Patent Publication Number: US-2023148936-A1

Title: Electrocardiographic imaging using patch electrodes

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of priority to U.S. provisional patent application No. 63/278,634, filed 12 Nov. 2021, which is incorporated herein by reference in its entirety. 
    
    
     FIELD 
     The present technology is generally related to systems and methods of electrocardiographic imaging using patch-type electrodes. 
     BACKGROUND 
     Electrocardiographic imaging (ECGI) is a non-invasive multi-lead ECG-type imaging tool that combines non-invasive electrical measurements with three-dimensional geometry of the heart and torso to reconstruct electrical signals onto the heart or another surface of interest. Mathematically, this is performed by solving the inverse problem. In one example, electrodes are implemented on a vest, and require medical imaging of the patient while wearing the vest prior to conducting an electrophysiology study. In addition to the relatively high cost of the vest, the preparation, collection and processing of preliminary data before the electrophysiology study tend to time consuming and require a high level of expertise. 
     SUMMARY 
     The techniques of this disclosure generally relate to electrocardiographic imaging using patch-type electrodes. 
     In one aspect, the present disclosure provides a system that includes an arrangement of body surface electrodes on one or more patches adapted to be placed an outer surface of a patient&#39;s body. A computing apparatus includes non-transitory memory to store data and instructions executable by a processor thereof. The data includes anatomical geometry data, electrode geometry data and electrical data. The anatomical geometry data represents anatomy of the patient, which includes at least a portion of a heart and the outer surface of the patient&#39;s body, in three-dimensional space. The electrode geometry data represents locations of respective body surface electrodes in three-dimensional space. The electrode geometry data can be derived from at least one of (i) electrical signals measured by at least some of the electrodes, or (ii) a template describing the locations of respective body surface electrodes as applied to the patient. The electrical data represents electrophysiological signals measured by the electrodes. The instructions can be programmed to register the anatomical geometry data and the electrode geometry data to provide co-registered geometry data representing the anatomy of the patient and the locations of the body surface electrodes in a common three-dimensional space. Electrophysiological signals can be reconstructed on a cardiac envelope of the heart based on the co-registered geometry data and the electrical data. 
     In another aspect, the disclosure provides one or more non-transitory computer-readable media having instructions, which when executed by a processor, perform a method. The method can include emphasizing electrophysiological signals for a subset of measurement channels, corresponding to electrophysiological signals measured non-invasively by respective electrodes. The method can also include storing electrical data to represent the emphasized electrophysiological signals for the subset of measurement channels together with other electrophysiological signals for other measurement channels measured non-invasively by other electrodes. The method can also include reconstructing electrophysiological signals on a cardiac envelope of a heart based on co-registered geometry data and the electrical data including the emphasized electrophysiological signals. The co-registered geometry data represents the anatomy of the patient and spatial locations of the respective electrodes in a common three-dimensional space, and the electrical data represents electrophysiological signals measured non-invasively by electrodes corresponding to respective measurement channels. 
     In yet another aspect, the disclosure provides one or more non-transitory computer-readable media having instructions, which when executed by a processor, perform a method. The method includes selecting a proper subset of a plurality of measurement channels, the measurement channels corresponding to arrangement of electrodes on one or more patches adapted to measure electrophysiological signals on an outer surface of a patient&#39;s body. The method also includes storing electrical data to represent the electrophysiological signals measured for the selected subset of measurement channels. The method also includes reconstructing electrophysiological signals on a cardiac envelope of a heart based on co-registered geometry data and the electrical data for the selected subset of measurement channels, in which the co-registered geometry data represents the anatomy of the patient and spatial locations of the body surface electrodes in a common three-dimensional space. The method can repeat the selecting, storing and reconstructing for a plurality of subsets of electrodes to provide respective sets of reconstructed electrophysiological signals. 
     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 to generate electrocardiographic maps of cardiac electrical activity. 
         FIGS.  2 ,  3 ,  4  and  5    depict an arrangement of patches carrying respective electrodes distributed across a model of a patient&#39;s torso. 
         FIG.  6    is a block diagram that illustrates an example of an electrode geometry calculator. 
         FIG.  7    is a block diagram that illustrates another example of an electrode geometry calculator. 
         FIG.  8    is a block diagram that illustrates an example of a mapping system that includes reconstructed signal analysis. 
         FIG.  9    is a block diagram that illustrates an example of a mapping system that includes non-invasive signal analysis. 
         FIGS.  10 A,  10 B,  10 C,  10 D and  10 E  depict examples electrophysiological signals showing a comparison of signal waveforms, a set of patch electrodes and graphical maps provided for a full set of body surface electrodes and the set of patch electrodes. 
         FIGS.  11 A and  11 B  depict examples of graphical potential maps for a cardiac envelope derived from signals measured by a full vest and patch electrodes, and  FIG.  11 C  depicts EGM waveforms for a given point on a cardiac envelope on the cardiac envelopes of  FIGS.  11 A and  11 B . 
         FIGS.  12 A- 17 B  are examples of different patch electrode configurations. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure relates to using one or more patch-type electrodes for measuring electrophysiological signals from an outer surface of a patient&#39;s body. As described herein, the use of patch-type electrodes facilitates the workflow for performing ECGI and reduces the cost—both human and material costs—so ECGI can be used on a more widespread basis. As an example, the use of patch-type electrodes enables additional analyses to be performed on electrophysiological signals such as to identify input channels having a given sensitivity to low amplitude or other signal characteristics. As a result, information from such channels having low signal amplitudes or poor signal quality can be retained and analyzed, as described herein. This is in contrast to existing approaches where channels having low amplitudes or poor signal quality are usually removed from ECGI processing or otherwise diluted due to relative poor signal quality or low amplitude compared to other channels. 
     Additionally or alternatively, in some examples, systems and method disclosed herein can be configured to analyze reconstructed electrophysiological signals to identify one or more anatomical regions exhibiting a desired physiological characteristic. For example, reconstructed signals at respective anatomical locations on a cardiac envelope (e.g., the patient&#39;s heart) can be analyzed over time, such as in response to a surgical or other intervention. Signals exhibiting variations in response to the and/or during the intervention can be identified and emphasized (e.g., by weighting of respective signals) for re-computing the reconstructed signals on a cardiac envelope. 
       FIG.  1    is a block diagram that illustrates a system  100  for measuring and mapping electrophysiological activity, such as electrocardiographic maps of cardiac electrical activity for a patient&#39;s heart. For example, the system  100  includes a mapping system  102  configured to generate output data  104 , which may be used to render a graphical map (e.g., a map on a heart model)  106  and/or display processed electrical signals on a display  108 . The system  100  can also provide information in other formats to provide guidance to the user representative of and/or derived from electrical activity that may be measured non-invasively, invasively or a combination of invasively and non-invasively measured electrical signals. 
     In the example of  FIG.  1   , the system  100  includes a plurality of patches  110 , each having an arrangement of two or more electrodes configured to measure cardiac electrical activity non-invasively from an outer surface of the patient&#39;s body on which the patches are placed. For example, each patch  110  can include an arrangement of electrodes distributed across a web of flexible material (e.g., a panel or sheet) that is conformable to the contour of the patient&#39;s body surface where the patch is positioned. The size of the panel for each patch can vary depending on the number and spatial distribution of electrodes carried by the panel. For example, the patch can include a circular or rectangular arrangement of electrodes disposed on a web of flexible material, and the distribution can be uniform or non-uniform according to sensing requirements. An electrically conductive wire or other form of conductor can carry electrical measurement signals from electrodes on respective patches to a measurement system  112 , or alternately, the signals can be transferred over optical fibers or wirelessly. Examples of patch-type sensors that could be used as the patches  110  in the system of  FIG.  1    are shown in  FIGS.  2 - 5 ,  10 A- 10 B, and  12 A- 17 B . Other patch-type sensors could be used in other examples based on this disclosure. 
     The measurement system  112  can include corresponding controls  120  configured to provide electrophysiological measurement data (also referred to herein as electrical data)  122  that describes electrophysiological activity (e.g., ECG signals) detected by electrodes of the respective patches  110 . For example, signal processing circuitry (e.g., analog-to-digital conversion circuitry) of the measurement system  112  is configured convert measured analog measured electrophysiological signal to corresponding digital electrophysiological signals represented in the electrophysiological measurement data  122 . The control  120  can also be configured to control the data acquisition process for measuring electrical activity and providing the measurement data  122  (e.g., at a predefined sampling rate). 
     As a further example, the patches  110  can be distributed over a portion of the patient&#39;s torso (e.g., thorax) to position respective electrodes at body surface locations for measuring electrical activity originating within the patient&#39;s heart  114 . Because the patches are separate, a user can select and configure desired arrangements and numbers of patches  110  to position respective electrodes accordingly. For example, electrodes of the selected set of patches can cover the entire thorax. In another example, a set of patches may reside over a selected portion of the patient&#39;s torso (leaving one or more parts of the torso free of patches), such as designed for measuring electrical activity for a particular purpose (e.g., an array of electrodes specially designed for analyzing atrial fibrillation and/or ventricular fibrillation), to provide room for additional equipment and devices (e.g., defibrillator pads, 12-lead ECG or the like) and/or for monitoring a predetermined spatial region of the heart. 
     In some examples, the system  100  includes one or more sensors that may also be located on a device  116  that is adapted to be inserted into the patient&#39;s body  118  and to measure electrophysiological signals invasively, which can be provided to the measurement system  112  and stored as part of the electrical measurement data  122 . For example, a catheter, having one or more therapy delivery devices  116  affixed thereto can be inserted into the body  118  as to contact the patient&#39;s heart  114 , endocardially or epicardially. Various types and configurations of therapy delivery devices  116  can be utilized, which can vary depending on the type of treatment and the procedure. For instance, the therapy device  116  can be configured to deliver electrical therapy, chemical therapy, sound wave therapy, thermal therapy or any combination thereof. 
     By way of further example, the therapy delivery device  116  can include one or more electrodes located at a tip of an ablation catheter configured to generate heat or other energy form for ablating tissue in response to electrical signals (e.g., radiofrequency energy) supplied by a therapy system  124 . In other examples, the therapy delivery device  116  can be configured to deliver cooling to perform ablation (e.g., cryogenic ablation), to deliver chemicals (e.g., drugs), ultrasound ablation, high-frequency radiofrequency ablation, pulsed field ablation, non-invasive ablation or a combination thereof. In still other examples, the therapy delivery device  116  can include one or more electrodes located at a tip of a pacing catheter to deliver electrical stimulation, such as for pacing the heart, in response to electrical signals (e.g., pacing current pulses) supplied by the therapy system  124 . Other types of therapy can also be delivered via the therapy system  124  and the invasive therapy delivery device  116  that is positioned within the body. 
     As a further example, the therapy system  124  can be located external to the patient&#39;s body  118  and be configured to control therapy that is being delivered by the device  116 . For instance, the therapy system  124  includes a control system (e.g., hardware and/or software)  126  that can communicate (e.g., supply) electrical signals via a conductive link electrically connected between the delivery device (e.g., one or more electrodes)  116  and the therapy system  124 . The control system  126  can control parameters of the signals supplied to the device  116  (e.g., current, voltage, repetition rate, trigger delay, sensing trigger amplitude) for delivering therapy (e.g., ablation, stimulation, etc.) via electrode(s) of the therapy device  116  to one or more locations of the heart  114 . The control system  126  can set the therapy parameters and apply stimulation based on automatic, manual (e.g., user input) or a combination of automatic and manual (e.g., semiautomatic controls). One or more sensors (not shown) of the device  116  can also communicate sensor information back to the therapy system  124 . The position of the device  116  relative to the heart  114  can be determined and tracked intraoperatively via an imaging modality (e.g., fluoroscopy, X ray), a mapping system  102 , direct vision, a localization system or the like. The location of the device  116  and the therapy parameters thus can be combined to determine corresponding therapy delivery parameters. 
     In each of such example approaches for acquiring electrical information from the patient&#39;s body  118 , including invasively, non-invasively, or a combination of invasive and non-invasive sensing, the electrodes on the patches  110  provide the sensed electrophysiological information to the measurement system  112 . In some examples, the control  120  can control acquisition of measurement data  122  separately from operation of the therapy system  124  (if implemented), such as in response to a user input. In other examples, the measurement data  122  can be acquired in coordination with (e.g., concurrently or otherwise synchronized with) delivering therapy by the therapy system  124 , such as to detect electrical activity of the heart  114  that occurs in response to applying a given therapy (e.g., according to therapy parameters). For instance, appropriate time stamps can be utilized for indexing the temporal relationship between the respective measurement data  122  and other data that is used by the mapping system (e.g., therapy system  124 ) as to facilitate the evaluation and analysis thereof. 
     The mapping system  102  is programmed to combine the electrophysiological measurement data  122 , corresponding to measured electrophysiological signals of the heart  114 , with geometry data  130  to generate the output data  104 . As described herein, for example, the output data  104  can represent or characterize electrophysiological signals on a surface of interest (e.g., a cardiac surface or other surface envelope on or within the heart  114 ). The mapping system  102  may further generate the output data  104  to represent information, including a representation of cardiac signals on a surface of interest, based on the electrophysiological measurement data  122  and the geometry data  130 , as disclosed herein. 
     To enable the mapping system  102  to generate such output data, the geometry data  130  includes electrode geometry data  132  and anatomical geometry data  134 . For example, the system  100  is further programmed derive the electrode geometry data  132  to represent locations of respective body surface electrodes in three-dimensional space based on (i) electrical signals measured by at least some of the electrodes on the patches  110 , and/or (ii) a template describing the locations of respective body surface electrodes as applied to the patient&#39;s body  118 . In another example, where electrodes are distributed across each of the patches  110  in a known spatial configuration and a spatial geometry of each of the patches is known (e.g., by localization) with respect to the patient&#39;s body  118  in a three-dimensional coordinate system, the electrode geometry data  132  can be readily extrapolated from the spatial geometry of the patches. In other examples, different approaches can be implemented to derive the electrode geometry data  132 , such as described herein (see, e.g.,  FIGS.  6  and  7   ). 
     The anatomical geometry data  134  is generated to represent spatial geometry of the surface of interest of the patient in three-dimensional space. The anatomical geometry data  134  can be derived from imaging data acquired by a medical imaging modality and/or a user-specific template describing the anatomy of the patient. For example, an anatomical model can be constructed based on imaging data obtained (e.g., by a medical imaging modality) for the patient and provide spatial coordinates for the patient&#39;s heart  114  and, in some cases, the outer surface of the patient&#39;s body where the patches  110  are or will be positioned. The medical imaging data can be generated for the patient&#39;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 geometry data  132  and the anatomical geometry data  134  can be registered in a common three-dimensional spatial coordinate system. 
     In another example, the anatomical geometry data  134  may be provided by a template, such as including a look-up table (e.g., implemented as part of the mapping system  102  or as separate program code) in response to a user input. For example, the look-up table is configured as a template to provide an estimate of anatomical geometry data geometry in response to input data entered by the user describing one or more patient parameters. The patient parameters, for example, can include patient physical characteristics, medical information and the like, such as a combination of two or more of a patient body mass index, a measure of chest size, gender, height and weight, age (or other demographics), or known arrhythmias (e.g., determined from an ECG or other diagnostic tool). Additionally, in other examples, the anatomical geometry data  134  can be determined from imaging data acquired by a medical imaging modality individually or in combination with a user-specific template describing the physical characteristics of the patient. 
     As described above, the mapping system  102  is configured to generate output data  104 , which can be used to render a graphical map  106  and/or present other information on the display  108 . For example, the mapping system  102  includes an electrogram reconstruction function (e.g., machine-readable instructions)  136  programmed to compute an inverse solution and provide corresponding reconstructed electrograms across a surface of interest based on the electrophysiological measurement data  122  and the geometry data  130 . The reconstructed electrograms thus can correspond to electrocardiographic activity across a surface of interest, such as a cardiac envelope, and can include static (three-dimensional at a given instant in time) and/or be dynamic (e.g., four-dimensional map that varies over time). Examples of inverse algorithms that can be implemented by the EGM reconstruction  136  include those disclosed in U.S. Pat. Nos. 7,983,743 and 6,772,004. The EGM reconstruction  136  can implement other inverse algorithms in other examples. The EGM reconstruction  136  thus is programmed reconstruct the body surface electrical activity measured via the sensor array  114  onto a multitude of locations on the surface of interest (e.g., greater than 1,000 locations, such as about 2,000 locations or more on a cardiac surface). Additionally or alternatively, the mapping system  102  can compute electrical activity over a sub-region of the heart based on electrical activity measured invasively, such as via a basket catheter or other form of measurement probe (e.g., on or attached to device  116 ). 
     In some examples, the mapping system  102  includes an electrical measurement analysis and modifier function (e.g., machine-readable instructions)  138 . For example, the function  138  is programmed to modify values of electrophysiological signals for a subset of the electrodes (corresponding to respective input channels) to emphasize or de-emphasize respective signals measured over a time interval. In an example, the subset of electrodes (e.g., input channels) can be chosen to include electrodes that are part of one or more patches  110 , and may be selected automatically or in response to a user input (e.g., user input provided by a graphical user interface (GUI)  140 ). In another example, the subset of electrodes (e.g., input channels) can be selected based on the function  138  analyzing one or more characteristics (e.g., amplitude) the signals. The function  138  can be programmed to perform a comparative analysis among all input channels to identify which channels exhibit a prescribed signal characteristic over one or more time intervals. Alternatively or additionally, the function  138  can perform a comparative multiple intervals for each respective signal channel to identify which channels exhibit changes in respective signals between two more time intervals, such as including low amplitude signal changes. The function  138  further can a machine learning model trained to determine the most likely subset of electrodes to optimize the solution for a given sensing application. For example, the machine learning model implemented by function  138  can be programmed to identify a configuration and arrangement of patch electrodes adapted to sense a user-specified type of arrhythmia (e.g., tachycardia, bradycardia, premature contractions, etc.). Additionally, or alternatively, the machine learning model implemented by function  138  can be programmed to identify a configuration and arrangement of patch electrodes adapted to sense electrophysiological signals at one or more regions of interest on the cardiac surface. For example, the machine learning model implemented by the function  138  can utilize one or more types of models, including support vector machines, regression models, self-organized maps, k-nearest neighbor classification or regression, fuzzy logic systems, data fusion processes, boosting and bagging methods, rule-based systems, artificial neural networks or convolutional neural networks. 
     As a further example, the function  138  is programmed to modify electrophysiological signals so that signals for a subset of the channels in the electrical data are emphasized relative to the other channels. That is, the electrical data  122  is modified to represent emphasized versions of measured electrophysiological signals for the subset of channels and other (non-emphasized or normal) measured electrophysiological signals for other channels on the body surface. Thus, the reconstruction engine  136  can be programmed to compute reconstructed signals on the cardiac surface of interest (by solving the inverse problem) based on the co-registered geometry data  130  and modified electrical data  122 , including the emphasized electrophysiological signals for the subset of channels. As a result of using emphasized signals during inverse reconstruction, the influence of the signals provided by the subset of channels can likewise be accentuated in the reconstructed electrograms. This can include programming the function  138  to accentuate a subset of channels having low amplitude signals (e.g., lower than a threshold) and/or channels exhibiting small signal changes or other signal characteristics over time that usually would be obfuscated by the influence of other (e.g., more pronounced) signals that are measured by electrodes distributed across the body surface. Additionally, or alternatively, the function  138  can be programmed to accentuate a subset of channels for signals having amplitudes exceeding a threshold) but exhibit changes that are obfuscated by the influence of a greater number of other channels other having signals that exhibit no change. Further information about function  138  is disclosed with respect to  FIG.  8   . 
     The mapping system  102  can also include a reconstructed EGM analysis and control function (e.g., machine-readable instructions)  142 . In an example, the function  142  is programmed to select a proper subset of channels for the plurality of electrodes, and the EGM reconstruction function  136  calculates an inverse solution to reconstruct the electrophysiological signals on the cardiac envelope based on the subset of selected channels over one or more time interval. The functions  142  and  136  can repeat the process of selecting input channels and calculating the inverse solution for a plurality of different subsets of channels to provide respective sets of reconstructed electrophysiological signals over the same one or more time intervals. The proper subset of channels can be randomly or pseudorandomly selected from the plurality of electrodes. Alternatively or additionally, some or all channels can be selected for a subset in response to a user input (e.g., using GUI  140 ) selecting which channels to include or exclude from a given channel subset. The number of channels selected can be the same for each subset or different numbers of channels can be used, such as ranging from one to a subset that includes all channels. Because a different subset of channels are used to generate each respective set of reconstructed electrophysiological signals (over the same time interval(s)), each computed set of reconstructed electrophysiological signals can represent electrical activity across the same surface of interest (the entire heart or a region of interest) in manner that can vary based on different arrangements of channels influencing the resulting reconstructed electrophysiological signals when the inverse solution is calculated. As a result, the electrogram reconstruction function  136  can generate different sets of reconstructed electrophysiological signals for a given time interval based on non-invasive electrical measurement data for different subsets of channels over the given time interval. The results reconstructed electrophysiological signals thus can show respective conditions or characteristics across the patient&#39;s heart and/or for a particular region of interest that can vary according to the relative influence of the respective input channels used for each reconstruction. For example, a number of patches having associated electrodes and the arrangement of such patches thus can be placed on the outer surface of a patient&#39;s body particularly adapted to sense a specific arrhythmia condition (e.g., atrial fibrillation, premature ventricular contraction (PVC), atrial tachycardia, ventricular tachycardia, or the like) for a given patient. 
     Because the patches and electrodes can be customized in this or another manner consistent with this disclosure, fewer patches and electrodes can be used in certain circumstances without reducing (and possibly increasing) the signal-to-noise ratio (SNR). The reduction in the number of electrodes further can reduce the overall cost to the patient. 
     The mapping system  102  also includes an output generator  144  to provide the output data  104  to visualize a graphical map  106  or other information on the display  108  based on measurement data  122  and the geometry data  130  for one or more time intervals. Some examples of output displays that can be provided by the output generator  144  include graphical representations of measured electrophysiological signals based on the data  122  and/or graphical maps of reconstructed electrophysiological signals, such as disclosed with respect to  FIGS.  10 D- 10 E . The output generator  144  may also generate the output data  104  to display other types of information, such as time-domain plots, frequency-domain plots or the like. As disclosed herein, the graphical maps and other information can be computed based on electrical data that is acquired non-invasively via one or more electrodes located on patches  110  distributed across the surface of the patient&#39;s body  118 . The time interval for which the output data/maps are computed can be selected based on user input, such as via the GUI  140 . Additionally or alternatively, the selected time intervals can be detected and synchronized with the application of therapy by the therapy system  124 . 
     Additionally, the output data  104  can be utilized by the control  126  of the therapy system  124  in examples that include the therapy system in the system  100 . For example, the therapy control  126  can be fully automated control, semi-automated control (partially automated and responsive to a user input via GUI  140 ) based on the output data  104  or manual control. In some examples, the control system  126  for the therapy system  124  can utilize the output data  104  to control one or more therapy control parameters. As an example, the control  126  can control delivery of ablation therapy to a site of the heart (e.g., epicardial or endocardial wall) based on the analysis of electrical measurement data (by function  138 ) or the analysis of reconstructed electrophysiological signals (by function  142 ), such as disclosed herein. For instance, the delivery of therapy can be terminated automatically in response to detecting the absence of a previously detected arrhythmogenic condition. In other examples, an individual user can view the map generated in the display to manually control the therapy system based on information in the graphical map  106  that is visualized on the display  108 . Other types of therapy and devices can also be controlled based on the output data  104 . 
     As a further example, the system  100  has applications throughout various phases of patient care. As an example, the system can be used as part of a patient screening process, such as part of a patient risk stratification process (e.g., part of a diagnostic and/or treatment planning procedure). Additionally, the system  100  can be utilized as part of a treatment procedure, such as to determine parameters for delivering a therapy to the patient (e.g., delivery location, amount and type of therapy by one or more therapy system  124 ). The system  100  further may be used to perform post-treatment evaluation of the patient. 
       FIGS.  2 ,  3 ,  4 , and  5    show different views of patches  200  having respective electrodes  202  distributed across an outer surface of a patient&#39;s thorax  204 . In particular,  FIG.  2    is a front view,  FIGS.  3  and  5    are side views, and  FIG.  4    is a back view. In the example of  FIGS.  2 ,  3 ,  4   , and  5 , each of the patches is shown as having a generally circular shape and are spaced evenly across the thorax  204  so as to position the electrodes  202  uniformly across the thorax. As shown, each patch  200  can include seven electrodes  202 , including one central electrode and six along a periphery of the patch spaced a common distance from the central electrode. Different numbers and arrangement of electrodes  202  can be used in other examples. Also shown in  FIGS.  2 ,  3 ,  4 , and  5    a number of free electrodes  202  are positioned on the thorax  204 , which free electrodes are not attached to or implemented on respective patches. Free electrodes can be used to place electrodes at one or more respective locations where it is desired to position fewer electrodes than a given patch includes. In other examples, different configurations of patches can be used, in which each patch configuration has a respective different number of electrodes, but may include a common distribution and arrangement of electrodes. In another example, different patch configurations can have include a different distribution and arrangement of electrodes, such as to provide application-specific patch configurations. In each configuration, the relative spatial position and geometry of electrodes across each respective patch is known. Thus, by knowing or determining a relative spatial position of a given patch on a patient&#39;s body, the spatial position of respective electrodes on the patient&#39;s body can likewise be known or readily determined and stored as electrode geometry data (e.g., data  132 ). 
       FIG.  6    is a block diagram showing an example of an electrode geometry calculator  300  programmed to provide geometry data  302 , including electrode geometry data  304 . The electrode geometry data  304  can correspond to electrode geometry data  132  implemented in the system  100  of  FIG.  1   , and the description of  FIG.  3    also refers to  FIG.  1   . 
     In an example, the electrode geometry calculator  300  is programmed to determine the electrode geometry data based on navigation data  306 , probe signal data  308 , electrode location data  310  and non-invasive electrical measurement data  312 . For example, an invasive system  314  is configured to provide the navigation data  306  and probe signal data  308 . The navigation data  306  can represent spatial coordinates (e.g., three-dimensional coordinates) of a probe that is positioned within the patient&#39;s body. A non-invasive system  316  is configured to provide the electrode location data  310  and electrical measurement data  312 . In some examples, the probe signal data (e.g., representing parameters of an applied waveform)  308  and the electrode location data (e.g., representing a spatial relationship among electrodes)  312  can be fixed and known a priori. 
     As an example, a navigation system (e.g., part of invasive system  314 ) can be configured to provide the navigation data  306  to represent the position of a probe within a patient&#39;s body. Examples of cardiac navigation systems that can be implemented to provide the navigation data include the EnSite NavX navigation and visualization system (commercially available from Abbott), the CARTO cardiac mapping system (commercially available from Johnson &amp; Johnson), the NOGA XP cardiac navigation system (commercially available from Biosense Webster) to name a few. Other navigation system may be used within the invasive system  314  in other examples to provide the navigation data  306 . In yet another example, the navigation data  306  can represent a known location within the patient&#39;s body, such as an anatomical landmark, at which a probe is positioned when providing a probe signal. 
     As a further example, the probe signal data  308  can represent an applied signal that is provided by one or more electrodes disposed on a probe, such as a catheter or other device that is adapted to be inserted into the patient&#39;s body. For example, the probe signal data can include parameter describing the applied signal (e.g., signal morphology, such as waveform shape, duty cycle frequency) as well as a time parameter (e.g., time stamp) when the signal is applied. The electrode is configured to provide an applied electrical signal within the patient&#39;s body. If more than one electrode used to provide the prove signal data  308 , each such electrode is at predetermined location relative to each other. A signal generator can apply a specific signal to the probe electrode, which can be measured by electrodes on the body surface (e.g., patch electrodes), as described below. For example, the applied signal can be a predetermined waveform, such as may be a pulse, a square wave, a sinusoidal waveform or the like that can be generated by a signal source electrically connected to the electrode and is distinguishable from anatomically generated signals. The navigation data  306  represents the spatial location of the probe, including at the time when the probe provides the probe signal. For example, the navigation data  306  and probe signal data  308  can include a time stamp to enable the electrode geometry calculator  300  to determine the location of the probe when the probe provides the probe signal. 
     As mentioned, the non-invasive system  316  is configured to provide the electrode location data  310  and electrical measurement data  312 . For example, the non-invasive system includes electrodes, such as can be on one or more patches, arranged on an outer surface of the patient&#39;s body to measure body surface electrophysiological signals corresponding to the electrical measurement data  312 . As described herein, the relative spatial position of electrodes on each patch  110  is known and can be stored in memory as the electrode location data  310 . 
     As a further example, the electrode geometry calculator  300  includes a dipole model cost function  318  having parameters representing a dipole location and moment corresponding to the applied electrical signal, as described by the probe signal data. The geometry calculator  300  is programmed to apply a boundary condition on the dipole model cost function  318  to determine a location of one or more of the electrodes relative to a known location of the probe and to generate the electrode geometry data  304  for such one or more electrodes based on the determined location of the electrodes. Thus, by determining a spatial location of one electrode on a given patch  110  according to the dipole model cost function, the geometry calculator  300  can determine the spatial location of each other electrode on the given patch based on the electrode location data  310  for the given patch. Alternatively, the geometry calculator  300  can be programmed to employ the dipole model cost function  318  to determine the spatial location of each electrode independently of the electrode location data  310 . An example of a dipole model cost function, which can be implemented as the dipole model cost function  318 , is disclosed in U.S. Patent Publication No. 2016/0061599. 
     Advantageously, the electrode geometry calculator  300  can determine the electrode geometry  304  without requiring imaging of the patient while the electrodes are positioned on the patient&#39;s body. This allows the application of electrodes to be independent of access to an imaging modality, which can significantly increase the use of the systems and methods disclosed herein. By not requiring use of a medical imaging modality, the overall cost of the process can likewise be reduced. 
       FIG.  7    is a block diagram showing another example of an electrode geometry calculator  400  that can be implemented to provide geometry data  402 , including electrode geometry data  404 . For example, the electrode geometry calculator  400  can be programmed to provide the electrode geometry data  404 , which corresponds to electrode geometry data  132  implemented in the system  100  of  FIG.  1   . Accordingly, the description of  FIG.  4    also refers to  FIG.  1   . 
     Similar to  FIG.  7   , the electrode geometry calculator  400  is programmed to determine the electrode geometry data  404  based on navigation data  406 , invasive measurement data  408 , and non-invasive electrical measurement data  410 . For example, an invasive system  412  is configured to provide the navigation data  406  and invasive measurement data  408 . The navigation data  306  can represent spatial coordinates (e.g., three-dimensional coordinates) of a probe that is positioned within the patient&#39;s body, including the real-time position of the probe, as disclosed herein. The navigation data  406  can also include a time stamp to identify a time that is associated with each measurement of probe position (e.g., made by a navigation system). 
     The probe includes one or more electrodes configured to measure electrophysiological signals on or within the patient&#39;s heart and provide the electrical measurement data  408  to represent a measured intrabody electrophysiological signal at a known location within the patient&#39;s body. Each measurement location is known based on the navigation data  406 . The probe can be a contact probe or a non-contact probe configured to measure the electrophysiological signals accordingly. The measured electrophysiological signal may be a unipolar or bipolar signal, which can vary depending on the configuration and arrangement of electrodes on the probe. The electrical measurement data  408  can include signal data representing the amplitude of a measured electrical signal over time and time stamp data to identify the time of respective measurements. The time stamps of the navigation data and the electrical measurement data  408  can by synchronized or otherwise coded to enable the respective data to be synchronized. 
     A non-invasive system  414  is configured to provide the electrical measurement data  410 . As described herein, the non-invasive system  414  includes an arrangement of electrodes adapted to be positioned on the outer surface of a patient&#39;s body, such as implemented on one or more patches  110 . For example, the non-invasive system can include measurement system  112  configured to measure body surface electrophysiological signals based on signals sensed by the electrodes and provide the corresponding electrical measurement data  410 , which includes a time stamp or other indication of time associated with the measured electrophysiological signals. 
     The electrode geometry calculator  400  includes morphology analysis function  416  programmed to analyze morphology of signals based on the intrabody electrical measurement data  408  and the non-invasive electrical measurement data  410 , including the measured non-invasively by the body surface electrodes. The electrode geometry calculator  400  further is programmed to determine the electrode geometry data  404  based on the morphological signal analysis. For example, the morphology analysis function  416  is programmed to compare a morphology of the measured intrabody electrophysiological signal(s) with a morphology of the respective signals measured on the patient&#39;s body to generate the electrode geometry data based on the comparison. 
     As a further example, the morphology analysis function  416  includes a forward calculation function  418  programmed to compute a forward calculation to determine a representation of the measured intrabody electrophysiological signal on the outer surface of the patient&#39;s body. The forward calculation function  418  can be programmed to determine electrical signals on the outer surface of the patient&#39;s body based on the measured electrical signal at the known intrabody location. For example, the forward calculation function  418  employs the Laplace equation to relate measurements at each body surface electrode location and the known intrabody location, in which the location of the body surface electrodes are unknown parameters. The known intrabody location can be on the septum of the heart or another known location. The forward calculator thus can compute the electrophysiological signals on the body surface at respective angles on the body surface with respect to the known intrabody location. 
     For example, assuming spatial geometries from torso and heart already segmented from CT or MRI, but patch electrode/vest may not be placed on the patient during imaging during CT/MRI. Given an R wave from normal sinus rhythm, the activation pattern is a strong dipole moving fast along ventricle septum. The duration of the R wave can be known, and the dipole direction and location also can be known based on heart anatomy. The forward calculation function  418  can thus be programmed to forward calculate the dipole to torso surface and create pseudo ECG tracings projected onto the body surface for one or more regions of interest. ECG signals can be measured for each of patches placed on the patient&#39;s body surface, and the measured ECG signals can be matched with the projected pseudo ECGs (e.g., using correlation or other matching algorithm). The region having the best match can be identified as the actual location of the patch, and the location information can be stored in memory and/or displayed graphically. The overall process implemented by morphology analysis function  416  can be an iterative process, so that the region of forward calculation (e.g., computed by forward calculation function  418 ) can be refined over a set of iterations to determine the location with improved precision (e.g., higher resolution). 
     The morphology analysis function  416  also includes an angle calculator  420  programmed to calculate angle from the known intrabody location within the patient&#39;s body to respective electrodes. For example, the angle calculator  420  can be programmed to implement a minimization algorithm (e.g., least squares minimization or the like) to determine the respective angles for each electrode location. In another example, the angle calculator  420  can be programmed to determine respective angles by employing a brute force method or other optimization method. The morphology analysis function includes a localization function  422  programmed to determine the location of the respective electrodes to provide the electrode geometry data  404  based on the respective calculated angles for the electrodes computed from the forward calculation. 
       FIG.  8    is a block diagram showing an example of a mapping system  500  that includes an electrical measurement analyzer and signal modifier function  502 . The function  502  provides an example of a method (e.g., executable instructions) that can be used to implement the function  138  of  FIG.  1   . Accordingly, the description of  FIG.  8    also refers to  FIG.  1   . 
     As shown in the example of  FIG.  8   , the function  502  includes a non-invasive signal analysis function  504  programmed to analyze electrophysiological signals provided in electrical measurement data  506 . For example, the electrical measurement data corresponds to electrical data  122  representing measured electrophysiological signals provided from a plurality of electrodes distributed across an outer surface of a patient&#39;s body. Each electrode, electrical connectors (wires and/or traces), and/or input signal processing circuitry (e.g., amplifiers and filters of measurement system  112 ) can define a respective input channel that provides a measured electrophysiological signal for a respective location on the body surface. In an example, the electrodes are implemented on one or more patches adapted to be placed on the outer surface of the patient&#39;s body and sense cardiac electrical activity, such as described herein. 
     The analysis function  504  can include a comparator function  508  programmed to compare electrophysiological signals. The comparator function  508  can compare signals of different signal channels over the same time interval (or time instance) or compare signals for the same signal channels across two more different time intervals (or time instances). In another example, the comparator function  508  can compare each of the signal channels (or a selected subset of channels) to a baseline, which can be a normalized baseline or a patient-specific baseline. The comparator function  508  can compare different signal parameters, such as a comparison of signal amplitude, frequency and/or morphology. The type of signal analysis and/or the signals being analyzed by the analysis function  504  can be preprogrammed (e.g., by default) or set by a user (e.g., in response to a user input). Additionally or alternatively, the comparator function  508  can be programmed to perform a comparative analysis among all input channels to identify which channels exhibit a prescribed signal characteristic over one or more time intervals. Alternatively or additionally, the comparator function can perform a comparative analysis for each respective signal channel over time to ascertain which channels exhibit changes in respective signals between two more time intervals, such as including low amplitude signal changes. 
     As a further example, the variation calculator  510  is programmed to compute an indication of variation of the electrophysiological signals. The variation can depend on the type of comparative analysis being implemented by the comparator function. For example, the variation calculator  510  can compute the variation in signal magnitude, such as representing a difference between signal values, such as signal amplitude (e.g., a difference between average amplitude, maximum amplitude or dominant frequencies) over a time interval. Alternatively, the variation calculator  510  can compute the variation as statistical variance or standard deviation from a mean signal value (e.g., signal amplitude and/or frequency). The determined variation for each channel can be stored in memory linked to the respective channel for additional signal processing functions, such as disclosed herein. 
     The function  502  also includes a signal emphasizer  512  that is programmed to modify values of electrophysiological signals for a subset of the electrodes (corresponding to respective input channels). For example, the signal emphasizer  512  can be programmed to emphasize or de-emphasize respective signals (or features of such signals) measured over a time interval. The signal emphasizer  512  can be programmed to modify signals for the subset of electrodes (e.g., input channels) based on a spatial relationship of the electrodes. For example, the signal emphasizer  512  can be programmed to select electrodes that are part of one or more patches  110 , and may be selected automatically or in response to a user input (e.g., user input provided by a graphical user interface (GUI)  140 ). In another example, the signal emphasizer  512  can be programmed to select the subset of electrodes (e.g., input channels) based on the analysis implemented by the signal analysis function  504 , including the comparator function  508  and/or the variation calculator  510 . Additionally, regardless of how the subset of electrodes is selected, the signal emphasizer  512  can be programmed to modify the signal values of the selected subset of channels based on the analysis implemented by the signal analysis function  504 . 
     As a further example, the signal emphasizer  512  is programmed to modify electrophysiological signals so that signals (or certain signal features) for the selected subset of the channels in the electrical data  506  are emphasized (e.g., by increase signal amplitude) relative to other channels. In an example, the signal emphasizer  512  emphasizes a given channel in the selected subset of channels by weighting the given channel so that the relative amplitude of the given signal is increased. The signal emphasizer  512  can apply a uniform (e.g., the same) weighting to signals in each of the selected subset of channels. In another example, signal emphasizer  512  can apply a variable weighting to respective signals in each of the selected subset of channels, which weighting can be set based on the signal analysis by function  504 . Additionally, the weighting may further vary over time such as based on the signal analysis, including the analysis implemented by the comparator function  508  and the variation calculator  510 . While the modification is described as increasing the emphasizing the selected subset of channels, in other examples, this can be implemented by de-emphasizing the non-selected remaining subset of channels so that the net relative value of the selected subset of channels are increased. 
     In an example, the signal emphasizer  512  is programmed to modify the electrical measurement data  506 , which is stored in memory, to represent emphasized versions of measured electrophysiological signals for the subset of channels and leave alone (e.g., not emphasize) measured electrophysiological signals for the other channels for electrodes on the body surface. In another example, the signal emphasizer  512  is programmed to implement weighting of the channels to be applied by an electrogram reconstruction function  514  as part of solving the inverse problem. For example, the weighting can be implemented as a multiplying factor for elements of a transfer matrix that relates the geometry between the heart and body surface where the electrodes are position. As one example, measurement in each row can be scaled by multiplying the measurement by a respective weight value (e.g., w 1  though w N ), such as follows: 
     
       
         
           
             
               
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     Thus, the reconstruction function  514  can be programmed to compute reconstructed signals on the cardiac surface of interest (by solving the inverse problem) based on the co-registered geometry data  522  and modified electrical measurement data  506 , including the emphasized electrophysiological signals for the subset of channels. As a result of using emphasized (and/or de-emphasized) signals during inverse reconstruction, the influence of the signals provided by the subset of channels can be accentuated in the reconstructed electrograms. This can include programming the signal emphasizer  512  to accentuate a subset of channels having low amplitude signals (e.g., lower than a threshold) and/or channels exhibiting small signal changes or other signal characteristics over time that usually would be obfuscated by the influence of other (e.g., more pronounced) signals that are measured by electrodes distributed across the body surface. 
     By way of further example, the comparator function  508  can be programmed to compare electrophysiological signals (provided in the electrical measurement data)  506  measured by the electrodes (e.g., implemented on patches) positioned on the body surface relative to baseline data for the same patient measured on the body surface over one or more different time intervals. The comparator function  506  can be programmed to identify (e.g., by tagging or flagging) the subset of the channels exhibiting small signal changes from the baseline signal values. In an example, the baseline data represents electrophysiological signals prior to an intervention event, and the detected changes represent electrophysiological signals measured at a time during or after the event. Examples of interventions can include chemical, electrical stimulation, thermal (e.g., cryogenic or heat) ablation, radiation, radiofrequency, laser, non-contact pulse field or other therapy applied to the heart. In an example, the variation calculator  510  can be programmed compare the detected change in the measured signals relative to a threshold to ascertain which subset of channels exhibits a greater relative change compared to the threshold. The same threshold can be applied to signal changes determined for each of the respective channels or different thresholds can be used, such as regionally or on a per channel basis. 
     In an example, the system  500  includes an event detector  516  programmed to detect the event and to store an indication of the event, such as a time or time interval during which the event occurred. The non-invasive signal analysis function  504  thus can employ the stored event indication to select baseline or pre-event electrical measurement data at a time prior to the event, and to select intra-event or post-event electrical measurement data  506  during or after the event, respectively. The signal analysis function  504  (e.g., including the comparator function  508  and variation calculator  510 ) can then perform corresponding signal analysis with respect to the selected pre-event and intra- and/or post-event electrical measurement data  506 , as disclosed herein. As described herein, for example, the signal emphasizer can be programmed to emphasize the subset of channels by increasing the weight the subset of channels exhibiting the greater relative changes and removing and/or decreasing the weight of channels exhibiting smaller changes with respect to the detected event. Alternatively, where a user desires to emphasize signals exhibiting smaller changes relative to an event, the signal emphasizer can be programmed to de-emphasize the subset of channels exhibiting the greater relative changes and increasing the weight of channels exhibiting smaller changes with respect to the detected event. The reconstruction function  514  thus is programmed to generate reconstructed electrograms according to the weighting or other adjustments applied to the electrical measurement data  506  and based on the geometry data  522 . 
     As described herein, an output generator  518  is programmed to provide the output data  520  to visualize a graphical map or other information on a display (e.g., display  108 ) based on the reconstructed electrograms generated for one or more time intervals. One or more graphical maps may be displayed concurrently on the display, such as including a map without weighting applied and with one or more maps in which weighting (e.g., different weighting) has been applied by signal emphasizer  512 . The different maps being displayed can be for the same time interval. Alternatively, the different maps being displayed can be for different intervals, such as including a time interval before a given event and a time interval after the event. 
     In some examples, the system  500  further includes a neighborhood calculator  524  programmed to determine a proximity of electrodes on the patient&#39;s body based on a spatial analysis of the electrode geometry data, which is part of the geometry data  522 . The electrode geometry data can be determined according to any approach disclosed herein (see, e.g.,  FIGS.  1 ,  6  and/or  7   ). The signal analysis function can be programmed to evaluate electrophysiological signals measured by electrodes in each respective spatial neighborhood, as specified by the neighborhood calculator. For example, the neighborhood calculator  524  can assign each of the spatial neighborhoods to include electrodes of a respective patch  110 , a set of two or more adjacent patches  110  or an arrangement of neighboring electrodes located on two or more patches  110 . The neighborhood calculator  524  can group electrodes together into respective spatial neighborhoods automatically or in response to a user input (e.g., using GUI  140 ) selecting which patches  110  and/or respective electrodes to include a given spatial neighborhood. In a further example, the signal analysis function  504  can be programmed to apply the comparator function  508  and/or the variation calculator  510  to analyze signal channels for electrodes within each spatial neighborhood. 
     In an example, the electrophysiological signal measurements are made by electrodes on the body surface with respect to a common reference. The reference can be a global reference for all electrophysiological signal measurements. Alternatively, each region (e.g., neighborhood) can have a respective reference that is set, such as at a centroid of the region or selected in response to a user input. 
     For example, the signal analysis function  504  is programmed (e.g., by applying the comparator function  508  and/or the variation calculator  510 ) to analyze electrophysiological signal measurements with each neighborhood (e.g., 2-10 cm 2  diameter region on a cardiac surface), as identified by the neighborhood calculator  524 , for changes. Alternatively, the neighborhood calculator  524  can specify a given region as defined by a given patch, such that the signal analysis function  504  analyzes electrophysiological signals for the given patch for changes. The signal analysis function  504  can be programmed to determine the amount of signal change (e.g., low amplitude changes) for each region, and signal emphasizer  512  can apply weighting to respective channels if the signal change for the associated neighborhood exceeds a delta/threshold. 
     In some examples, the signal emphasizer  512  is programmed to further weight regions (e.g., respective spatial neighborhoods) with a weighting factor that is a function of (e.g., be proportional to) an amount of change (e.g., determined by the analysis function  504  relative to common reference) that each of the spatial neighborhoods exhibits. For example, the emphasizer  512  can weight a given neighborhood of signal channels exhibiting a greater amount of change in electrophysiological signals relative to signals channels of one or more other neighborhoods more heavily (e.g., being multiplied by a larger weighting factor) than the other neighborhoods exhibiting a lesser amount of relative change in the measured electrophysiological signals. In another example, neighborhoods exhibiting a lesser amount of relative change in the measured electrophysiological signals may be non-weighted or even may be de-emphasized (or removed altogether) by applying a fractional weighting that is less than one (e.g., &lt;1.0). In this way, the relative influence of spatial regions exhibiting more variation can be increased over other regions, and the resulting output data  520  can be used to visualize corresponding signal activity in graphical maps of the reconstructed electrophysiological signals. For instance, this approach enables a user to observe subtle changes in electrophysiological measurements during an intervention as well as from comparison of before and after the intervention. That is, the weighting can be spatially correlated with exhibiting subtle changes so that the channels with subtle changes are emphasized and stand out more in the output data  520  and are displayed in associated graphical maps. 
       FIG.  9    is a block diagram showing an example of a mapping system  600  that includes a reconstructed signal analysis and modifier function  602 . The function  602  provides an example of a method (e.g., executable instructions) that can be used to implement the function  142  of  FIG.  1   . In some examples, the reconstructed signal analysis and modifier function  602  also can be used in a mapping system in combination with the non-invasive signal analysis and modifier function  502  of  FIG.  8   . Accordingly, the description of  FIG.  9    also refers to  FIGS.  1  and  8   . 
     In the example of  FIG.  9   , a channel selector  603  is programmed to select different subsets of channels for respective electrodes that are to be used from the electrical measurement data  612  for electrogram reconstruction. The channel selector  603  can randomly or pseudorandomly select from the plurality of channels. Alternatively, or additionally, the channel selector  603  can be programmed to select one or more subset of channels responsive to a user input. An electrogram reconstruction function  608  (e.g., corresponding to electrogram reconstruction function  136 ) generates respective sets of reconstructed electrophysiological signals for one or more time intervals for each selected subset of channels based on corresponding electrical measurement data  612  and geometry data  614 . The time interval(s) for use by the electrogram reconstruction function  608  can be also be selected in response to a user input or through an automated interval selection method. The process of selecting and reconstructing electrophysiological signals for different subsets of electrodes for a given time interval thus can be repeated to provide a respective set of reconstructed electrophysiological signals for each selected subset of channels to seek convergence toward a unified solution. 
     The reconstructed signal analysis and modifier function  602  thus is programmed to analyze the subsets of reconstructed electrophysiological signals. The reconstructed signal analysis and modifier function  602  includes a combiner function  604  and a variation calculator  606 . The reconstructed signal analysis and modifier function  602  is programmed to analyze one or more sets of reconstructed electrophysiological signals. The analysis can be across the entire surface of interest onto which the electrophysiological signals have been reconstructed or the analysis can be constrained to a selected region of interest (e.g., selected in response to a user input). 
     In an example, the combiner function  604  can be programmed to combine (e.g., average together) electrophysiological signals that have been reconstructed (by electrograms reconstruction function  608 ) across the surface of interest from two or more respective sets of reconstructed electrophysiological signals to provide to provide an aggregate set of reconstructed electrophysiological signals. For example, the combiner function  604  can average (or add together) signal values at each point (e.g., node) distributed across the surface of interest (e.g., an epicardial surface) over a time interval(s) to generate the aggregate set of reconstructed signals. More than one aggregate set of reconstructed electrophysiological signals can be generated for a given time period, which may be further analyzed and re-combined (e.g., by function  602 ) based on the results of such analysis. 
     The variation calculator  606  may be implemented as an instance of the variation calculator  510  of  FIG.  8   , such as applied and programmed to compute an indication of variation of reconstructed electrophysiological signals in respective sets of reconstructed signals. For example, the variation calculator  606  can compute the variation in reconstructed signal magnitude, such as representing a difference between signal values, such as signal amplitude (e.g., a difference between average amplitude, maximum amplitude or dominant frequencies) over a time interval. 
     In another example, the variation calculator  606  can be programmed to detect changes in electrophysiological signals that have been reconstructed on to the surface of interested based on a comparison one or more respective sets of the reconstructed electrophysiological signals with respect to baseline data. For example, the variation calculator  606  can determine which subsets of channels exhibit greater relative changes compared to the baseline data. The baseline data can be reconstructed electrophysiological signals from a time interval known to represent a normal cardiac rhythm for the patient. In another example, the baseline data can represent reconstructed electrophysiological signals that the reconstruction function  608  reconstructed based on a full (or nearly full) set of channels. 
     A signal emphasizer  610  can be programmed to emphasize (e.g., by weighting) a selected set of measurement channels, corresponding to electrophysiological signals measured by respective electrodes on the body surface, based on the reconstructed signal analysis  602 . Signal emphasizer  610  can emphasize the signals by increasing the weight of the channels exhibiting the greater relative changes and/or and removing or decreasing the weight of channels exhibiting smaller changes relative to the baseline, such as described herein. For example, the signal emphasizer  610  can be programmed to selectively weight channels to increase the influence of those subsets of channels determined (e.g., by analysis and modifier function  602 ) to contribute to greater relative changes compared to the baseline data. Alternatively, or additionally, the function can decrease the weight of or remove respective channels exhibiting the smaller changes compared to the baseline data. As an example, signal emphasizer  610  can be programmed to increase or decrease weights applied to emphasize (or deemphasize) respective body surface channels exhibiting an amplitude change in percentage (e.g., 5% amplitude change compared to a baseline amplitude) or an absolute voltage change (e.g., 0.02 mV in atria and 0.1 mV in ventricle) after intervention when assessing the intervention&#39;s impact. As described herein, the mapping system  600  can include an output generator  618  is programmed to provide the output data  620  to visualize a graphical map or other information on a display (e.g., display  108 ) based on the reconstructed electrograms generated for one or more time intervals. 
       FIGS.  10 A,  10 B and  10 C,  10 D and  10 E  depict examples electrophysiological signals showing a comparison of signal waveforms, a set of patch electrodes and graphical maps provided for a full set of body surface electrodes and the set of patch electrodes. 
     For example,  FIG.  10 A  shows a set of preprocessed signal waveforms  700 ,  702  and  704 . The waveforms  700  represent signal channels measured by 248 electrodes distributed across a patient&#39;s thorax. The set of signal waveforms  702  includes an interval of measured signals for 197 of the 248 channels that have been determined to be good channels (e.g., by non-invasive signal analysis function  54 ). The set of signal waveforms  704  represents the same interval of measured signals for 13 channels of the measured by a set of patch electrodes, namely a patch having respective electrodes adapted to detect a PVC. 
     For example,  FIGS.  10 B and  10 C  are diagrams  710  and  712  of a front and back of a torso showing examples of patch electrodes  714  and  716 , respectively, each having an arrangement of electrodes. The patch  714  includes six electrodes  706  configured in a circular arrangement, and the patch  716  includes seven patch electrodes  708  configured in a circular arrangement with an electrode near a center thereof. For example, the patches can each include a web (e.g., substrate) of pliant conformable material with an adhesive backing to attach to the sensing location shown in  FIGS.  10    B and  10 C. In other examples, the electrodes of the respective patches could be activated for sensing electrophysiological signal from a larger, full complement of electrodes. 
       FIGS.  10 D and  10 E  show activation maps  720  and  722  for respective sets of sensed electrophysiological signals. For example,  FIG.  10 D  shows the map  720  generated for the selected interval based on the signal waveforms shown at  702 . The activation map  722  is generated (e.g., by output generator  144 ) for the respective electrophysiological signals sensed by patch electrodes  714  and  716 . As a further example, a cross-correlation between the maps  720  and  722  can be computed to demonstrate a median cross-correlation of 0.98 and mean of 0.92 between the patch electrodes (e.g., 13 electrodes) and the full set of 197 electrodes. 
       FIGS.  11 A and  11 B  show potential maps  730  and  732 , respectively, reconstructed for a cardiac envelope (e.g., the whole heart surface) based on electrophysiological signals measured by different electrode configurations and geometry data, such as described herein. The map  730  is generated based on electrophysiological signals (e.g., waveforms  700  or  702  shown in  FIG.  10 A ) measured by a full set of electrodes and geometry data. The map  732  is generated based on electrophysiological signals measured by the patch electrodes  714  and  716  (e.g., derived from waveforms  704  shown in  FIG.  10 A ) and geometry data. 
     The geometry data can include three-dimensional coordinates representing the entire cardiac envelope to where the EP signals are being reconstructed (e.g., a cardiac mesh). The geometry data can also include a three-dimensional representation for the torso (e.g., a torso surface mesh), including electrode locations used for measuring signals used to generate the maps  730  and  732 . For the map  732  generated based on measurements from the patch electrode, the geometry data can represent a torso mesh including patch electrode locations and locations on the mesh without patch electrodes for measuring the signals. The potential maps  730  and  732  demonstrate that there is a high level of correlation between the full set of electrodes and the patch electrodes  714  and  716 .  FIG.  11 C  also includes a plot  734  of waveforms  736  and  738  selected from a respective point (e.g., the same anatomical spatial location—a node) on the cardiac envelope, shown at  740  and  742  in  FIGS.  11 A and  11 B , respectively. 
       FIGS.  12 A- 17 B  are diagrams showing examples of different patch electrode configurations that can be implemented. The examples of  FIGS.  12 A- 17 B  are not exhaustive and various other configurations can be realized, such as adapted to sense body surface electrophysiological signals associated with a localized arrhythmia (e.g., AF, PVC, AT, VT, or the like), for use during a particular procedure (e.g., CRT or Brugada syndrome) or otherwise having a specific geometry (e.g., a reduced set of electrodes adapted for simplified global or localized electrophysiological measurements). 
     For example,  FIGS.  12 A and  12 B  include front and back diagrams  750  and  752  showing an example of one or more patches in the form of a belt that can extend across the front and back portions of a patient&#39;s torso. For example, the belt includes a portion of patch electrodes  754  arranged and configured on a front portion of the torso and another portion of patch electrodes  756  arranged and configured on a back portion of the torso. The patch electrodes  754  and  756  are shown at locations adapted to measure electrophysiological signals indicating a PVC. 
       FIGS.  13 A and  13 B  include front and back diagrams  760  and  762  showing another example of one or more patches in the form of a belt that can extend across the front and back portions of a patient&#39;s torso. For example, the belt includes a portion of patch electrodes  764  arranged and configured on a front portion of the torso and another portion of patch electrodes  766  arranged and configured on a back portion of the torso. The example patch electrode of  FIGS.  13 A- 13 B  includes a greater number of electrodes on the back portion than the front portion. 
       FIGS.  14 A and  14 B  include front and back diagrams  770  and  772  showing another example of multiple patches configured for sensing electrophysiological signals from the front and back portions of a patient&#39;s torso. For example, the patch electrode includes a first patch (or multiple patches) having a group of electrodes  774  arranged and configured on a central front portion of the torso and another group of electrodes  776  arranged and configured on a central back portion of the torso. 
       FIGS.  15 A and  15 B  include front and back diagrams  780  and  782  showing another example of one or more patches configured for sensing electrophysiological signals from the front and back portions of a patient&#39;s torso. For example, a portion of patch electrodes  784  are arranged and configured in the form of a belt on a front portion of the torso and another portion of patch electrodes  786  arranged and configured as a central grouping of electrodes on the back portion of the torso. 
       FIGS.  16 A and  16 B  include front and back diagrams  790  and  792  showing another example of patch electrodes configured for sensing electrophysiological signals from the front and back portions of a patient&#39;s torso. In the example of  FIGS.  16 A and  16 B , patch electrodes  794  and  796  include respective groups of electrodes arranged and configured on a front portion of the torso. The patch electrodes  794  and  796  can be mounted to the same substrate, such as to maintain a desired spatial arrangement between the patch electrodes  794  and  796 , or separate patch substrates could be used. Another set of patch electrodes  798  and  800  include respective groups of electrodes arranged and configured on a back portion of the torso. As with the front patch electrodes, the patch electrodes  798  and  800  can be mounted to the same substrate, such as to maintain a desired spatial arrangement between the patch electrodes, or separate patch substrates could be used. 
       FIGS.  17 A and  17 B  include front and back diagrams  810  and  812  showing another example of one or more patches configured for sensing electrophysiological signals from the front and back portions of a patient&#39;s torso. As shown  FIG.  17 A , a portion of patch electrodes  814  are arranged and configured in the form semi-circular ends connected by a line of electrodes on a front portion of the torso.  FIG.  17 B  show a portion of patch electrodes  816  arranged and configured on the back portion of the torso, including a central grouping and two side groupings lateral to the central grouping of electrodes. For example, the patch electrodes  814  and  816  can be adapted to measure electrophysiological signals during a focal atrial tachycardia (AT) for the right inferior pulmonary vein (RIPV). By using such an arrangement of electrodes, the contribution of other signals, which can dilute or otherwise obfuscate the components of the AT, can be reduced. In the example of  FIGS.  17 A and  17 B , the patch electrodes include 27 sensors are arranged and configured to measure a set of electrograms that emphasize signal components that describe the AT condition measured from the body surface. 
       FIGS.  12 A- 17 B  thus show some examples of different patch electrodes, which can be implemented to sense a given arrhythmia condition and/or signals at one or more regions of interest, each of which can be specified in response to a user input or be derived automatically based on analysis of electrophysiological signals measured for one or more time intervals. In other examples, physical patches containing patch electrodes arranged in configured in a given manner can be implemented for sensing electrophysiological signal spatially and temporally relevant to sensing requirements. 
     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.