prior art techniques using body surface electrodes  430 . In some embodiments, system  100  is further configured to treat the location of the source of the cardiac rhythm disorder or irregularity, for example by ablating the patient&#39;s heart at the detected source location. 
     The embodiment of system  100  shown in  FIGS. 12( a ) and 12( b )  comprises five main functional units: electrophysiological mapping (EP mapping unit)  140  (which is also referred to herein as data acquisition device  140 ), ablation module  150 , pacing module  160 , imaging and/or navigation system  70 , and computer or computing device  300 . Data acquisition, processing and control system  15  comprises data acquisition device  140 , ablation module  150 , pacing module  160 , control interface  170  and computer or computing device  300 . In one embodiment, at least one computer or computing device or system  300  is employed to control the operation of one or more of systems, modules and devices  140 ,  150 ,  160 ,  170  and  70 . Alternatively, the respective operations of systems, modules or devices  140 ,  150 ,  160 ,  170  and  70  may be controlled separately by each of such systems, modules and devices, or by some combination of such systems, modules and devices. 
     Computer or computing device  300  may be configured to receive operator inputs from an input device  320  such as a keyboard, mouse and/or control panel. Outputs from computer  300  may be displayed on display or monitor  324  or other output devices (not shown in  FIGS. 12( a ) and 12( b ) ). Computer  300  may also be operably connected to a remote computer or analytic database or server  328 . At least each of components, devices, modules and systems  60 ,  110 ,  140 ,  146 ,  148 ,  150 ,  170 ,  300 ,  324  and  328  may be operably connected to other components or devices by wireless (e.g., BLUETOOTH) or wired means. Data may be transferred between components, devices, modules or systems through hardwiring, by wireless means, or by using portable memory devices such as USB memory sticks. 
     During body surface EP mapping or EGF analysis procedures, and as described above, body surface electrodes  430  are positioned on the thorax of patient  5 , and by way of example may be mounted on a vest  420  that is configured to place individual electrodes  430  in predetermined positions on the patient&#39;s body. These predetermined electrode positions can also be provided to imaging and/or navigation system  60  and/or to computer  300  as a data file so that the spatial positions of body surface electrodes  430  are known (at least approximately), and so that EGF analysis can be carried out accordingly as described above in connection with intra-cardiac EGF analysis (e.g., as described above in connection with  FIGS. 1( a ) through 10( d ) ). 
     When system  100  of  FIGS. 12( a ) and 12( b )  is operating in an EP mapping or EGF mode, body surface electrodes  430  function as detectors of electrocardiographic signals. In one embodiment, the analog signals obtained from body surface electrodes  430  are routed by multiplexer  146  to data acquisition device  140 , which comprises an amplifier  142  and an A/D converter (ADC)  144 . The amplified or conditioned electrogram signals may be displayed by electrocardiogram (ECG) monitor  148 . The analog signals are also digitized via ADC  144  and input into computer  300  for data processing, EGF analysis and graphical display (as described above). 
     Note that in some embodiments of system  100  shown in  FIGS. 12( a ) and 12( b ) , and as described above, multiplexer  146  may not form a portion of system  100 . In addition, in some embodiments computing device  300  may be combined or integrated with one or more of data acquisition device  140 , ablation module  150 , and/or pacing module  160 . 
     Referring now to  FIGS. 12( c ) and 12( d ) , here are shown respective anterior and posterior views of patient  5 &#39;s thorax with vest  420  worn on or attached thereto. Quadrants I, II, III, and IV are shown in each of  FIGS. 12( c ) and 12( d ) , which delineate quadrants into which electrodes  430  fall. Some quadrants or portions of such quadrants may be more useful or suitable than others for purposes of acquiring body surface electrogram signals of sufficient fidelity that correspond to the patient&#39;s right or left atria, for example, or to any other portion of the patient&#39;s heart which is desired to analyzed using EGF or EP mapping techniques. See, for example, “Detailed Anatomical and Electrophysiological Models of Human Atria and Torso for the Simulation of Atrial Activation” to Ferrer et al, PLOS One, Nov. 2, 2015, DOI:10.1371/journal.pone.0141573 (hereafter “the Ferrer reference”), the entire disclosure of which is hereby incorporated by reference herein pursuant to an Information Disclosure Statement filed on even date herewith containing a complete copy of such publication. See also “Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study” to Rodrigo et al.,  Heart Rhythm , September, 2014, 11(9): 1584-1591, doi: 10.1016/j.hrthm.2014.05.013 (hereafter “the Rodrigo reference”), the entire disclosure of which is hereby incorporated by reference herein pursuant to an Information Disclosure Statement filed on even date herewith containing a complete copy of such publication. 
     By way of non-limiting example, body surface electrodes  430  may be clustered or more densely positioned on those portions of a patient&#39;s thorax that are expected to yield, or that are measured to yield, the best or optimum fidelity body surface electrogram signals corresponding to signals originating in a patient&#39;s atrium, the two atria, or any other target portion of the patient&#39;s heart  10  that is to be analyzed. For example, body surface electrodes  430  can be concentrated on the anterior portion of the patient&#39;s thorax in the region where quadrants I, II, III and IV intersect near the middle of the thorax, or at a location slightly upwards from, or slightly upwards and to the left from, such intersection. Note that electrodes  430  can be arranged on the patient&#39;s thorax in any suitable pattern or configuration, including, but not limited to, an array of rows and columns (as shown in  FIGS. 12( a ) through 12( d ) ), a rectangular array, a square array, a circular or elliptical array, a cross-shaped array, a star-shaped array, and/or an array with varying electrode density or spacing where electrode spacing is finest in a region of maximum interest and least in a region of less interest. As body surface electrode data from a patient&#39;s thorax are analyzed using EGF techniques and/or machine learning techniques (more about which is said below), the locations of electrodes  430  on patient&#39;s thorax  5  may also be changed or adjusted so that body surface electrogram signals of the highest fidelity and/or lowest noise are acquired by system  100 . 
     In one embodiment, body surface electrogram signal data are processed by computer  300  to produce a display showing the location(s) of the source(s) of cardiac rhythm disorders and/or irregularities in the patient&#39;s heart  10  in real-time or near-real-time, further details of which are provided below. That is, at and between the sampled locations of the patient&#39;s endocardium, computer  300  may be configured to compute and display in real-time or near-real-time an estimated, detected and/or determined location(s) of the site(s), source(s) or origin)s) of the cardiac rhythm disorder(s) and/or irregularity(s) within the patient&#39;s heart  10 . This permits a medical practitioner to select interactively and quickly the electrodes  430  of vest  420  that are best detecting the location of the source(s) of the cardiac rhythm disorder or irregularity. Note that in some embodiments, EGF techniques are utilized to analyze body surface electrogram signal data without having to resort to complicated, lengthy and computationally-intensive tomographic or reverse modelling computations. In some applications, all that is required is to determine whether sources of AF and/or AT are present in a patient&#39;s atrium or atria, and whether those sources are stable or unstable. In other embodiments, and assuming sufficient computational power is available to process the acquired body surface electrogram signals, reverse modelling (e.g., “solving the reverse problem”), downward continuation, and/or employing tomographic techniques using a three-dimensional grid of voxels may be employed to yield higher fidelity and more accurate EGF results. 
     Referring once again to  FIGS. 12( a ) and 12( b ) , EP mapping system or data acquisition device  140  is configured to condition the analog body surface electrogram signals delivered by electrodes  430  to amplifier  142 . Conditioning of the analog body surface electrogram signals received by amplifier  142  may include, but is not limited to, low-pass filtering, high-pass filtering, bandpass filtering, and notch filtering. The conditioned analog signals are then digitized in analog-to-digital converter (ADC)  144 . ADC  144  may further include a digital signal processor (DSP) or other type of processor which is configure to further process the digitized electrogram signals (e.g., low-pass filter, high-pass filter, bandpass filter, notch filter, automatic gain control, amplitude adjustment or normalization, artifact removal, etc.) before they are transferred to computer or computing device  300  for further processing and analysis. 
     In some embodiments, the rate at which individual body surface electrogram and/or ECG signals are sampled and acquired by system  100  can range between about 0.25 milliseconds and about 8 milliseconds, and may be about 0.5 milliseconds, about 1 millisecond, about 2 milliseconds or about 4 milliseconds. Other sample rates are also contemplated. While in some embodiments system  100  is configured to provide unipolar signals, in other embodiments system  100  is configured to provide bipolar signals. 
     In one embodiment, system  100  can include a BARD® LABSYSTEM™ PRO EP Recording System, which is a computer and software driven data acquisition and analysis tool designed to facilitate the gathering, display, analysis, pacing, mapping, and storage of intracardiac EP data. Also in one embodiment, data acquisition device  140  can include a BARD® CLEARSIGN™ amplifier, which is configured to amplify and condition electrocardiographic signals of biologic origin and pressure transducer input, and transmit such information to a host computer (e.g., computer  300  or another computer). 
     Computing device or computer  300  is suitably configured and programmed to receive or access the body surface electrogram signals provided by body surface electrodes  430 . Computer  300  is further configured to analyze or process such electrogram signals in accordance with the methods, functions and logic disclosed and described herein so as to permit reconstruction of cardiac activation information from the electrogram signals using EGF techniques. This, in turn, makes it possible to locate with at least some reasonable degree of precision the location of the source of a heart rhythm disorder or irregularity. Once such a location has been discovered—or not discovered—the characteristics of the source(s) may be analyzed and the therapy, if any, that is to be delivered to the patient may be determined. 
     In one embodiment, and as shown in  FIGS. 12( a ) and 12( b ) , system  100  may also comprise a physical imaging and/or navigation system  70 . Physical imaging and/or navigation device  60  included in system  70  may be, by way of example, a 2- or 3-axis fluoroscope system, an ultrasonic system, a magnetic resonance imaging (MRI) system, a computed tomography (CT) imaging system, and/or an electrical impedance tomography EIT) system. Operation of system  70  be controlled by computer  300  via control interface  170 , or by other control means incorporated into or operably connected to imaging or navigation system  70 . In one embodiment, computer  300  or another computer triggers physical imaging or navigation system  60  to take “snap-shot” pictures of the heart  10  of a patient (body not shown). A picture image is detected by a detector  62  along each axis of imaging, and can include a silhouette of the heart as well as a display of the positions of body surface electrodes  430  on the patient&#39;s thorax (more about which is said below), which is displayed on imaging or navigation display  64 . Digitized image or navigation data may be provided to computer  300  for processing and integration into computer graphics that are subsequently displayed on monitor or display  64  and/or  324 . 
     Medical navigation systems suitable for use in the various embodiments described and disclosed herein include, but are not limited to, image-based navigation systems, model-based navigation systems, optical navigation systems, electromagnetic navigation systems (e.g., BIOSENSE® WEBSTER® CARTO® system), and impedance-based navigation systems (e.g., the St. Jude® ENSITE™ VELOCITY™ cardiac mapping system), and systems that combine attributes from different types of imaging AND navigation systems and devices to provide navigation within the human body (e.g., the MEDTRONIC® STEALTHSTATION® system). 
     Referring now to  FIG. 13 , there is shown one embodiment of a generalized method  500  of employing EGF techniques in conjunction with body surface electrodes  430  to permit patients to be classified as A-type, B-type or C-type patients, which method comports with the EGF patient classification discussion set forth above. At step  510 , EGF analysis is performed on body surface electrode data that have been acquired from a patient using, by way of non-limiting example, system  100  of  FIGS. 12( a )  or  12 ( b ). EGF analysis can be carried out in accordance with the teachings set forth above that relate to, or are described in connection with,  FIGS. 1 through 11 ( a ). In step  520 , it is determined whether the patient is an A-type patient, a B-type patient, or a C-type patient, using EGF techniques (such as those described above in connection with  FIGS. 11( b ) through 11( h ) ). If the patient is determined to be an A- or B-type patient, intra-cardiac ablation of the detected source(s) can be carried out at step  540  with the benefit of the EGF techniques described in detail above. If the patient is determined to be a C-type patient at step  520 , no intra-cardiac ablation is carried out at step  530 , although PVI for that patient may be well advised depending on the EGF results. After having read and understood the present specification and drawings, those skilled in the art will understand that many variations, combinations, and permutations of method  500  are possible. 
       FIGS. 14( a ) and 14( b )  show examples of EGF analysis carried out using body surface electrodes and EGF techniques. In  FIG. 14( a ) , simulated electrical potential wave propagation from the heart to and across body surface electrodes is illustrated for calculated potential distributions occurring as a normal sinus rhythm P-wave propagates from its source outwardly to 30 msec., 60 msec., 90 msec., and 100 msec. EGF results obtained with surface body electrodes are shown in the upper right central portion of  FIG. 14( a ) , where no AF source is indicated or detected. In  FIG. 14( b ) , simulated electrical potential wave propagation from the heart to and across body surface electrodes is illustrated for calculated potential distributions occurring in a patient with AF. EGF results obtained from simulated body surface electrodes are shown in the four EGF panels of  FIG. 14( b ) , where an AF source is clearly indicated and has been detected.  FIGS. 14( a ) and 14( b )  show body surface potential distributions sharing some similarities to those discussed above in the Ferrer and Rodrigo references. 
     With reference to the foregoing discussion and the Figures relating thereto (i.e.,  FIGS. 1( a ) through 14( b ) , and also with further reference to  FIGS. 15 through 18  and the discussions appearing below in connection therewith, the following additional embodiments, features, and aspects are also contemplated. 
     In one embodiment, there is provided a system configured to detect at least one location of at least one source of at least one cardiac rhythm disorder in a patient&#39;s heart, the system comprising: (a) at least one computing device; (b) at least one data acquisition device operably connected to the at least one computing device or configured to provide as outputs therefrom body surface electrogram signals; (c) a plurality of body surface electrodes configured to generate body surface electrogram signals and for placement on the patient&#39;s body surface, the plurality of body surface electrodes being operably connected to the at least one data acquisition device, and (d) a display or monitor operably connected to the at least one computing device and configured to visually display to the user one or more vector maps generated by the at least one computing device; wherein the computing device comprises at least one non-transitory computer readable medium configured to store instructions executable by at least one processor to determine the at least one location of the at least one source of the at least one cardiac rhythm disorder in the patient&#39;s heart, the computing device being configured to: (i) receive the body surface electrogram signals from the plurality of body surface electrodes located on the patient&#39;s body, where amplitudes of the body surface electrogram signals received by the at least one computing device have been at least one of conditioned, amplified, normalized, filtered, and adjusted by the data acquisition device before being provided to the computing device; (ii) assign or relate positional data corresponding to predetermined positions of the body surface electrodes on the patient&#39;s body to their respective corresponding body surface electrogram signals and body surface electrodes; (iii) generate at least one spatial map of the body surface electrode positions; (iv) for each or selected discrete times over which the body surface electrogram signals are being processed, process the amplitude-adjusted body surface electrogram signals to generate a plurality of electrogram surfaces or data grids, each such surface or data grid corresponding at least partially to the at least one spatial map, at least one surface or data grid being generated for each such time, and (v) process the plurality of electrogram surfaces or data grids through time to generate at least one vector map corresponding at least partially to the spatial map, the at least one vector map being configured to reveal the at least one location of the at least one source of the at least one cardiac rhythm disorder, the at least one vector map being shown to the user on the display or monitor. 
     The foregoing embodiment can further comprise: (1) the plurality of electrogram surfaces being a plurality of three-dimensional electrogram surfaces that includes a first three-dimensional electrogram surface corresponding to a first EGF recording of a first duration of time and a second three-dimensional electrogram surface corresponding to a second EGF recording of a second duration of time, the second duration of time being greater than the first duration of time; (2) the first and second three-dimensional electrogram surfaces facilitating a determination of whether the patient&#39;s AF revealed in the velocity vector or EGF map is characterized by one or more of: (a) atrial behavior exhibiting spatially and temporally stable rotors, drivers or sources (Type A); (b) atrial behavior where spatially stable rotors switch on and off (Type B), and (c) chaotic atrial behavior in which the rotors are spatially and temporally variable (Type C); (3) the first duration of time ranging between about 0.5 seconds and about 30 seconds, between about 1 second and about 5 seconds, or between about 1 second and about three seconds; (4) sources being detected over a duration of between about 0.5 seconds and about 30 seconds, or between about 1 second and about 15 seconds; (5) the second duration of time ranging between about 1 minute and about 3 minutes, between about 30 seconds and about 10 minutes, or between about 15 seconds and about 20 minutes; (6) the vector map being a velocity vector map; (7) at least one of an activity value, a flow angle stability value, and a steadiness value being generated by the computing device for one or more sources corresponding to the at least one cardiac rhythm disorder revealed in the vector map; (8) at least one of the activity value, the flow angle stability value, and the steadiness value being displayed on the display or monitor; (9) on the basis of at least one of the generated activity values, flow angle stability values, and steadiness values the computing device determining whether to classify the patient as an A-type patient, a B-type patient, or a C-type patient; (10) the computing device being configured to determine whether the at least one velocity vector map corresponds to an A-type patient, a B-type patient, or a C-type patient; (11) the electrogram surfaces or data grids comprising at least one three-dimensional surface; (12) the electrogram surfaces or data grids being generated by the computing device using Green&#39;s function; (13) the vector map generated by the computing device being configured to reveal a location in the patient&#39;s heart of one or more of: (a) an active rotor; (b) a passive rotor, (c) a breakthrough point, and (d) a focal point; (14) the velocity vector map being generated by the computing device using at least one optical flow analysis technique; (15) the at least one optical flow analysis technique being selected from the group consisting of a Horn-Schunck method, a Buxton-Buston method, a Black-Jepson method, a phase correlation method, a block-based method, a discrete optimization method, a Lucas-Kanade method, and a differential method of estimating optical flow; (16) the at least one processor and the at least one non-transitory computer readable medium being configured to determine, using a trained atrial discriminative machine learning model, predictions or results concerning atrial fibrillation in the patient&#39;s heart; (17) the trained atrial discriminative machine learning model having been trained at least partially using data obtained from a plurality of other previous patients, where intracardiac electrophysiological (EP) mapping signals for the other patients have been processed using electrographic flow (EGF) methods to detect at least one of: (I) the presence of sources of atrial fibrillation in the other patients&#39; hearts; (II) the locations of sources of atrial fibrillation in the other patients&#39; hearts; (III) the activity levels of sources of atrial fibrillation in the other patients&#39; hearts; (IV) the spatial variability levels of sources of atrial fibrillation in the other patients&#39; hearts; (V) the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts; and (VI) the classification of patients as at least one of types A, B and C; (18) paired data sets of body surface electrogram signals and the intracardiac EP mapping signals having been acquired simultaneously from at least some of the plurality of other patients and the paired data sets have been correlated to one another using the trained atrial discriminative machine model; (19) the trained atrial discriminative machine learning model being further configured to generate one or more of the following predictions or results for the patient using the conditioned electrogram signals and positional data corresponding to the patient: (1) Does the patient have atrial fibrillation or not? (2) If the patient has atrial fibrillation, determining at least one of the spatial variability level, the activity level, and the flow angle stability level associated with one or more sources detected in the patient&#39;s heart; (3) If the patient has atrial fibrillation, determining the locations of one or more sources detected in the patient&#39;s heart; (4) If the patient has atrial fibrillation, whether one or more activation sources detected in the patient&#39;s heart are characterized by chaotic flow; and (5) classification of the patient as one of types A, B and C, and (20) the computing device being further configured to: (iv) process the conditioned electrogram data and positional data in the trained machine learning model to generate the one or more predictions or results; and (v) display the one or more predictions or results on the display or monitor to the user. 
     In another embodiment, there is provided a method of detecting at least one location of at least one source of at least one cardiac rhythm disorder in a patient&#39;s heart using a system comprising at least one computing device, the computing device comprising at least one non-transitory computer readable medium configured to store instructions executable by at least one processor to determine the at least one location of the at least one source of the at least one cardiac rhythm disorder in the patient&#39;s heart, the system further comprising a plurality of body surface electrodes operably connected to the computing device through a data acquisition device and a monitor or screen operably connected to the computing device, the method comprising: (a) acquiring body surface electrogram signals using the body surface electrodes located on one or more body surfaces of the patient; (b) using at least one of the computing device and the data acquisition device, at least one of conditioning, filtering, normalizing and adjusting the amplitudes of the acquired body surface electrogram signals; (c) using the computing device, assigning positions or identifiers for each of the body surface electrodes to corresponding individual body surface electrogram signals; (d) using the computing device and the assigned positions or identifiers, providing or generating a spatial map of the body surface electrode positions; (e) using the computing device, for each or selected discrete times over which the body surface electrogram signals are being processed, processing the amplitude-adjusted body surface electrogram signals to generate a plurality of electrogram surfaces or data grids corresponding at least partially to the spatial map, one surface or data grid being generated for each such time, and (f) using the computing device, processing the plurality of electrogram surfaces or data grids through time to generate a vector map corresponding at least partially to the spatial map, the vector map being configured to reveal on the monitor or display to a user the at least one location of the at least one source of the at least one cardiac rhythm disorder. 
     The foregoing embodiment can further comprise: (1) conditioning the electrogram signals further comprises one or more of amplifying the electrogram signals, notch filtering the electrogram signals, and bandpass, low-pass or high-pass filtering the body surface electrogram signals; (2) generating the electrogram surfaces or data grids using Green&#39;s function; (3) showing the at least one cardiac rhythm disorder as an active rotor, a passive rotor, a breakthrough point, or a focal point on the vector map; (4) generating the vector map using at least one optical flow analysis technique; (5) the at least one optical flow analysis technique being selected from the group consisting of a Horn-Schunck method, a Buxton-Buston method, a Black-Jepson method, a phase correlation method, a block-based method, a discrete optimization method, a Lucas-Kanade method, and a differential method of estimating optical flow, and (6) the method further  15   s  comprising detecting at least one property or characteristic of the at least one source of at least one cardiac rhythm disorder in a patient&#39;s heart using the system, where the property or characteristic is, by way of for example, a location, an activity level, a steadiness level, a flow angle stability level, or other property or characteristic disclosed or described herein. 
       FIG. 15  shows some benefits accruing to one embodiment of an ABLACON EGF analysis system  700 . As shown in the embodiment of  FIG. 15 , system  700  comprises patient  715 , ABLAWATCH app  713 , general practitioner  701 , ABLAMAP amplifiers, catheters, and navigation systems  703 , EP-Lab  705 , ABLACLOUD deep learning  711 , ABLAMAP body surface system  707 , and cardiologist  709 . Benefits  720  (“Keep patient safe and informed”),  730  (“Evaluate stroke risk, therapy success forecast, and AF burden (outcome)”),  740  (“Hit extra PV targets”), and  750  (“Avoid needless ablations”) arise from use of EGF technology when employed in the integrated and holistic manner illustrated in  FIG. 15 . In system  700 , one or more central servers and the internet can be employed to collect, analyze and disseminate or distribute the various types of information shown in  FIG. 15 . See, for example, “Association of Atrial Fibrillation Clinical Phenotypes With Treatment Patterns and Outcomes: A Multicenter Registry Study” to Taku Inohara et al., JAMA Cardiol. 2018; 3(1):54-63, the entirety of which is hereby incorporated by reference herein. Embodiments of system  700  other than that shown explicitly in  FIG. 15  are also contemplated, such as systems  700  have more or fewer elements than those shown in  FIG. 15 . 
       FIG. 16  shows one embodiment of an ABLACON diagnosis and treatment system  800 , which mirrors at least portions of system  700  shown in  FIG. 15 . System  800  illustrated in  FIG. 16  denotes specific tasks that are carried out in, or results that are provided by, System  700  of  FIG. 15  (e.g., history, HR derived statistics, HR correlated with sleep activity, etc.). The holistic and integrated systems illustrated in  FIGS. 15 and 16  permit the centralized acquisition, storage, processing, and dissemination of remote information gleaned from hundreds or thousands of patients from around the world. Systems  700  and  800  are configured to permit continuous updating of EGF data and results based on anonymized patient data, and associated deep learning of the system so that EGF results can be improved and made more accurate over time. In addition, EGF results and/or raw or partially processed electrogram signal data from a particular patient can be uploaded to systems  700  and/or  800  for comparison to other data, or for the generation of a diagnosis of the data and/or therapy treatment recommendations or suggestions. 
     Referring now to  FIGS. 17 and 18 , there are shown various aspects, features and components according to some embodiments of machine learning systems, devices, components, and methods that can be employed to leverage and enhance the benefits and advantages of the body surface electrode and EGF techniques and processes described above so that the natures and types of cardiac rhythm disorders characteristic of patients&#39; hearts can more be more efficiently, accurately, quickly, and cost-effectively detected and diagnosed. In one such embodiment, where a trained atrial discriminative machine learning system is employed, body surface electrode data acquired from a patient serve as the inputs to the trained atrial discriminative machine learning system, which then provides as outputs predicted EGF flow fields or associated characteristics for the patient&#39;s heart and/or a predicted ABC classification of the patient&#39;s heart disorders, where such predicted outputs are based upon the body surface electrode and EGF techniques and processes described above in combination with machine learning techniques, as well as the use of intra-cardiac electrode data (more about which is said below). 
       FIG. 17  shows one embodiment of a simplified machine learning system  900  and a corresponding generalized machine learning workflow. Training data for system  900  initially comprises paired sets of EGF or optical flow results obtained from intra-cardiac electrode data and body surface electrode data, where such paired sets of data are acquired and recorded from the same patients at the same time. System  900  is first trained using a sufficiently large number of such sets of paired data until system  900  is capable of providing accurate optical flow predictions and/or ABC or other suitable patient classifications based upon new body surface electrode data from new patients that are provided later on as input data to system  900 . System  900  is preferably configured so that it is continuously and/or periodically updated with new training data. Some generalized types of machine learning methods that can be employed in system  900  include, by way of illustrative but non-limiting example, supervised (e.g., classification or regression) methods, unsupervised (e.g., clustering and estimation of probability density function) methods, and semi-supervised learning (e.g., text/image retrieval system) methods. 
     Now described are more specific examples of machine learning systems, methods applied to the problem of accurately predicting the presence or classification of cardiac rhythm disorders in a patient&#39;s heart based upon input surface body electrode data only (in combination with a trained atrial discriminative machine learning model). 
     With reference now to  FIG. 18 , in one embodiment a body surface and intra-cardiac electrode machine learning system and its various components and associated methods are employed to facilitate screening of patients for atrial fibrillation. The system is configured to determine, using only non-invasive body surface electrode data, how best to treat a patient in any subsequent procedures that might be carried out, such as acquiring and analyzing intra-cardiac EP data using a basket catheter, atrial ablation, or pulmonary vein isolation. Such systems and methods may also be employed to determine whether a particular patient is a good or poor candidate for undergoing a further intra-cardiac (e.g., atrial) electrophysiological (EP) mapping diagnostic procedure and/or for an intra-cardiac atrial ablation or PVI procedure. In some embodiments, such a machine learning system and its various components and associated methods are can be used to classify patients potentially suffering from AF as A-, B-, or C-type patients. 
     In accordance with the teachings and disclosures set forth above and in the Figures, patients who are determined to be C-type patients using the body surface and intracardiac electrode machine learning system described and disclosed herein can be spared the trouble, risk, cost and expense associated with undergoing very likely unnecessary but expensive intra-cardiac EP mapping and/or ablation or PVI procedures that would be unlikely to do anything to improve their state of health. Further in accordance with the teachings and disclosures set forth above and in the Figures, and in contrast, patients who are determined to be A- or B-type patients using the body surface and intracardiac electrode machine learning system described and disclosed herein would be good candidates for intra-cardiac EP mapping and/or ablation or PVI procedures that are very likely to provide them with beneficial results. 
       FIG. 18  shows a block diagram and data flow diagram according to one embodiment of a body surface and intra-cardiac electrode machine learning system, which employs an atrial discriminative training (ADT) machine learning model (or MLM) that works in combination with a loss or cost function module (or LM). The ADT MLM is configured to provide its results or predictions to the LM, and in turn the LM is configured to provide outputs based on the ADT MLM&#39;s results or predictions back to the ADT MLM (more about which is said below). The ADT MLM can be any suitable type of machine learning module or network, such as one or more of the following types of networks or modules: convolutional neural network (CNN), decision tree, support vector machine, logistic regression, mixture of Gaussian, a feedforward neural network or artificial neuron network, a radial basis function neural network, a Kohonen self-organizing neural network, a recurrent neural network (RNN) or long short term memory network, and/or a modular neural network. The ADT MLM and/or the LM can also be configured to employ optimization techniques or schemes such as stochastic gradient descent schemes or decision tree schemes. 
     Continuing to refer to  FIG. 18 , in one embodiment of a body surface and intracardiac electrode machine learning system and its various components and associated methods, a goal is to estimate the properties of EGF using body surface electrodes only. EGF can help identify action potential flow patterns for improved understanding and treatment of AF. In some of the embodiments described above, EGF is estimated using intracardiac unipolar electrodes, which is costly and invasive. By using and leveraging body surface electrode data in combination with intra-cardiac electrode data and EGF results obtained therewith, initial patient evaluation or classification, PVI outcome prediction, coarse or general localization of AF drivers, and more precise follow-up quantification (e.g., “recurrence of AF likely to occur, yes or no) can be carried out at much lower cost, and with essentially no risk to the patient, as compared to conventional invasive intra-cardiac basket catheter EP mapping methods. 
     Still continuing to refer to  FIG. 18 , in one embodiment an atrial discriminative trained (ADT) machine learning model is trained to estimate the correlation of body surface electrode signals to EGF properties and characteristics derived from intracardiac electrodes. By way of non-limiting example, such a machine learning model can be a feature extraction method that employs wavelet decomposition and amplitude histograms in combination with a regression or classification model such as a support vector machine, a decision tree or logistic regression, an end-to-end model such as a deep neural network, and/or any combination or permutation of the foregoing. 
     As shown in  FIG. 18 , an input signal x to the machine learning model ADT comprises data recorded from body surface electrodes BS (x). In some embodiments, signal x potentially undergoes preprocessing steps, such as a high/low/band-pass filtering or for the compensation of ventricular artifacts (e.g., removing the QRST complex). The desired output(s) y of the machine learning model provides an estimate(s) for one or more properties of EGF in a patient&#39;s heart, such as activity or flow angle stability. Training data (x, y) is obtained from simultaneous recordings from body surface electrodes (x) and intracardiac electrodes (y). In one embodiment, the machine learning model is parametrized with parameters W. These can be weights of neural network connections, etc. 
     The prediction of the machine learning model is then ŷ=fW(x) (or “f of x, parametrized by W’). This prediction should be as close as possible to y. During training, parameters Ware optimized so as to minimize the error in estimating y. Such an error can be described as a loss function L(y, ŷ), for example the modulus of the difference L(y, ŷ)=∥y−ŷ∥. Which is carried out in block LM, as described above. 
     Since the target values y are derived from intracardiac electrodes located in only one atrium, but body surface electrodes pick up signals x from both atria, a mechanism is needed to compensate for this. The idea behind atrial discriminative training (ADT) is to ask the machine learning model to make two predictions: fW(x)=(ŷL, ŷR), one for each atrium. However, the ground truth y from the intracardiac recording is only known for one atrium (y=yL or y=yR). The cost function therefore separates the two predictions into L(y, ŷ)=αL(yL, ŷL)+βL(yR, ŷR) and the coefficients are set according to the locations of the intracardiac recordings: (α, β)=(1, 0) if y=yL and (α, β)=(0, 1) if y=yR. 
     As a result, at training time, the machine learning model does not know which atrium it is supposed to predict, and predictions for the atrium that was not measured intracardially are not penalized. At test time, the machine learning model will be able to make predictions for both atria simultaneously. Consequently, the foregoing systems and methods can be used to localize and quantify drivers of AF. 
     It will now be seen that an ADT MLM can be trained to directly predict optical flow from two different input images, which correspond to: (a) optical flow images derived from intra-cardiac EP data; and (b) optical flow images derived from body surface EP data acquired at the same time and from the same patients. Training data need not be perfect or noise free. 
     After having read and understood the present specification, drawings and claims, those skilled in the art will now understand that configurations and architectures of MLMs other than those explicitly described and disclosed herein can also be used obtain similarly useful results. Moreover, and with reference to  FIGS. 15, 16, and 17 , the ADT MLM used to provide optical flow predictions from body surface electrode data can be continuously or periodically updated using intra-cardiac data and/or body surface electrode data, as well as ABC patient classification data, that are or have been obtained from patients. See, for example, Deep Learning blocks  711  and  811  in  FIGS. 15 and 16 . Whether located in a remote “cloud” server, a health care facility, or another type of location, machine learning training, updating, computations, and analyses can be are carried out using hardware and computer systems  300  similar those shown in  FIGS. 1( a ) and 1( b ) , or using other individual, portable, stationary, conventional, network-based, and/or cloud-based hardware and computer systems, as those skilled in the art will understand. 
     With respect to the foregoing atrial discriminative machine learning models, and the systems, devices, components, and methods associated therewith, the following additional embodiments, features, and aspects are also contemplated. 
     In one embodiment, there is provided a system configured to determine and display to a user one or more predictions or results concerning atrial fibrillation in a patient&#39;s heart, the system comprising: (a) at least one computing device; (b) at least one data acquisition device operably connected to the at least one computing device or configured to provide as outputs therefrom body surface electrogram signals; (c) a plurality of body surface electrodes configured to generate body surface electrogram signals and for placement on the patient&#39;s body surface, the plurality of body surface electrodes being operably connected to the at least one data acquisition device, and (d) a display or monitor operably connected to the at least one computing device and configured to visually display to the user the predictions or results concerning the atrial fibrillation generated by the at least one computing device; wherein the computing device comprises at least one processor and at least one non-transitory computer readable medium configured to store instructions executable by the at least one processor to determine, using a trained atrial discriminative machine learning model, the predictions or results concerning atrial fibrillation in the patient&#39;s heart, the computing device being configured to: (i) receive the body surface electrogram signals from the plurality of body surface electrodes located on the patient&#39;s body, where the body surface electrogram signals received by the at least one computing device have been at least one of conditioned, amplified, normalized, filtered, and adjusted by the data acquisition device before being provided to the computing device as conditioned electrogram signals; (ii) assign or relate positional data corresponding to positions or estimated positions of the body surface electrodes on the patient&#39;s body to their respective corresponding body surface electrogram signals and body surface electrodes; (iii) input the conditioned electrogram signals and positional data into the trained atrial discriminative machine learning model, where the trained atrial discriminative machine learning model has been trained at least partially using data obtained from a plurality of other previous patients, where intracardiac electrophysiological (EP) mapping signals for the other patients have been processed using electrographic flow (EGF) methods to detect at least one of: (I) the presence of sources of atrial fibrillation in the other patients&#39; hearts; (II) the locations of sources of atrial fibrillation in the other patients&#39; hearts; (III) the activity levels of sources of atrial fibrillation in the other patients&#39; hearts; (IV) the spatial variability levels of sources of atrial fibrillation in the other patients&#39; hearts; (V) the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts; and (VI) the classification of patients as at least one of types A, B and C; where paired data sets of body surface electrogram signals and the intracardiac EP mapping signals have been acquired simultaneously from at least some of the plurality of other patients and the paired data sets have been correlated to one another using the atrial discriminative trained machine model, and the trained atrial discriminative machine learning model is further configured to generate one or more of the following predictions or results for the patient using the conditioned electrogram signals and positional data corresponding to the patient: (1) Does the patient have atrial fibrillation or not? (2) If the patient has atrial fibrillation, determining at least one of the spatial variability level, the activity level, and the flow angle stability level associated with one or more sources detected in the patient&#39;s heart; (3) If the patient has atrial fibrillation, determining the locations of one or more sources detected in the patient&#39;s heart; (4) If the patient has atrial fibrillation, whether one or more activation sources detected in the patient&#39;s heart are characterized by chaotic flow; and (5) classification of the patient as one of types A, B and C; and further wherein the computing device is configured to: (iv) process the conditioned electrogram data and positional data in the trained machine learning model to generate the one or more predictions or results; and (v) display the one or more predictions or results on the display or monitor to the user. 
     The foregoing embodiment can further comprise: (1) a trained atrial discriminative machine learning model configured to provide the results or predictions therefrom to a loss function module; (2) a loss function module configured to provide outputs based on the results or predictions provided by the trained atrial discriminative machine learning model back to the trained atrial discriminative machine learning model to facilitate optimizing results or predictions subsequently provided thereby; (3) predictions or results generated by an atrial discriminative machine learning model comprising one or more of: (a) the patient has no detectable atrial fibrillation at the present time; (b) the patient has a type of atrial fibrillation having a substantial probability of being treated successfully with pulmonary vein isolation alone; (c) the patient has a type of atrial fibrillation that has a substantial probability of being treated successfully only with atrial ablation or with atrial ablation in combination with pulmonary vein isolation; and (d) providing an estimate of the probability of recurrence of atrial fibrillation in the patient; (4) predictions or results generated by the atrial discriminative machine learning model comprising one or more of: (a) the estimated locations of one or more sources or rotors in the patient&#39;s heart; (b) whether one or more sources or rotors in the patient&#39;s heart are located in a right atrium or a left atrium of the patient; (c) at least one type of source or rotor in the patient&#39;s heart, including active rotors or sources, passive rotors or sources, focal points, breakthrough points, and chaotic rotors or sources; (d) activity levels of one or more sources or rotors in the patient&#39;s heart; (d) spatial variability levels of one or more sources or rotors in the patient&#39;s heart, and (e) flow angle stabilities of one or more sources or rotors in the patient&#39;s heart; (5) a trained atrial discriminative machine learning model being updated at least partially using data obtained from a plurality of new patients, where paired data sets of body surface electrogram signals and intracardiac electrophysiological (EP) mapping signals have been acquired simultaneously from each of the plurality of new patients, and the paired data sets have been correlated to one another using machine learning; (6) a trained atrial discriminative machine learning model is updated or trained at least partially using MRI data obtained from a plurality of new patients, where areas or regions of fibrosis in the plurality of new patients have been identified in the MRI data and are correlated using machine learning to one or more of the body surface electrogram signals and the EP mapping signals; (7) a trained atrial discriminative machine learning model is updated or trained at least partially using one or more of body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients, where a first portion of the new patients have no atrial fibrillation and are identified in the data as being atrial-fibrillation-free, and a second portion of the new patients have atrial fibrillation and are identified in the data as having atrial fibrillation, and the paired data sets have been correlated to one another using machine learning; (8) a trained atrial discriminative machine learning model being updated or trained at least partially using body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients determined to have atrial fibrillation prior to being treated by intra-cardiac ablation, where body surface electrogram data and intracardiac EP mapping signals are obtained from the plurality of new patients before and after atrial ablation procedures are performed in at least one atrium of each such new patient, and the resulting body surface electrogram data and intracardiac EP mapping signals have been correlated to one another using machine learning; (9) a trained atrial discriminative machine learning model being updated or trained at least partially using body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients determined to have had atrial fibrillation previously, who were treated with a pulmonary vein isolation procedure previously, and who have been atrial-fibrillation-free for at least one year after being treated by the pulmonary vein isolation procedure, where body surface electrogram data and intracardiac EP mapping signals have been obtained from the plurality of new patients before and one year after the pulmonary vein isolation procedures, and the resulting body surface electrogram data and intracardiac EP mapping signals have been correlated to one another using machine learning; (10) a trained atrial discriminative machine learning model being updated or trained at least partially using simulated body surface electrograms generated using a heart and torso model having one or more known origins of rotors or sources in one or more atria thereof; (11) a trained atrial discriminative machine learning model being updated or trained at least partially using data from a plurality of patients, where the data from the patients relate to one or more of atrial volume, atrial dimensions, patient age, patient weight, patient height, and patient body mass index; (12) a trained atrial discriminative machine learning model comprising one or more of: (a) a neural network, (b) a generative neural network; (c) a recurrent neural network, and (d) a feed-forward neural network; (13) a trained atrial discriminative machine learning model comprising a recurrent neural network employing long short-term memory (LSTM); (14) a trained atrial discriminative machine learning model comprising one or more of: (a) a nearest neighbor model; (b) a naive Bayes model; (c) a decision tree model; (d) a linear regression model; (e) a support vector machine (SVM) model, and (f) a neural network; (15) a trained atrial discriminative machine learning model being generated at least partially using supervised learning or unsupervised learning; (16) a trained atrial discriminative machine learning model comprising convolutional layers; (17) conditioned electrogram signals being processed by the at least one computing device to remove or substantially remove at least portions of the QRS or QRST complex from at least some of the electrogram signals; (18) Green&#39;s function being employed in the EGF methods; and (19) EGF methods including at least one optical flow analysis technique selected from the group consisting of a Horn-Schunck method, a Buxton-Buston method, a Black-Jepson method, a phase correlation method, a block-based method, a discrete optimization method, a Lucas-Kanade method, and a differential method of estimating optical flow. 
     In another embodiment, there is provided a method of determining and displaying to a user one or more predictions or results concerning atrial fibrillation in a patient&#39;s heart using a system comprising at least one computing device, at least one data acquisition device operably connected to the at least one computing device or configured to provide as outputs therefrom body surface electrogram signals, a plurality of body surface electrodes configured to generate body surface electrogram signals and for placement on the patient&#39;s body surface, the plurality of body surface electrodes being operably connected to the at least one data acquisition device, and a display or monitor operably connected to the at least one computing device and configured to visually display to the user the predictions or results concerning the atrial fibrillation generated by the at least one computing device, the computing device comprising at least one processor and at least one non-transitory computer readable medium configured to store instructions executable by the at least one processor to determine, using a trained atrial discriminative machine learning model, the predictions or results concerning atrial fibrillation in the patient&#39;s heart, the method comprising: (a) acquiring body surface electrogram signals using the body surface electrodes located on one or more body surfaces of the patient; (b) using at least one of the computing device and the data acquisition device, at least one of conditioning, filtering, normalizing and adjusting the amplitudes of the acquired body surface electrogram signals; (c) using the computing device, assigning positions or identifiers for each of the body surface electrodes to corresponding individual body surface electrogram signals; (d) using the computing device and the assigned positions or identifiers, providing or generating a 2D or 3D spatial map of the body surface electrode positions; (e) using the computing device and the trained atrial discriminative machine learning model, inputting the conditioned electrogram signals and positional data into the trained atrial discriminative machine learning model, where the trained atrial discriminative machine learning model has been trained at least partially using data obtained from a plurality of other previous patients, intracardiac electrophysiological (EP) mapping signals for the other patients have been processed using electrographic flow (EGF) methods to detect at least one of the presence of sources of atrial fibrillation in the other patients&#39; hearts, the locations of sources of atrial fibrillation in the other patients&#39; hearts, the activity levels of sources of atrial fibrillation in the other patients&#39; hearts, the spatial variability levels of sources of atrial fibrillation in the other patients&#39; hearts, the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts, and the classification of patients as at least one of types A, B and C, where paired data sets of body surface electrogram signals and intracardiac electrophysiological (EP) mapping signals have been acquired simultaneously from at least some of the plurality of other patients and the paired data sets have been correlated to one another using machine learning, and the trained atrial discriminative machine learning model is to generate one or more of the following predictions or results: (1) Does the patient have atrial fibrillation or not? (2) If the patient has atrial fibrillation, determining at least one of the spatial variability level, the flow angle stability level, and the activity level associated with one or more sources detected in the patient&#39;s heart; (3) If the patient has atrial fibrillation, determining the locations of one or more sources detected in the patient&#39;s heart; (4) If the patient has atrial fibrillation, whether one or more sources detected in the patient&#39;s heart are characterized by chaotic behavior (f) processing the conditioned electrogram data and positional data in the trained machine learning model to generate the one or more predictions or results; and 
     (g) displaying the one or more predictions or results on the display or monitor to the user. 
     The foregoing embodiment can further comprise: (1) a trained machine learning model having been trained at least partially using data obtained from the plurality of other previous patients, where intracardiac electrophysiological (EP) mapping signals for the other patients have been processed using electrographic flow (EGF) methods to detect at least one of the presence of sources of atrial fibrillation in the other patients&#39; hearts, the locations of sources of atrial fibrillation in the other patients&#39; hearts, the activity levels of sources of atrial fibrillation in the other patients&#39; hearts, the spatial variability levels of sources of atrial fibrillation in the other patients&#39; hearts, and the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts (2) generating predictions or results using the machine learning model that comprise one or more of: (a) the patient has no detectable atrial fibrillation at the present time; (b) the patient has a type of atrial fibrillation having a substantial probability of being treated successfully with pulmonary vein isolation alone; (c) the patient has a type of atrial fibrillation that has a substantial probability of being treated successfully only with atrial ablation or with atrial ablation in combination with pulmonary vein isolation; (d) providing an estimate of the probability of recurrence of atrial fibrillation in the patient; (3) generating predictions or results using the machine learning model that comprise one or more of: (a) the estimated locations of one or more sources or rotors in the patient&#39;s heart; (b) whether one or more sources or rotors in the patient&#39;s heart are located in a right atrium or a left atrium of the patient; (c) at least one type of source or rotor in the patient&#39;s heart, including active rotors or sources, passive rotors or sources, focal points, breakthrough points, and chaotic rotors or sources; (d) activity levels of one or more sources or rotors in the patient&#39;s heart; (e) spatial variability levels of one or more sources or rotors in the patient&#39;s heart, and (f) flow angle stability levels of one or more sources or rotors in the patient&#39;s heart; (4) updating or training the trained machine learning model at least partially using MRI data obtained from a plurality of new patients, where areas or regions of fibrosis in the plurality of new patients have been identified in the MRI data and have been correlated using machine learning to one or more of body surface electrogram signals and EP mapping signals acquired from the plurality of new patients; (5) updating or training the trained machine learning model at least partially using one or more of body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients, where a first portion of the new patients have no atrial fibrillation and are identified in the data as being atrial-fibrillation-free, and a second portion of the new patients have atrial fibrillation and are identified in the data as having atrial fibrillation, and the paired data sets have been correlated to one another using machine learning; (6) updating or training the trained machine learning model at least partially using MRI data obtained from at least some of the plurality of new patients, and areas or regions of atrial fibrosis have been identified in the MRI data for such new patients, and the MRI data concerning areas or regions of fibrosis have been correlated to one or more of the body surface electrogram signals and the EP mapping signals using machine learning; (7) updating or training the trained machine learning model at least partially using body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients determined to have atrial fibrillation prior to being treated by intra-cardiac ablation, where body surface electrogram data and intracardiac EP mapping signals are obtained from the plurality of new patients before and after atrial ablation procedures are performed in at least one atrium of each such new patient, and the resulting body surface electrogram data and intracardiac EP mapping signals have been correlated to one another using machine learning; (8) updating or training the trained machine learning model at least partially using body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients determined to have atrial fibrillation prior to being treated by intra-cardiac ablation, where body surface electrogram data and intracardiac EP mapping signals are obtained from the plurality of new patients before and after atrial ablation procedures are performed in at least one atrium of each such new patient, and the resulting body surface electrogram data and intracardiac EP mapping signals have been correlated to one another using machine learning; (9) updating or training the trained machine learning model at least partially using body surface electrogram data and intracardiac EP mapping signals obtained from a plurality of new patients determined to have had atrial fibrillation previously, who were treated with a pulmonary vein isolation procedure previously, and who have been atrial-fibrillation-free for at least one year after being treated by the pulmonary vein isolation procedure, where body surface electrogram data and intracardiac EP mapping signals have been obtained from the plurality of new patients before and one year after the pulmonary vein isolation procedures, and the resulting body surface electrogram data and intracardiac EP mapping signals have been correlated to one another using machine learning; (10) updating or training the trained machine learning model at least partially using simulated body surface electrograms generated using a heart and torso model having one or more known origins of rotors or sources in one or more atria thereof; (11) updating or training the trained machine learning model at least partially using data from a plurality of patients, where the data from the patients relate to one or more of atrial volume, atrial dimensions, patient age, patient weight, patient height, and patient body mass index; (12) a trained machine learning model comprising one or more of: (a) a neural network, (b) a generative neural network; (c) a recurrent neural network, and (d) a feed-forward neural network; (13) a trained machine learning model comprising a recurrent neural network employing long short-term memory (LSTM); (14) a trained machine learning model comprising one or more of: (a) a nearest neighbor model; (b) a naive Bayes model; (c) a decision tree model; (d) a linear regression model; (e) a support vector machine (SVM) model, and (f) a neural network; (15) at least partially generating the trained machine learning model using supervised or unsupervised learning; (16) a trained machine learning model comprising convolutional layers; (17) removing or substantially removing at least portions of the QRS or QRST complex from at least some of the conditioned electrogram signals; (18) processing the EP mapping signals for at least some of the other patients using electrographic flow (EGF) methods to detect at least one of the presence of sources of atrial fibrillation in the other patients&#39; hearts, the locations of sources of atrial fibrillation in the other patients&#39; hearts, the activity levels of sources of atrial fibrillation in the other patients&#39; hearts, the steadiness levels of sources of atrial fibrillation in the other patients&#39; hearts, the flow angle stability levels of sources of atrial fibrillation in the other patients&#39; hearts, and a classification of the other patients as A-type patients, B-type patients, or C-type patients, at least one of which sources, source locations, source activity levels. source steadiness levels, flow angle stability levels, and patient classifications have then been correlated with their corresponding body surface electrogram signals in the trained machine learning model; (19) employing Green&#39;s function in processing the EP mapping signals; and (20) EGF methods including at least one optical flow analysis technique selected from the group consisting of a Horn-Schunck method, a Buxton-Buston method, a Black-Jepson method, a phase correlation method, a block-based method, a discrete optimization method, a Lucas-Kanade method, and a differential method of estimating optical flow. 
     In yet other embodiments utilizing EGF techniques, there are provided systems, devices, components, and methods for detecting the locations of epileptogenic and seizure onset zones in a patient&#39;s brain. These can include diagnosing and treating epilepsy in a patient using electroencephalographic (EEG) mapping techniques in conjunction with EGF techniques, as well as in some embodiments using imaging, navigation, electrical stimulation and other types of medical systems, devices, components, and methods. Such embodiments relate to systems, devices, components and methods for discovering with enhanced precision the location(s) of the source(s) of epileptic seizure onset zones (SOZs), and other types of epileptogenic zones, in a patient&#39;s brain. 
     In some embodiments configured to detect the source(s) of epileptic seizure onset zones (SOZs), and other types of epileptogenic zones, in a patient&#39;s brain, there are provided at least one computing device comprising at least one non-transitory computer readable medium configured to store instructions executable by at least one processor to determine the location of the at least one epileptogenic zone in the patient&#39;s brain, the computing device being configured to: (a) receive a plurality of electroencephalogram (EEG) signals representative of the electrical activity of the patient&#39;s brain; (b) normalize or adjust amplitudes of the EEG signals; (c) assign predetermined positions of electrodes employed to acquire the EEG signals; (c) provide or generate a two-dimensional (2D) or three-dimensional (3D) spatial map of the electrode positions; (d) for each or selected discrete times over which the EEG signals are being processed, process the amplitude-adjusted EEG signals to generate a plurality of three-dimensional EEG surfaces corresponding at least partially to the 2D or 3D map, one surface being generated for each such time, and (e) process the plurality of three-dimensional EEG surfaces through time to generate a velocity vector map corresponding at least partially to the 2D or 3D map, the velocity vector map being configured to reveal the location of the at least one epileptogenic zone within the patient&#39;s brain using EGF techniques. 
     These embodiments aimed at diagnosing epilepsy can also include one or more of non-invasive electrodes configured to be located on the exterior of a patient&#39;s skull; invasive electrodes configured to be implanted within the patient&#39;s brain; at least portions of the generated EEG surfaces generated by the computing device being configured to correspond to estimated wave shapes or wavefronts; EEG surfaces generated by the computing device using Green&#39;s function; EEG surfaces generated by the computing device using a two-dimensional or three-dimensional bi-harmonic spline interpolation function; vector maps generated by the computing device comprising arrows or colors representative of directions of electrical potential propagation; vector maps generated by the computing device comprising arrows or colors having attributes representative of velocities of electrical potential propagation; vector maps generated by the computing device being configured to reveal the at least one epileptogenic zone as one or more of a symptomatogenic zone, an irritative zone, a seizure-onset zone, a structurally abnormal zone, and a functional deficit zone in the patient&#39;s brain; vector maps generated by the computing device being configured to reveal a location of an epileptic focal point in the patient&#39;s brain; vector maps generated by the computing device being configured to reveal a location of an epileptogenic lesion in the patient&#39;s brain; vector maps being generated by the computing device using at least one optical flow analysis technique; the at least one optical flow analysis technique is selected from the group consisting of a Horn-Schunck method, a Buxton-Buston method, a Black-Jepson method, a phase correlation method, a block-based method, a discrete optimization method, a Lucas-Kanade method, and a differential method of estimating optical flow; the plurality of EEG signals are processed by the computing device to generate an averaged EEG signal, and the averaged EEG signal is subtracted from each of the individual EEG signals to generate artifact- or far-field adjusted individual EEG signals; the artifact-adjusted individual EEG signals are processed by the computing device with a high-pass filter to remove DC offsets; the high-pass filter applied by the computing device removes frequencies below between about 5 Hz and about 20 Hz; interpolated or estimated values are generated by the computing device for positions in between the measured or calculated grid values corresponding to one or more of the EEG signals, the plurality of smoothed EEG surfaces, and the vector map.; a representative amplitude value is generated by the computing device for each individual EEG signal, and the representative amplitude value generated for each EEG signal is stored for later use in image backgrounds that show low signal amplitude areas of the 2D representation, the low signal amplitude areas being indicative of one or more of low electrical activity areas or regions of the patient&#39;s brain, poor electrode contact, and areas of the patient&#39;s brain that have sustained trauma, injury or neural damage; electrode positions in the 2D representation are modified by the computing device based upon navigational or positional data corresponding to measured or sensed actual electrode positions; the navigational data are provided to the computing device by a medical navigation system, a computed tomography scanner, a magnetic resonance image scanner, or an X-ray fluoroscopy system; 
     In view of the structural and functional descriptions provided herein, those skilled in the art will appreciate that portions of the described devices and methods may be configured as methods, data processing systems, or computer methods. Accordingly, these portions of the devices and methods described herein may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware, such as shown and described with respect to computer system  300  illustrated in  FIG. 1( b ) . Furthermore, portions of the devices and methods described herein may be a computer method stored in a computer-usable storage medium having computer readable program code on the medium. Any suitable computer-readable medium may be utilized including, but not limited to, static and dynamic storage devices, hard disks, optical storage devices, and magnetic storage devices. 
     Certain embodiments of portions of the devices and methods described herein are also described with reference to block diagrams of methods, systems, and computer methods. It will be understood that such block diagrams, and combinations of blocks diagrams in the Figures, can be implemented using computer-executable instructions. These computer-executable instructions may be provided to one or more processors of a general purpose computer, a special purpose computer, or any other suitable programmable data processing apparatus (or a combination of devices and circuits) to produce a machine, such that the instructions, which executed via the processor(s), implement the functions specified in the block or blocks of the block diagrams. 
     These computer-executable instructions may also be stored in a computer-readable memory that can direct computer  300  or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture including instructions which implement the function specified in an individual block, plurality of blocks, or block diagram. The computer program instructions may also be loaded onto computer  300  or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on computer  300  or other programmable apparatus provide steps for implementing the functions specified in the an individual block, plurality of blocks, or block diagram. 
     In this regard,  FIG. 1( b )  illustrates only one example of a computer system  300  (which, by way of example, can include multiple computers or computer workstations) that can be employed to execute one or more embodiments of the devices and methods described and disclosed herein, such as devices and methods configured to acquire and process sensor or electrode data, to process image data, and/or transform sensor or electrode data and image data associated with the analysis of cardiac electrical activity and the carrying out of the combined electrophysiological mapping and analysis of the patient&#39;s heart  10  and ablation therapy delivered thereto. 
     It will now be seen that the various systems, devices, components and methods disclosed and described herein are capable of detecting with considerable accuracy and precision the locations of sources of cardiac rhythm disorders in a patient&#39;s heart. 
     What have been described above are examples and embodiments of the devices and methods described and disclosed herein. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the invention, but one of ordinary skill in the art will recognize that many further combinations and permutations of the devices and methods described and disclosed herein are possible. Accordingly, the devices and methods described and disclosed herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. In the claims, unless otherwise indicated, the article “a” is to refer to “one or more than one.” 
     The foregoing outlines features of several embodiments so that those skilled in the art may better understand the detailed description set forth herein. Those skilled in the art will now understand that many different permutations, combinations and variations of hearing aid  10  fall within the scope of the various embodiments. Those skilled in the art should appreciate that they may readily use the present disclosure as a basis for designing or modifying other processes and structures for carrying out the same purposes and/or achieving the same advantages of the embodiments introduced herein. Those skilled in the art should also realize that such equivalent constructions do not depart from the spirit and scope of the present disclosure, and that they may make various changes, substitutions and alterations herein without departing from the spirit and scope of the present disclosure. 
     After having read and understood the present specification, those skilled in the art will now understand and appreciate that the various embodiments described herein provide solutions to long-standing problems, both in the use of electrophysiological mapping systems and in the use of cardiac ablation systems.