Patent Publication Number: US-2023138492-A1

Title: Monitoring and analysis of invasive and non-invasive electrophysiological signals

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Pat. Application No. 63/274710, filed 2 Nov. 2021, which is incorporated herein by reference in its entirety. 
    
    
     FIELD 
     The present technology is generally related to monitoring and analysis of electrophysiological signals. 
     BACKGROUND 
     Electrocardiographic mapping (ECM) is a technology that is used to determine and display heart electrical information from sensed electrical signals. ECM can be performed based on invasive or non-invasive measurements of cardiac electrical activity. However, each of invasive measurements and non-invasive measurements can have limitations that may impact the electrophysiology data that is ultimately used in the diagnosis and treatment of cardiac arrhythmias. 
     SUMMARY 
     The techniques of this disclosure generally relate to using both invasive and non-invasive electrophysiological measurements for electrocardiographic mapping. 
     In one aspect, the present disclosure provides a system that includes an arrangement of body surface electrodes adapted to measure electrophysiological signals on an outer surface of a patient’s body. The system also includes an invasive electrode adapted to measure electrophysiological signals within the patient’s body. A signal monitoring device has inputs to receive to the electrophysiological signals measured on the outer surface of a patient’s body and within the patient’s body. The signal monitoring device is configured to provide electrophysiological measurement data representing the electrophysiological signals measured on the outer surface of the patient’s body and the electrophysiological signals measured within the patient’s body. A computing apparatus includes non-transitory memory to store data and instructions executable by a processor thereof. The data can include geometry data representing the anatomy of the patient spatially, and locations of the respective body surface electrodes and the invasive electrode in three-dimensional space. Geometry data for the invasive electrode can vary based on the movement of the invasive electrode within the patient’s body. The instructions, which are executable by the processor, are programmed to reconstruct electrophysiological signals on nodes distributed across a surface of interest within the patient’s body based on the electrophysiological measurement data and the geometry data, in which the electrophysiological measurement data representing the electrophysiological signals measured at the outer surface of a patient’s body and within the patient’s body. 
     In another aspect, the disclosure provides a computer-implemented method. The method includes storing location data for at least one invasive electrode that is movable within a patient’s body. The method also includes storing electrophysiological measurement data representing the electrophysiological signals measured at the outer surface of a patient’s body by body surface electrodes and within the patient’s body by the at least one invasive electrode. The method also includes storing geometry data representing anatomy of the patient spatially, and locations of the respective body surface electrodes and the at least one invasive electrode in three-dimensional space. The geometry data for the at least one invasive electrode can vary based on movement of the at least one invasive electrode within the patient’s body. The method also includes reconstructing electrophysiological signals on a surface of interest within the patient’s body based on the electrophysiological measurement data and the geometry data. 
     The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram that illustrates a system for monitoring and analyzing electrophysiological signals. 
         FIG.  2    is a mathematical expression showing an example of correspondence between portions of the expressions and measured electrophysiological signals. 
         FIG.  3    is a set of ECGI maps that illustrate an example PVC event. 
         FIG.  4    is a set of ECGI maps that illustrate another example PVC event. 
         FIG.  5    is a set of ECGI maps that illustrate another example PVC event. 
         FIG.  6    is bar graph that illustrates an example library of PVC ECGI image data sets. 
         FIG.  7    is a graph that illustrates examples of partitioned data sets for training a machine learning model. 
         FIG.  8    is confusion matrix that illustrates performance of machine learning model trained to classify different categories of PVCs. 
         FIG.  9    is a set of ECGI maps that illustrate examples of classification of respective ECGI images for different categories of PVCs predicted by an example machine learning model. 
         FIG.  10    is a flow diagram illustrating an example method for monitoring and analyzing electrophysiological signals. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure relates to systems and methods for monitoring and analysis of electrophysiological signals, which may be measured invasively and non-invasively. 
     In an example, a system includes an arrangement of body surface electrodes adapted to measure non-invasively electrophysiological signals on an outer surface of a patient’s body as well as an invasive electrode adapted to measure invasively electrophysiological signals within the patient’s body (e.g., on or within the patient’s heart). A signal processing device has inputs coupled to receive to the electrophysiological signals measured on the outer surface of a patient’s body and within the patient’s body and to provide electrophysiological measurement data. The electrophysiological measurement data thus represents both the electrophysiological signals measured on the outer surface of the patient’s body and the electrophysiological signals measured within the patient’s body. The electrophysiological measurement data can be stored in memory of a computing apparatus along with geometry data representing anatomy of the patient spatially and locations of the respective body surface electrodes and the invasive electrode in three-dimensional space. The computing apparatus also includes one or more processors configured to reconstruct electrophysiological signals on a surface of interest within the patient’s body based on the electrophysiological measurement data and the geometry data. For example, the electrophysiological signals can be reconstructed onto an epicardial surface, endocardial surface, a transmural surface (e.g., at spatial locations between epicardial and endocardial surfaces) as well as any combinations thereof. 
     In some examples, the invasive electrode is mounted to a probe (e.g., catheter) that is moveable within the patient’s body so that it can be used to measure electrophysiological signals at respective locations. The location of the probe can be determined by a localization system and the location can be updated as the probe is moved from a given location to a new location within the patient’s body. The computing device can be configured to identify a region of interest based on the reconstructed electrophysiological signals on the surface of interest, and activation time (e.g., an activation map) can be determined for the region of interest. A site (e.g., one or more points) of earliest activation can be determined based on the electrophysiological signals within the region of interest. The earliest activation site or another site of interest determined from the reconstructed electrophysiological signals can be used to provide spatial guidance to a user, such as to instruct the user to move the probe to a target site, which may be the site of earliest activation or another site of interest. The electrophysiological signals can be measured at different locations within the region of interest and an updated electrocardiographic map can be generated based on the electrophysiological and geometry data. 
     In an example, the computing apparatus is programmed to compute the reconstructed electrophysiological signals using a method of fundamental solutions (MFS) in which each electrode (invasive and non-invasive) is represented mathematically according to its relative spatial location and signal measurement. As the probe is moved, the probe location data is updated from a previous location to a current location, and a portion of the mathematical representation for electrophysiological signals measured by the probe and its location is updated to reflect the current location and signal measurement. The computing apparatus can employ MFS to reconstruct the electrophysiological signals on the surface of interest updated mathematical representation and the electrophysiological signals. 
     In a further example, the computing apparatus can instruct a user to place the invasive electrode at a prescribed location within the patient’s body in response to data indicating an ambiguity in electrophysiological signals on the surface of interest at or near the prescribed location. The prescribed location can represent a location that provides low amplitude signals or a signal otherwise difficult to sense from the body surface electrodes. The geometry data can be updated when the invasive electrode is at or near the prescribed location and electrophysiological signals can be reconstructed on the surface of interest based on the updated geometry data and the electrophysiological data, including the electrophysiological signals measured on the outer surface of the patient’s body and the electrophysiological signals measured at or near the prescribed location. The resulting reconstructed electrophysiological signals can thus resolve or reduce the ambiguity and exhibit improved accuracy because the reconstruction is based on additional invasive electrophysiological signals at or near the prescribed location, which is determined or known a priori to influence the ambiguity. 
       FIG.  1    depicts an example of a system  10  for monitoring and mapping electrophysiological measurements from a patient  12 . The system  10  includes one or more invasive electrodes  14  and body surface electrodes  16 , which are coupled to a signal measurement device  18 . For example, each of the electrodes  14 ,  16  is coupled to the signal measurement device  18  through a respective electrically conductive channel (e.g., including electrically insulated wires and/or traces) to communicate electrophysiological signals measured from the patient’s body. The electrically conductive channels for the electrodes  14  and  16  can include an arrangement of connectors configured to couple to respective connectors (e.g., male and female connectors) of an electrode interface  20  of the measurement device  18 . In other examples, the electrodes  14 ,  16  may be coupled to the electrode interface  20  through other forms of communication (e.g., optical fiber or wireless leads). The electrode interface  20  can measure unipolar, bipolar or a combination of unipolar and bipolar electrophysiological signals depending on the configuration of the measurement device  18  and processing of the signals measured by the electrodes  14  and  16 . In the following examples, the electrophysiological signals are described as being cardiac signals measured by the electrodes  14 ,  16 ; however, the systems and methods disclosed herein are applicable to measure and process other types of electrophysiological signals (e.g., brain signals). 
     As an example, the one or more invasive electrodes  14  can be coupled to or otherwise carried by an electrophysiology probe. The probe can be a catheter that is moveable within the patient’s body  12 , such that the position of the probe and associated electrode(s) can vary. For example, the cardiac catheter can be inserted into a femoral vein and advanced to a position within the patient’s heart. Alternatively, the probe and electrode(s)  14  can be configured to measure electrophysiological signals on an outer surface of the patient’s heart. Thus, the signals measured by the invasive electrodes  14  depend on where the probe is positioned within the patient’s body  12 . The probe may be moved manually, robotically assisted or fully robotically. 
     The system  10  also includes a navigation system  22  configured to localize the spatial position of the invasive electrode  14 . The spatial position of the electrode  14  (or associated probe) can be stored in memory as location data  24 . The location data  24  thus represents a three-dimensional spatial position (e.g., spatial coordinates) of the electrode  14 . Alternatively, the location data can represent the location of a sensor or other known location on the probe carrying the electrode(s), and the spatial location of each electrode  14  can be derived readily from the location data  24 . The spatial location of the electrode(s)  14  can be with respect to the patient’s body or a coordinate system of the navigation system  22 . As described below, for example, the spatial location of the invasive electrode  14 , which is described by or derived from the location data  24 , can be registered with respect to anatomical geometry of the patient’s body  12 . The registration can be repeated in response to detecting changes in the location data as the electrode is moved within the patient’s body. In some examples, the navigation system  22  can also generate the location data  24  to include the location of one or more of the non-invasive electrodes  16 , which are distributed across an outer surface of the patient’s body (e.g., on the thorax). 
     Useful examples of the navigation system  22  include the CARTO XP EP navigation system (commercially available from Biosense-Webster) and the ENSITE NAVX visualization and navigation technology (commercially available from Abbott); although other navigations systems could be used to provide the navigation data representative of the spatial position for the invasive electrode  14  and associated probe. Another example of a navigation system that can be utilized to localize the position of the invasive electrodes is disclosed in U.S. Pat. No. 10,323,922, issued Jun. 18, 2019 Aug. 29, 2014, and entitled LOCALIZATION AND TRACKING OF AN OBJECT, which is incorporated herein by reference. For example, a probe (e.g., catheter) can include one or more electrodes  14  disposed at known locations with respect to the probe. The probe can be used to position each such electrode  14  with respect to the heart and the navigation system  22  can determine corresponding three-dimensional coordinates for the electrode(s)  14  that is represented by the location data  24 . 
     The number and placement of invasive electrodes  14  can vary depending upon the type of catheter or other probe to which the electrodes are coupled. In a further example, the invasive electrode(s) can be contact electrodes that measure signals from a surface of an object that the electrode physically engages or contacts. Alternatively, the invasive electrode(s)  14  can be non-contact electrodes that measure signals from a surface of an object while the electrode is spatially apart from (e.g., no physical contact between the electrode and the surface being measured). 
     The body surface electrodes  16  include a distributed arrangement of multiple electrodes (e.g., about  250  or more sensors) positioned on an outer surface of the patient’s body  12 . In an example, the body surface electrodes  16  are distributed completely around the thorax, such as can be mounted to a wearable garment (e.g., vest) in which each of the electrodes has a known location in a given coordinate system. For example, body surface electrodes  16  can be implemented as a non-invasive type of sensor apparatus as disclosed in U.S. Pat. Publication No. 2013/0281814, entitled Multi-Layered Sensor Apparatus. Other configurations and numbers of body surface electrodes  16  could be utilized in other examples. 
     As described above, the electrode interface  20  has respective inputs coupled to each of the electrodes  14  and  16 . The signal measurement device  18  can also include signal processing circuitry  26  configured to process electrical signals received by the electrodes  14 ,  16 . The signal processing circuitry  26  can be implemented as hardware and/or software, such as including a digital signal processor and other processing circuitry and machine readable instructions (executable by a processor) configured to remove noise (e.g., line noise) and convert the received signals into a desired format for storing the measured electrophysiological signals as electrophysiological data  28 . The signal processing circuitry  26  can also add channel information (e.g., to specify electrode number or location), add timestamps (e.g., to specify the time or each measurement sample) or perform other signal processing functions that may be desired. The electrophysiological data  28  thus can include signal measurement values for each sample as well as additional information, such as time stamps and channel information. 
     The system  10  also includes a mapping system  30  configured to generate output data  32 , which may be used to render a graphical map (e.g., a map on a heart model)  34  and/or display processed electrical signals on a display  36 . The mapping system  30  can also provide information in other display formats to provide guidance to the user representative of and/or derived from electrical activity that may be measured by any combination of the electrodes  14  and  16 . The mapping system  30  can be a computer-implemented apparatus that includes one or more processors and memory to store data and machine-readable instructions, which are executable by the processor. 
     The mapping system  30  is programmed to generate the output data  32  based on the electrophysiological data  28  and geometry data  40 . As described herein, for example, the output data  104  can represent or characterize electrophysiological signals on a surface of interest. For example, the surface of interest is a cardiac envelope, such as an epicardial surface, an endocardial surface, a combination of epicardial and endocardial surfaces of the patient’s heart, a full thickness model of the heart or other three-dimensional geometrical surface (e.g., a sphere). The mapping system  30  may further generate the output data  32  to provide guidance to help a user move the invasive electrode  14  to a location of interest (e.g., one or near a region of interest of the heart) based on the electrophysiological data  28  and the geometry data  40 , as disclosed herein. 
     The geometry data  40  includes electrode geometry data and anatomical geometry data. The electrode geometry data to represent spatial locations of respective body surface electrodes  16  and invasive electrode  14  in three-dimensional space. The anatomical geometry data represents spatial geometry of the surface of interest of the patient in three-dimensional space. The mapping system  30  can include a spatial registration function (e.g., machine-readable instructions)  42  programmed to co-register the electrode geometry data and the anatomical geometry data in a common coordinate system. The spatial registration function  42  can implement one or more transforms to align spatially respective data sets for location of the invasive electrodes  14 , the location of the body surface electrodes  16  as well as the anatomical geometry for the surface of interest. 
     As an example, the navigation system  22  generates location data  24  to represent the spatial location of the invasive electrodes  14  in a given coordinate system (e.g., of the navigation system), which is typically different from the coordinate system in which the anatomical geometry data is generated. The anatomical geometry data can be derived from imaging data acquired by a three-dimensional medical imaging modality. In one example, an anatomical model can be constructed based on imaging data obtained (e.g., by a medical imaging modality) for the patient to provide spatial coordinates for points across the patient’s heart and, in some cases, in which the electrodes  16  are positioned on the patient’s body when the medical image is acquired, for the locations of the body surface electrodes  16  positioned on the outer surface of the patient’s body. The medical imaging data can be generated for the patient’s body using a medical imaging modality, such as multi-plane x-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), single-photon emission computed tomography (SPECT) and the like. The electrode locations and locations of the surface (or surfaces) of interest can be identified in a respective coordinate system of the acquired images through appropriate image processing, including extraction and segmentation. For instance, segmented image data can be converted into a two-dimensional or three-dimensional graphical representation that includes the volume of interest for the patient. Appropriate anatomical or other landmarks, including locations the electrode  14 , can be identified in the geometry data  40  to facilitate spatial registration of the electrophysiological data  28 . The identification of such landmarks can be done manually (e.g., by a person via image editing software) or automatically (e.g., via image processing techniques). 
     In another example, the location of the body surface electrodes  16  can be acquired by a digitizer, manual measurements or another non-imaging based technique, such as including being obtained by the navigation system  22  and included as part of the location data  24 . The spatial registration function  42  can provide the geometry data  40  to include the location information for the electrodes  14  and  16  as well as the anatomical geometry all spatially aligned in the common coordinate system. Because the location of the invasive electrode can be moved within the patient’s body  12 , the corresponding location data  24  can be updated (e.g., in real-time or near-real time) to reflect the current spatial where the invasive electrical measurement is obtained. Thus, the spatial registration function  42  can further be programmed to update the geometry data in response to detecting change in the location data  24 . The location data  24  can also include a time stamp so that the mapping system  30  can programmatically link (e.g., synchronize) a given time instance of the geometry data  40 , which includes location of the electrode  14 , with respective samples of the electrophysiological signals that are measured. 
     In a further example, the catheter or probe carrying the invasive electrode  14  can also include an imaging device to acquire images representing the anatomy of the patient. For example, the imaging device can be implemented as an optical camera, a dielectric camera, an optical coherence tomography device, an ultrasound transducer or other device integrated into or carried by the catheter or probe. The imaging device is configured to acquire images of anatomical feature intraoperatively, such as including all or part of the surface of interest or structure that is coupled to the surface of interest. The acquired images can be stored in memory as image data, such as in response to a user input to trigger image capture. The spatial registration function  42  can further be programmed to update and refine the geometry data based on the image data, such as to refine the surface of interest or other anatomy within the patient’s body based on the invasively acquired images. 
     The mapping system  30  also includes a reconstruction engine  46  (e.g., instructions) programmed to compute reconstructed electrophysiological signals for locations on the surface of interest within the patient’s body  12 . In one example, the reconstruction engine  46  computes the reconstructed signals (e.g., electrical potentials) on the surface of interest by executing machine-readable instructions (e.g., an algorithm) to reconstruct electrical signals spatially and temporally on to the surface of interest based on the electrophysiological data (e.g., measurements from the electrodes  14  and  16 )  28  and the geometry data  40  (e.g., including geometry data that is updated and/or refined, as described herein). As described herein, the geometry data  40  includes three-dimensional spatial information representing the surface (or surfaces) of interest describing a surface on to which reconstructed signals are computed (by engine  46 ) co-registered with respective locations where electrophysiological measurements are made (e.g., by the electrodes  14  and  16 ). The reconstruction engine  46  can calculate the reconstructed electrical signals on the surface of interest for one or more surfaces of interest over one or more time intervals. The time interval(s) may be selected through a user interface  48  in response to a user input entered by a user device  50  (e.g., mouse, keyboard, touchscreen interface, gesture interface or the like). 
     By way of example, the reconstruction engine  46  includes a transfer matrix generator  56  programmed to generate a transfer matrix  54  based on the geometry data  40 . As described herein, the geometry data  40  used by the transfer matrix generator  56  includes information describing anatomical geometry of the surface of interest and geometry of the respective electrodes  14  and  16 , which have been co-registered (e.g., by spatial registration function  42 ) in a common spatial coordinate system (e.g., with respect to patient anatomy). The anatomical geometry may be implemented as a mathematical model (e.g., a spline or mesh or point cloud) that defines locations (e.g., a point cloud) across the surface of interest. The electrode geometry can also be implemented as a model that defines spatial coordinates of the electrodes in the common coordinate system. As described herein, the electrode geometry of the invasive electrode  14  may change reflect movement of invasive electrode  14 . The reconstruction engine  46  thus can employ transfer matrix  54  and the electrophysiological data  28  to reconstruct electrophysiological signals on the surface of interest. 
     As a further example, the reconstruction engine  46  includes code programmed to implement the method of fundamental solutions (MFS), shown as  52 . The reconstruction engine  46  thus employs the MFS  52  to solve the inverse problem for computing reconstructed electrical signals on the surface of interest. MFS  52  includes a mathematical representation that spatially relates an influence of the electrophysiological signals measured on the outer surface of the patient’s body and the electrophysiological signals measured within the patient’s body to the electrophysiological signals at one or more regions of interest around heart. In an example, the MFS method  52  can implement MFS ECGI similar to that disclosed in U.S. Pat. No. 7,983,743, which is incorporated herein by reference, and further modified to utilize the electrophysiological data that includes measurements from both the invasive and non-invasive electrodes  14  and  16 . Other useful examples of inverse algorithms that can be implemented by the reconstruction engine  46  to reconstruct include the boundary element method (BEM), such as disclosed in U.S. Pat. Nos. 6,772,004, and 9,980,660, each of which is incorporated herein by reference. The reconstruction engine  46  further may employ a regularization technique (e.g., Tikhonov regularization) to estimate values for the reconstructed electrical signals on the surface of interest. 
     The following example demonstrates use of MFS  52  to solve the inverse problem for reconstructing electrical signals on the surface of interest (e.g., as defined in geometry data  40 ), such as can be implemented by the reconstruction engine  46 . For example, the reconstruction engine  46  can employ MFS  52  to estimate a solution of partial differential equations as a linear combination of fundamental solutions of the Laplacian operator, which constitutes a Cauchy problem for Laplace’s equation having the following boundary conditions: 
     
       
         
           
             
               
                 
                   
                     
                       
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     In the context of MFS  52 , the reconstruction engine can express potentials at each location can be expressed as follows: 
     
       
         
           
             
               
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     As an example, suppose there are N unipolar electrodes located at x tj , j = 1, ..., N on the body surface, and K bipolar measurements from respective bipolar channels. Bipolar signals b(x hi ) are the difference of signals from two nearby electrodes located at x hi,1  and x hi,2 , where i=1,..., K. In one example, the transfer matrix generator  56  is programmed to generate the matrix  54  as a linear system of the inverse problem for bipolar channels as follows: 
     
       
         
           
             
               
                 
                   
                     
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     In the above system of equations, each body surface electrode  16  corresponds to two rows in the matrix above - one for potential measurement, and another for the boundary condition on the current. Each bipolar channel corresponds to one of the middle rows. For bipolar channels with stable locations like CS catheters, in which the location of electrodes  14  remains fixed, the corresponding rows in the matrix  54  likewise remain unchanged. For catheters configured to move the electrode  14  relative to the patient’s heart, the transfer matrix generator  56  is programmed to recalculate the matrix  54  so the corresponding rows representative of the electrode(s) location are updated when the catheter moves. In an example, the transfer matrix generator  56  is programmed to add rows corresponding to the new location of the electrode  14  based on the location data  24  provided by the navigation system  22 . The transfer matrix generator  56  can also be programmed to remove rows that correspond to the channels representing a previous location of the electrodes  14  in response to detecting that the electrode  14  has moved. The reconstruction engine  46  is thus programmed to reconstruct the electrophysiological signals on the surface of interest according to the MFS  52  which includes a mathematical representation (matrix  54 ) for electrophysiological signals measured by electrodes  16  at respective locations on the outer surface of the patient’s body and the electrophysiological signals measured by electrode  14  at a current location within the patient’s body. 
     In some examples, such as for repeated rhythms in which beats used across different regions correspond to the same cardiac phases, the transfer matrix generator  56  can be programmed to keep previous measurements with the previous catheter location(s) and corresponding rows of the matrix, and to add new rows for the new measurements. Examples of repeated rhythms that can utilize this approach include normal sinus rhythms (NSR), AT or VT. The transfer matrix generator  56  can align the phases of the respective measurements with respect to body surface ECGs, such as via template matching methods. The body surface ECGs can be measured using the same electrodes or a subset thereof used to make the body surface measurements, or a separate set of ECG electrodes can be used in addition to the set of body surface electrode. 
     In another example, the transfer matrix generator  56 , can provide the matrix  54 , such as described above, to include respective unipolar channels for one or more invasive electrodes  14 , such as when the location of a reference channel is known. For example, if the unipolar channel uses the external reference as one or more body surface electrodes  16 , then the unipolar channels can simply correspond to potential measurements only, without imposing a boundary condition on current. If the unipolar channel uses an internal reference, then the transfer matrix generator  56  can create a row in the matrix in a similar way to that demonstrated above for bipolar channels, such as follows: 
     
       
         
           
             
               
                 
                   
                     
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         where: x cj  represents the unipolar location, 
         x uniref  represents the reference location, and 
         v(x cj ) represents the unipolar measurements. 
       
    
     As a further example,  FIG.  2    depicts an example annotated system of equations  80  to show which rows of the transfer matrix  54  and other matrices  82  and  84  in the system of equations correspond to which types of signal measurements. In particular, the system shows, at  86  correspondence between portions of the matrix relating to body surface unipolar signals. The system  80  also shows at  88  which portions correspond invasive bipolar signals. The system  80  further shows at  90  which portions correspond invasive unipolar signals. For example, in response to the invasive electrode(s)  14  being moved, portions  88  and  90  of the matrix  54  would be updated (e.g., by transfer matrix generator) to represent the relative influence of the invasive measurements, which can vary as a function of the location of the electrode  14  for respective measurement samples, on the inverse solution for reconstructing electrophysiological signals on the surface of interest. As described herein, for example, the invasive measurements can be on the heart wall, intramural and/or near the heart. 
     In some examples, the transfer matrix generator can be configured to update the transfer matrix in response to detecting that the electrode  14  (or catheter carrying the electrode) has moved by at least a predetermined distance (e.g., 1 cm, 0.5 cm or other set distance), which may be programmable in response to a user input. The movement of the electrode can be tracked based on a difference between the current location data  24  and the location data that was used to provide the geometry data for the last reconstructed signals (e.g., implemented by the reconstruction engine  46 ). In this way, small variations in movement (or errors within the navigation system) do not trigger recalculation of the transfer matrix  54 . 
     Returning to  FIG.  1   , the monitoring system  30  also includes an output generator  60  that is programmed to generate an EP map that can be rendered on the display  36  to graphically visualize the reconstructed electrical signals on the surface of interest. As disclosed herein, the surface of interest may be an epicardial surface, an endocardial surface, or a combination of an epicardial or endocardial surfaces. Additionally, the surface of interest can be a cardiac envelope, such as a surface residing between the center of a patient’s heart and the body surface where the electrodes are positioned. The surface of interest may encompass the entire cardiac surface or one or more regions (epicardial or endocardial) of interest, such as described herein. 
     The output generator  60  thus provides the output data  32  that may be provided to the display  36  to visualize one or more electrocardiographic maps as well as other electrical information derived from the reconstructed electrical signals. For example, the output generator  60  is programmed to generate an EP map based on the reconstructed signals (generated by reconstruction engine). By including reconstructed electrical signals (derived from both non-invasive measurements) and invasively measured signals in a respective map, the respective map can more accurately visualize cardiac electrophysiological signals. 
     In a further example, the output generator  60  is programmed to generate output data  32  based on a data analysis function  62 . The data analysis function  62  can include an electrical calculator function  64  and a spatial calculator function. Thus, the data analysis function  62  can perform calculations based on electrophysiological data  28 , based on geometry data  40  or based on a combination or electrophysiological and spatial information. 
     As an example, the electrical calculator  64  is programmed to identify a region of interest based on the electrophysiological signals that have been reconstructed on the surface of interest (e.g., by reconstruction engine  46 ). The output generator can be configured to specify the region of interest in output data based on the identified ROI. Additionally, the navigation control  44  can be configured to provide location information (e.g., spatial coordinates on the surface of interest) so the navigation system can provide further guidance to help a user move the electrode  14  to the ROI. Electrophysiological data  28  can be recorded by the electrodes  14  and  16  at each location within the ROI. The reconstruction engine  46  can reconstruct electrophysiological signals across the surface of interest based on the electrophysiological data recorded at each site for one or more time intervals. 
     The electrical calculator  64  can compare the reconstructed signals across the surface of interest over the one or more time intervals (e.g., multiple beats) to locate a site of of interest (e.g., a location having earliest activation or other electrophysiological parameter) on the surface based on electrophysiological signals measured by electrode  14  at different locations (recording sites) within the patient’s body within the ROI. In an example, the electrical calculator  64  can be further programmed to evaluate reconstructed electrophysiological signals on the surface of interest within the patient’s body and the electrophysiological signals measured by electrode  14  at the different locations within the patient’s body over a common time range to determine the site of interest. The site of interest may be determined to include an epicardial surface, an endocardial surface or at a location or region within the myocardium between epicardial and endocardial surfaces. 
     Electrical data that includes measurements made outside the ROI can be discarded or otherwise excluded from reconstruction and/or comparison by the electrical calculator  64 . The electrical data that includes measurements made outside the ROI can also be stored in memory for further analysis, such as if selected by the user interface  48  in response to a user input. 
     The data analysis  62  can further be configured to connect a site of rotational activity, fibrosis, and entrance or exit site for a ventricular tachycardia, or any region of electrophysiological interest with a corresponding location (a point or region) that can be further investigated with the invasive electrode(s)  14 . The output generator  60  further can generate an updated map based on reconstructed electrophysiological signals on the surface of interest within the patient’s body, including respective electrophysiological signals measured at the different locations within the patient’s body within the region of interest. 
     In another example, the spatial calculator  66  is programmed to identify a prescribed location within the patient’s body. The prescribed location can be identified in response to a user input specifying the location through the user interface  48 . Additionally or alternatively, the prescribed location can be identified based on information indicating an ambiguity in electrophysiological signals on the surface of interest at or near the prescribed location. The information indicating the ambiguity can be generated by data analysis function  62 , such as indicating low amplitude signals or differences among neighboring nodes within a region of interest or based on external information, such as from a medical images (e.g., cardiac echo, x-ray, CT scan or MRI). In an example, the output generator  60  can generate an output to instruct a user to place the invasive electrode at a prescribed location within the patient’s body in response to data. The localization of the prescribed location to the user can further be guided based on the location data  24  generate by the navigation system  22 , such as in response to instructions provided by navigation control  44 . In another example, the information indicating the ambiguity is determined based on an analysis of the electrophysiological signals measured on the outer surface of the patient’s body. Alternatively, or additionally, information indicating the ambiguity is determined based on the reconstructed electrophysiological signals or in response to a user input specifying an arrhythmia condition. 
     Additionally, the navigation control  44  can be programmed to cause the spatial registration function to update the geometry data  40  in response to detecting that the invasive electrode  14  is at or near the prescribed location within the patient’s body. For example, the navigation control function  44  is programmed to compute a distance between a position of the electrode  14 , which has been spatially registered (by spatial registration function  42 ) with patient anatomy (provided in geometry data), and the prescribed location. The distance can be computed by a number of pixels along a line connecting the electrode and the prescribed location, which can be translated to a physical distance. Alternatively, the navigation system  22  can be programmed to track the distance between the electrode and the prescribed location. The reconstruction engine  46  can then reconstruct the electrophysiological signals on a surface of interest based on the updated geometry data  40  and the electrophysiological data  28 , including the electrophysiological signals measured on the outer surface of the patient’s body and the electrophysiological signals measured at or near the prescribed location. 
     In another example, the data analysis function  62  is programmed to implement a machine learning (ML) model (e.g., machine-readable instructions executable by a processor)  70  pre-trained to automatically detect and classify one or more types of arrhythmias, such as premature ventricular contraction (PVC), premature atrial contraction (PAC), focal AT, re-entrant AT or others. For example, the ML model  70  can include of an artificial neural network (ANN) algorithm, a support-vector machine (SVM) algorithm, a decision tree algorithm, a recurrent neural network (RNN) algorithm, and a convolutional neural network (CNN) algorithm. Other types of machine learning can be used in other examples. The ML model  70  can be trained on prior identified electrocardiographic maps of interest from electrophysiological signals that represent one or more known categories of arrhythmias that occur during a respective time interval or a series of time intervals. The ML model  70  can be configured to process the reconstructed signals or resulting graphical maps, consistent with how the ML model is trained to identify respective trained categories of arrhythmia events. As a further example, the ML model  70  can be configured to evaluate respective input maps generated for a patient and label respective maps that are highly correlated (e.g., similar) to known training data to classify one or more categories of arrhythmias. 
       FIGS.  3 - 9    demonstrate an example of how machine learning can be implemented to detect automatically a given arrhythmogenic condition based on analysis of reconstructed electrical signals on a surface of interest (e.g., an ECGI map). In the example of  FIGS.  3 - 9   , the ML model  70  is trained to identify premature ventricular contractions (PVCs); however, the approach disclosed herein is equally applicable to detect and classify other types of arrhythmias. Each of  FIGS.  3 ,  4  and  5    show respective maps having different views that have been generated (e.g., by reconstruction engine  46 ) and are known to exhibit PVCs. The PVC can be detected from a standard 12 lead ECG or another method. The maps generated for PVCs, such as shown in  FIGS.  3 ,  4  and  5   , thus include the time interval(s) when the known PVC event(s) occurs and define part of a set (e.g., library) of PVC images. For example,  FIG.  3    depicts a case where the PVC occurs at the right coronary cusp (RCC).  FIG.  3    shows three maps of a patient’s heart from different angles including the left anterior oblique (LAO) view  302 , the basal view  304  and inferior view  306 .  FIG.  3    also shows an enlarged plane view of the RCC at  308 .  FIG.  4    depicts an example of a PVC occurring at the right ventricular outflow tract (RVOT) and shows different map views, including LAO view  310 , the basal view  312  and inferior view  314 . An enlarged plane view of a map of the RVOT is also shown at  316 .  FIG.  5    depicts an example of maps when a PVC occurs at or near the great cardiac vein (GCV).  FIG.  5    includes maps from different views including the LAO view  320 , the basal view  322  and inferior view  324 . An enlarged plane view of a map of the GCV is also shown at  326 . 
     Multiple images of ECGI maps for PVCs that occur at specific locations can be collected and stored in library, such as the represented by the library  400  of  FIG.  6    for GCV, LVOT and RVOT categories of PVC. As shown in  FIG.  7   , the ECGI image data sets from  FIG.  6    can be partitioned into training and testing data sets for each category of PVC, such as shown at  450 . The respective data sets can then be used to train the ML model to detect and classify each category of PVC.  FIG.  8    depicts an example of a confusion matrix, showing an accuracy of about 83%. There typically will be greater numbers of images than shown in the examples of  FIGS.  6  and  7    for use as training and testing data to generate the ML model and, the accuracy is expected to improve with a greater number of training and testing data. 
       FIG.  9    shows output ECGI maps and confidence scores predicted for respective categories of PVCs (e.g., GCV, LVOT and RVOT) based on applying the trained model. 
       FIG.  10    is a flow diagram that illustrates an example method  1000 . While for purposes of simplicity of explanation, the example method of  FIG.  10    is shown and described as executing serially, the example method  1000  is not limited by the illustrated order, as some actions could in other examples occur in different orders, multiple times and/or concurrently from that shown and described herein. Additionally, the method  1000  can be implemented as machine-readable instructions executed by a processor, such as by the mapping system  30 . Accordingly, the description of  FIG.  10    also refers to  FIG.  1   . 
     At  1002 , the method includes storing location data for at least one invasive electrode that is movable within a patient’s body. For example, navigation system  22  is configured to provide the location data representing the location of the invasive electrode in three-dimensional space. As disclosed herein, the at least one invasive electrode can be configured to move within the patient’s body. 
     At  1004 , the method includes storing electrophysiological measurement data representing the electrophysiological signals measured at the outer surface of a patient’s body by body surface electrodes as well as within the patient’s body by the at least one invasive electrode. For example, the signal measurement system  18  is configured to provide the electrophysiological measurement data  28 , such as disclosed herein. 
     At  1006 , the method includes storing geometry data representing anatomy of the patient spatially, and locations of the respective body surface electrodes and the at least one invasive electrode in three-dimensional space. The geometry data for the at least one invasive electrode is variable based on movement of the at least one invasive electrode within the patient’s body. For example, spatial registration function  42  can register the location data with the patient anatomy and electrode locations to provide the geometry data. In an example, a probe to which the invasive electrode is coupled, can be configured to move within the patient’s body, and the navigation/localization system updates the location data to represent a current location of the invasive electrode in the three-dimensional space. The signal processing circuitry  26  is similarly configured to update the electrophysiological measurement data to represent electrophysiological signals measured at the current location. In some examples, the probe or catheter to which the invasive electrode is coupled also includes an integrated imaging device, such as described herein. The optical imaging device can acquire images representing the anatomy of the patient, and the geometry data can be updated to refine the surface of interest or other anatomical structure within the patient’s body based on the imaging data. 
     At  1008 , the method includes reconstructing electrophysiological signals on a surface of interest within the patient’s body based on the electrophysiological measurement data and the geometry data. For example, reconstruction engine  46  is configured to implement MFS  52  to reconstruct the electrophysiological signals on the surface of interest. MFS includes a mathematical representation (e.g., including matrix  54 ) that spatially relates (or characterizes) an influence of the electrophysiological signals measured on the outer surface of the patient’s body and the electrophysiological signals measured within the patient’s body to the electrophysiological signals on the surface of interest. 
     At  1010 , the method includes detecting movement of the invasive electrode. For example, movement can be detected in response to navigation control  44  (or another function) determining a change in the spatial location of the invasive electrode. From  1010 , the method returns to  1002  to update respective location data, electrophysiological measurement data and geometry data responsive to the current/new location of the invasive electrode. Based on the updated data, the method  1000  can reconstruct electrophysiological signals on the surface of interest within the patient’s body. 
     For example, in response to the location data being updated from a previous location to the current location, a portion of the mathematical representation (matrix  54 ) can be removed for electrophysiological signals measured at the previous location and another portion be added to represent the current location where the electrophysiological signals are measured to provide an updated mathematical representation. The method can then reconstruct the electrophysiological signals on a surface of interest according to MFS with the updated mathematical representation. 
     The method  1000  further can implement other functions and features disclosed herein. For example, the method  1000  can use machine learning (ML model  70 ) to detect and classify one or more categories of arrhythmias. The method can also identify a region of interest based on analysis (e.g., by data analysis function  62 ) of the reconstructed electrophysiological signals on the surface of interest to locate a site of interest based on an analysis of signals measured at different locations within the patient’s body within the region of interest. Examples that can be used for the site of interest include a site of earliest activation, a site of rotational electrical activity, a fibrosis, and entrance and/or exit site for a ventricular tachycardia, a previously ablated location, and a region with heterogeneous depolarization or repolarization. Other sites of interest to the user can also be used in other examples. The site of identified site of interest can be connected spatially with a corresponding location on the surface of interest to describe an electrophysiological circuit associated with the site of interest. An updated map can be generated based on reconstructed electrophysiological signals on the surface of interest within the patient’s body, including respective electrophysiological signals measured at the different locations within the patient’s body within the region of interest. 
     The method  1000  can also generate an output (e.g., graphical or other form of guidance) to instruct a user to place the at least one invasive electrode at a prescribed location within the patient’s body in response to data indicating an ambiguity or another condition detected for electrophysiological signals on the surface of interest at or near the prescribed location. The geometry data can be updated (at  1006 ) in response to detecting that the invasive electrode is at or near the prescribed location within the patient’s body, and the electrophysiological signals reconstructed on the surface of interest can be updated based on the updated geometry data and the electrophysiological data. The data indicating the ambiguity or other condition can be determined based on an analysis of the electrophysiological signals measured on the outer surface of the patient’s body, the reconstructed electrophysiological signals or in response to a user input specifying an arrhythmia. 
     It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device. 
     In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer). 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.