Source: https://patents.google.com/patent/US10201277B2/en
Timestamp: 2019-05-24 10:34:40
Document Index: 587092268

Matched Legal Cases: ['art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art 10', 'art.\n9', 'art.\n10', 'art.\n11', 'art.\n18']

US10201277B2 - Systems, devices, components and methods for detecting the locations of sources of cardiac rhythm disorders in a patient's heart - Google Patents
US10201277B2
US10201277B2 US15/548,671 US201615548671A US10201277B2 US 10201277 B2 US10201277 B2 US 10201277B2 US 201615548671 A US201615548671 A US 201615548671A US 10201277 B2 US10201277 B2 US 10201277B2
US15/548,671
US20180020916A1 (en
2015-09-07 Priority to WOPCT/EP2015/001803 priority
2015-09-07 Priority to WOPCT/EP2015/001801 priority
2016-09-07 Priority to PCT/IB2016/001273 priority patent/WO2017042623A1/en
2018-01-25 Publication of US20180020916A1 publication Critical patent/US20180020916A1/en
2019-02-12 Publication of US10201277B2 publication Critical patent/US10201277B2/en
Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient's heart. In some embodiments, electrogram signals are acquired from inside a patient's heart, and subsequently normalized, adjusted and/or filtered, followed by generating a two-dimensional (2D) spatial map, grid or representation of the electrode positions, processing the amplitude-adjusted and filtered electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2 D grid, one surface being generated for each or selected discrete times, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector map corresponding at least partially to the 2 D grid. The resulting velocity vector map is configured to reveal the location of the source of the at least one cardiac rhythm disorder, which may be, by way of example, an active rotor in a patient's myocardium and atrium.
This application is a national stage entry of, and claims priority and other benefits from, Patent Application PCT/IB2016/001273 to Ruppersberg filed on Sep. 7, 2016, which is entitled “Systems, Device, Components and Methods for Detecting the Locations of Sources of Cardiac Rhythm Disorders in a Patient's Heart (hereafter “the '001273 patent application”). This application also claims priority and other benefits from 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 from which the '001273 patent application also claims priority. This application further claims priority and other benefits from 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”), and from which the '001273 patent application further claims priority. The respective entireties of the '001801, '001803 and '001273 patent applications are hereby incorporated by reference herein.
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 Miss. 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.
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.
Accordingly, it is one objective of the present invention to provide an improved system, especially a medical system, and methods for acquiring and processing intracardiac electrogram signals that reliably and accurately yield the precise locations and sources of cardiac rhythm disorders in a patient's heart.
In one embodiment, there is provided a system for detecting in a patient's heart a location of a source of at least one cardiac rhythm disorder, the system comprising 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 perform a method of determining the source and location of the cardiac rhythm disorder in the patient's heart, the computing device being configured to: (a) receive electrogram signals; (b) normalize or adjust amplitudes of the electrogram signals; (c) assign predetermined positions of the electrodes on a mapping electrode assembly to their corresponding electrogram signals; (c) provide or generate a two-dimensional (2D) spatial map of the electrode positions; (d) for discrete or selected times over which the electrogram signals are being processed, process the amplitude-adjusted electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each such time, and (e) process the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector map corresponding at least partially to the 2D map, the velocity vector map being configured to reveal the location of the source of the at least one cardiac rhythm disorder.
In another embodiment, there is provided a method of detecting a location of a source of at least one cardiac rhythm disorder in a patient's heart, the method comprising normalizing or adjusting the amplitudes of electrogram signals acquired from electrodes, especially configured to be located inside the patient's heart, assigning positions or identifiers for each of the electrodes to corresponding individual electrogram signals, providing or generating a two-dimensional (2D) spatial map of the electrode positions, for each or selected discrete times over which the electrogram signals are being processed, processing the amplitude-adjusted electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each such time, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector map corresponding at least partially to the 2D map, the velocity vector map being configured to reveal the location of the source of the at least one cardiac rhythm disorder.
Electrogram signals and processed data may be delivered or communicated to the system for performing the method, e.g., via a data carrier, after they have been acquired by the electrodes and stored for later processing by the system and method according to this invention. Further advantageous embodiments of the system and method are disclosed herein or will become apparent to those skilled in the art after having read and understood the claims, specification and drawings hereof.
FIG. 3 shows an illustrative embodiment of a mapping electrode assembly 120 of catheter 110 of FIG. 2;
FIG. 9 shows another vector velocity map generated from actual patient data using method or algorithm 200, and
Described herein are various embodiments of systems, devices, components and methods for aiding in the diagnosis and treatment cardiac rhythm disorders in a patient's heart using electrophysiological mapping 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.
Referring now to FIG. 1(a), there is illustrated one embodiment of a combined cardiac electrophysiological (EP) mapping, 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'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'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. 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.
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'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 82, probes and/or sensors A1 through H8 arranged in an 8×8 grid that are included in electrode mapping assembly 120, which is configured for insertion into the patient's heart through the patient's blood vessels and/or veins. Other numbers, arrangements, configurations and types of electrodes 82 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.
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 systern 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'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'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'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's heart 10. This permits a medical practitioner to move interactively and quickly the electrodes 82 of catheter 110 towards the location of the source of the cardiac rhythm disorder or irregularity.
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 82 A1 through H8 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.
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 82 A1-H8 (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, 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'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.
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 algorithms. 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 algorithm 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.
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 an individual block, plurality of blocks, or block diagram.
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 CDROM 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, e.g. for 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 algorithm (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.
Referring now to FIG. 2, there is shown an illustrative view of one embodiment of a distal portion of catheter 110 inside a patient'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's inferior vena cava and foramen ovalen (“IVC” and “FO” in FIG. 2), and is configured to obtain electrogram signals from left atrium 12 via an 8×8 array of electrodes 82 A1 through H8. Mapping electrode assembly and catheter 110 may also be positioned with the patient'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's heart chamber, and may also be employed to facilitate the assessment of entrainment, conduction velocity studies, and refractory period in a patient'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 82 A1 through H8 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's heart. At step 210, the amplitudes of electrogram signals acquired from electrodes 82 located inside a patient's heart, e.g., electrodes 82 included in a mapping electrode assembly 120, are normalized and/or adjusted. At step 230, positions A1 through H8 corresponding to each of the electrodes 82 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 A1 through H8 is generated or provided. In some embodiments, a three-dimensional (3D) spatial map of electrode positions A1 through H8 is generated or provided. (As discussed above, fewer or more than 64 electrodes 82 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 across the 2D (or 3D) map to generate a plurality of three-dimensional electrogram surfaces (which according to one embodiment may be smoothed electrogram surfaces), one surface being generated for each such discrete time. At step 250, the plurality of three-dimensional electrogram surfaces that have been generated across the 2D (or 3D) map through time are processed to generate a velocity vector map. The velocity vector 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's heart 10 at the location of the source of the cardiac rhythm disorder indicated by the velocity vector map.
Algorithm 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, algorithm 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 algorithm that determines the most likely mean spatial velocity map derived from hundreds of individual samples of amplitude patterns recorded by the mapping electrodes.
One approach that has been discovered to work particularly well with electrogram signal data is to determine the Green'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's function evaluated for each point of separation. Biharmonic spline interpolation, which can be used in conjunction with Green'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 based on Green's function. A Green'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'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.
In one embodiment, Green'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 algorithms and mathematics underlying Green's function, see: (1) “Moving Surface Spline Interpolation Based on Green'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'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.
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'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, multiquadric bi-harmonic 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 across the 2D or 3D map through time to generate a velocity vector 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 NonIterative, 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 algorithm assumes smoothness in flow over most or all of an image. Thus, the Horn-Schunck algorithm 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.
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 l(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 (l) can be represented as follows:
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/(lx 2+ly 2)½. However, one cannot determine the component of movement in the direction of the isobrightness 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.
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's heart, are sought to be determined. One underlying objective of method or algorithm 200 is to produce a vector velocity map, which is a representation of electrographical flow (and not optical flow) within e.g. a patient's heart. Instead of looking for differences or changes in optical brightness or intensity, changes in the velocity, direction and shape of electrical signals changes in electrographical flow) across a patient's heart are determined. That is, algorithm 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 of the reasons why algorithm 200 works so well in detecting the locations of the sources of cardiac rhythm disorders and irregularities is that ion channels in a patient'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.
Algorithm 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. Algorithm 200 has as its key inputs the potentials of electrogram signals (not their derivatives). As a result, algorithm 200 is notably free from the effects of spurious noise and artifacts introduced by time-derivative data processing techniques, including in step 250.
Indeed, none of steps 210, 230, 240, or 250 of method or algorithm 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 algorithm 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 algorithm 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 algorithm 200 (e.g., DSP filtering, deconvolution, etc.).
The data shown in FIG. 5(c) were used to perform an analysis in accordance with algorithm 200, which was carried out in three main steps: (1) normalization/adjustment/filtering of electrogram signals (step 210); (2) generating three-dimensional smoothed electrogram surfaces for discrete times or time slices from the normalized/adjusted/filtered electrogram signals (step 240) generated in the first main step 210, and (3) generating a velocity vector map based on the smoothed electrogram surfaces (step 250) generated in the second main step 240.
Described now is one embodiment and illustrative example of the first main step of the algorithm 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).
Now I describe one embodiment and illustrative example of the second main step of the algorithm 200 (generating three-dimensional electrogram surfaces for discrete times or time slices, or estimation of spatial wave shapes). The second step of algorithm 200 takes the spatial distributions of all electrodes 82 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, algorithm 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 bi-harmonic spline interpolation techniques and Green'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 algorithm 200 (generating a velocity vector map based on the electrogram surfaces). The third main step of algorithm 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 82 (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 40 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 algorithm 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 algorithm 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 (indicated by arrows 40) 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 40 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 algorithm 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's heart 10. The time interval over which such electrogram signals are recorded inside the patient's heart 10 may, of course, vary according to, among other things, the requirements of the examination 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.
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's myocardial wall where the electrogram signals were acquired and measured (see step 243) by catheter 110.
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 across a 2D or 3D map through time to generate a velocity vector map, for example by means of the optical flow analysis and estimation techniques and methods, such those 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, the entirety of which is hereby incorporated by reference herein. From Carrick, et al.'s video corresponding to the foregoing publication, and referring now to FIG. 7(a), 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 marked with circles within the 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 with circles 45) are apparent, as are two passive rotors (marked with 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 vector arrows 40 in FIG. 7(e) not only show the rotational centers of the individual rotors, but also show that active rotors indicated by circles 45 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 stars 46 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.
Upon applying smoothed surface calculations and fitting (as shown in FIG. 7(i)), algorithm 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)).
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 82 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 algorithm 200.
After confirming that algorithm 200 was capable of detecting complex rotor structures accurately in a patient's myocardium—even in the presence of strong artifacts and noise—algorithm 200 was applied to different time portions of the actual patient data shown in FIG. 5(b) so as to test further the algorithm'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'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 algorithm 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's myocardium. The velocity vector maps generated by algorithm 200 permit a health care professional to identify such cardiac rhythm disorders in a patient'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 algorithm 200. In FIG. 9, the 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 a circular arrow 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 the actual patient data. The raw data corresponding to FIGS. 10(a) through 10(d) were acquired from a single patient'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(c) shows the greyscale-schemes employed in FIGS. 10(a), 10(b), and 10(d).
The vector velocity map of FIG. 10(a) generated using algorithm 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 algorithm 200, but using data acquired from only 16 electrodes 82 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 algorithm 200, as it has been applied to electrographical flow problems.
FIG. 10(d) shows another example of results obtained using algorithm 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 algorithm 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).
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'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's heart 10 are known. Navigational data can be employed by computer 300 in algorithm 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 algorithm 200.
1. A system for detecting a location of a source of at least one cardiac rhythm disorder in a patient's heart, comprising:
at least one computing device comprising at least one non-transitory computer readable medium configured to store instructions executable by at least one processor for determining the source and location of the cardiac rhythm disorder in the patient's heart, the computing device being configured to: (a) receive electrogram signals acquired inside the patient's heart using electrodes mounted on a mapping electrode assembly; (b) normalize and/or adjust amplitudes of the electrogram signals; (c) assign predetermined positions of electrodes on the mapping electrode assembly to their corresponding electrogram signals; (c) provide or generate a two-dimensional (2D) spatial map of the electrode positions; (d) for each or selected discrete times over which the electrogram signals are being processed, process the amplitude-adjusted electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each such time, and (e) process the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector map corresponding at least partially to the 2D map, the velocity vector map being configured to reveal the location of the source of the at least one cardiac rhythm disorder and being displayed on a monitor or screen operably connected to the computing device so that a user can diagnose or treat the patient.
2. The system of claim 1, wherein at least portions of the electrogram surfaces generated by the computing device are configured to correspond to estimated wave shapes or wavefront.
3. The system of claim 1, wherein the electrogram surfaces are generated by the computing device using Green's function.
4. The system of claim 1, wherein the electrogram surfaces are generated by the computing device using a two-dimensional bi-harmonic spline interpolation function.
5. The system of claim 1, wherein the vector map generated by the computing device comprises arrows or colors representative of directions of electrical potential propagation.
6. The system of claim 1, wherein the vector map generated by the computing device comprises arrows or colors having attributes representative of velocities of electrical potential propagation.
7. The system of claim 1, wherein the vector map generated by the computing device is configured to reveal the at least one cardiac rhythm disorder as an active rotor at the location.
8. The system of claim 1, wherein the vector map generated by the computing device is configured to reveal a location of a passive rotor in the patient's heart.
9. The system of claim 1, wherein the vector map generated by the computing device is configured to reveal a location of a focal point in the patient's heart.
10. The system of claim 1, wherein the vector map generated by the computing device is configured to reveal a location of a breakthrough point in the patient's heart.
11. The system of claim 1, wherein the velocity vector map is generated by the computing device using at least one optical flow analysis technique.
12. The system of claim 11, wherein 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.
13. The system of claim 1, wherein the plurality of electrogram signals are processed by the computing device to generate an averaged electrogram signal, and the averaged electrogram signal is subtracted from each of the individual electrogram signals to generate artifact- or far-field adjusted individual electrogram signals.
14. The system of claim 13, wherein the artifact-adjusted individual electrogram signals are processed by the computing device with a high-pass filter to remove DC offsets.
15. The system of claim 14, wherein the high-pass filter applied by the computing device removes frequencies below between about 5 Hz and about 20 Hz.
16. The system of claim 1, wherein 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 electrogram signals, the plurality of smoothed electrogram surfaces, and the velocity vector map.
17. The system of claim 1, wherein a representative amplitude value is generated by the computing device for each individual electrogram signal, and the representative amplitude value generated for each electrogram 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 valve artifacts, poor electrode contact, and fibrotic areas of the heart.
18. The system of claim 1, wherein the 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.
19. The system of claim 18, wherein 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.
20. The system of claim 1, further comprising an electrophysiological data acquisition device configured to receive and condition the signals provided by the electrodes to provide as an output therefrom the electrogram signals.
21. The system of claim 1, wherein the monitor or screen is further configured to display one or more electrogram signals received from the data acquisition device, the normalized or amplitude-adjusted electrogram signals, the predetermined positions of the electrodes on a catheter, the 2D representation of the electrode positions, or the plurality of three-dimensional smoothed electrogram surfaces.
22. The system of claim 1, further comprising an ablation system comprising an ablation catheter, the ablation catheter being configured for ablating the patient's heart at the location and source of the cardiac rhythm disorder indicated by the velocity vector map.
23. The system of claim 1, further comprising a catheter configured for insertion inside the patient's body and heart, the catheter comprising at a distal end thereof the mapping electrode assembly comprising a plurality of electrodes for sensing and acquiring from different locations inside the patient's heart the electrogram signals, each electrode having a predetermined position on the mapping electrode assembly associated therewith.
24. The system of claim 23, wherein the catheter further comprises a force sensor located at the distal tip thereof, the force sensor being configured to engage an interior wall of the patient's heart and indicate when the interior wall has been engaged by the force sensor.
25. The system of claim 1, wherein the catheter is a basket catheter.
US15/548,671 2015-09-07 2016-09-07 Systems, devices, components and methods for detecting the locations of sources of cardiac rhythm disorders in a patient's heart Active 2036-10-06 US10201277B2 (en)
WOPCT/EP2015/001803 2015-09-07
WOPCT/EP2015/001801 2015-09-07
US20180020916A1 US20180020916A1 (en) 2018-01-25
US10201277B2 true US10201277B2 (en) 2019-02-12
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Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:RUPPERSBERG, PETER, DR.;REEL/FRAME:043230/0716