Electrophysiological system and method for evaluating a microentry site

Abstract

At least some embodiments of the present disclosure are directed to a system for processing cardiac information, the system including a processing unit configured to receive one or more cardiac electrical signals acquired over a heart beat from one or more electrodes positioned within a cardiac chamber, receive an indication of a measurement location corresponding to each of the cardiac electrical signals, analyze the one or more cardiac electrical signals to calculate a plurality of duty cycle values, the calculated duty cycle values ​​corresponding to a plurality of preselected cycle length windows, and calculate an average duty cycle of the calculated duty cycle values ​​over the plurality of preselected cycle length windows.

Description

Technical Field

[0001] The present disclosure relates to electrophysiological systems and methods for processing cardiac electrical signals.

Background Art

[0002] To treat various heart diseases such as supraventricular arrhythmias and ventricular arrhythmias, it is becoming increasingly common to use minimally invasive procedures such as catheter ablation. Such procedures involve mapping the electrical activity of the heart ( "cardiac mapping") (e.g., based on cardiac signals) at various locations on the endocardial surface, etc., to identify the site of arrhythmia occurrence, and then performing ablation targeting that site. To perform such cardiac mapping, a catheter having one or more electrodes can be inserted into the patient's heart chamber.

[0003] Conventional three-dimensional (3D) mapping techniques include contact mapping, non-contact mapping, and combinations of contact and non-contact mapping. In both contact mapping and non-contact mapping, one or more catheters are advanced into the heart. In some catheters, once inside the heart chamber, the catheter may be deployed to take on a 3D shape. In contact mapping, physiological signals resulting from the electrical activity of the heart are acquired using one or more electrodes located at the distal end of the catheter after determining that the distal end of the catheter is in stable and stationary contact with the endocardial surface of a particular heart chamber. In a non-contact based mapping system, the system provides physiological information regarding the endocardium of the heart chamber using signals detected by non-contact electrodes and information regarding the anatomical structure of the heart chamber and the relative electrode positions. The position and electrical activity are typically measured point by point sequentially at about 50 - 200 points on the inner surface of the heart to construct an electroanatomical description of the heart. The generated map can then be used as a basis for determining treatment strategies, such as tissue ablation, to alter the propagation of the electrical activity of the heart and restore normal heart rhythm.

[0004] In many conventional mapping systems, clinicians visually inspect or examine the captured electrograms (EGMs), increasing the examination time and cost. However, during an automated electroanatomical mapping process, approximately 6,000 to 20,000 intracardiac electrograms (EGMs) may be captured, which is not suitable for being fully manually examined by a clinician (e.g., a physician) for diagnostic evaluation, EGM classification, and / or the like. Typically, a mapping system extracts scalar values from each EGM to construct a voltage, activation, or other map type to depict the overall pattern of activity within the heart. The map reduces the need to examine the captured EGMs but also compresses often complex and useful information in the EGMs. Additionally, the map may lead to misunderstandings due to inappropriate selection of features such as electrical artifacts or activation times. Moreover, due to the complex nature of the prior art, cardiac maps are often not suitable for accurate and efficient interpretation.

[0005] Cardiac mapping may also be used to detect atrial tachycardia sites. Atrial tachycardia (AT) is a type of abnormal heart rhythm, or arrhythmia. It occurs when the electrical signals that control the heartbeat start from an abnormal location in the atrium and are rapidly repeated, causing the atrium to beat too fast. Atrial tachycardia can be classified into three broad categories, namely, focal AT, macro-reentry AT, and micro-reentry AT. Focal AT can occur in a structurally normal heart but can also occur in patients with heart disease. Macro-reentry AT can occur in the context of atrial fibrillation. Micro-reentry AT can occur in the situation where there is a disease in the atrial myocardium that supports very slow conduction. SUMMARY OF THE INVENTION

[0006] As described in the examples, Example 1 is a system for processing cardiac information. The system includes a processing unit configured to receive one or more cardiac electrical signals from one or more electrodes disposed within the cardiac cavity, the cardiac electrical signals being acquired over a cardiac beat, the processing unit being configured to receive an indication of the measurement location corresponding to each of the cardiac electrical signals, analyze the one or more cardiac electrical signals to calculate a plurality of duty cycle values, each calculated duty cycle value corresponding to one of a plurality of preselected cycle length windows, the processing unit being configured to calculate an average duty cycle of the calculated duty cycle values over the plurality of preselected cycle length windows, and facilitate presentation on a display device of a three-dimensional electroanatomical map overlaid with an annotation representing the calculated average duty cycle based on the measurement location of the associated cardiac electrical signal.

[0007] Example 2 is the system of Example 1, wherein the cardiac electrical signal includes an intracardiac electrogram (EGM). Example 3 is the system of Example 2, wherein the processing unit is configured to calculate a plurality of duty cycle values by determining an activation duration associated with a cardiac beat and dividing the activation duration by each of a plurality of preselected cycle length windows.

[0008] Example 4 is the system of Example 2, wherein the analysis of the one or more cardiac electrical signals includes generating an activation waveform therefrom, the activation waveform being based on the deflection of the one or more analyzed cardiac electrical signals from a signal baseline.

[0009] Example 5 is the system of Example 4, wherein the processing unit is further configured to determine an activation duration based on the activation waveform. Example 6 is the system of either Example 4 or 5, wherein the activation waveform includes a value indicating the probability that the deflection represents activation of cardiac tissue.

[0010] Example 7 is any of the systems of Examples 4-6, wherein the processing unit is configured to calculate a plurality of duty cycle values by calculating an average activation waveform value over each of a plurality of pre-selected cycle length windows.

[0011] Example 8 is any of the systems of Examples 1-7, wherein the calculated average duty cycle represents the probability that the heart tissue corresponding to the measurement location defines a micro-reentry site. Example 9 is any of the systems of Examples 1-8, wherein the plurality of pre-selected cycle length windows are in the range of 140 milliseconds to 2000 milliseconds.

[0012] Example 10 is any of the systems of Examples 1-9, further comprising a display device operably connected to the processing unit and configured to display a 3D anatomical map with annotations overlaid thereon.

[0013] Example 11 is any of the systems of Examples 1-10, wherein the processing unit is configured to calculate an average duty cycle value for each cardiac electrical signal. Example 12 is any of the systems of Examples 1-10, wherein the processing unit is configured to aggregate a plurality of cardiac electrical signals having associated measurement locations within a specified region and calculate an average duty cycle value for the aggregated cardiac electrical signals.

[0014] Example 13 is a method for processing cardiac information. The method includes receiving one or more cardiac electrical signals acquired over a heartbeat of a heart, analyzing the one or more cardiac electrical signals and calculating a plurality of duty cycle values, each calculated duty cycle value corresponding to one of a plurality of pre-selected cycle length windows, the method including calculating an average of the calculated duty cycle values over the plurality of pre-selected cycle length windows, and displaying on a display device a 3D anatomical map with an annotation representing the calculated average duty cycle overlaid thereon.

[0015] Example 14 is the method of Example 13, wherein analyzing one or more cardiac electrical signals includes generating an activation waveform therefrom, the activation waveform being based on deflections of one or more analyzed cardiac electrical signals from a signal baseline.

[0016] Example 15 is the method according to Example 13 or 14, wherein calculating a plurality of duty cycle values includes calculating a plurality of duty cycle values based on each of the activation waveform and a plurality of preselected cycle length windows.

[0017] Example 16 is a system for processing cardiac information. The system includes a processing unit configured to receive one or more cardiac electrical signals from one or more electrodes disposed within a cardiac chamber, the cardiac electrical signals being acquired over a cardiac beat, the processing unit being configured to receive an indication of a measurement location corresponding to each of the cardiac electrical signals, analyze the one or more cardiac electrical signals to calculate a plurality of duty cycle values, each calculated duty cycle value corresponding to one of a plurality of preselected cycle length windows, the processing unit being configured to calculate an average duty cycle of the calculated duty cycle values over the plurality of preselected cycle length windows and facilitate presentation on a display device of a three-dimensional electroanatomical map overlaid with an annotation representing the calculated average duty cycle based on the associated measurement location of the cardiac electrical signals.

[0018] Example 17 is the system of Example 16, wherein the cardiac electrical signals include an intracardiac electrogram (EGM). Example 18 is the system of Example 17, wherein the processing unit is configured to determine an activation duration associated with a cardiac beat and calculate a plurality of duty cycle values by dividing the activation duration by each of a plurality of preselected cycle length windows.

[0019] Example 19 is the system of Example 17, wherein the analysis of one or more cardiac electrical signals includes generating an activation waveform therefrom, the activation waveform being based on the deflection of one or more analyzed cardiac electrical signals from a signal baseline.

[0020] Example 20 is the system of Example 19, wherein the processing unit is further configured to determine an activation duration based on the activation waveform. Example 21 is the system of Example 19, wherein the activation waveform includes a value indicating the probability that the deflection represents activation of cardiac tissue.

[0021] Example 22 is the system of Example 19, wherein the processing unit is configured to calculate a plurality of duty cycle values by calculating average activation waveform values across each of a plurality of preselected cycle length windows.

[0022] Example 23 is the system of Example 16, wherein the calculated average duty cycle represents the probability that the cardiac tissue corresponding to the measurement location defines a microreentry site. Example 24 is the system of Example 16, wherein the plurality of preselected cycle length windows are in the range of 140 milliseconds to 2000 milliseconds.

[0023] Example 25 is the system of Example 16, further comprising a display device operably connected to the processing unit and configured to display a three-dimensional anatomical map with annotations overlaid thereon.

[0024] Example 26 is the system of Example 16, wherein the processing unit is configured to calculate an average duty cycle value for each cardiac electrical signal. Example 27 is the system of Example 16, wherein the processing unit is configured to aggregate a plurality of cardiac electrical signals having associated measurement locations within a specified region and calculate an average duty cycle value for the aggregated cardiac electrical signals.

[0025] Example 28 is a method for processing cardiac information. The method includes receiving one or more cardiac electrical signals acquired over a cardiac beat, analyzing the one or more cardiac electrical signals, and calculating a plurality of duty cycle values, each calculated duty cycle value corresponding to one of a plurality of preselected cycle length windows. The method includes calculating an average of the calculated duty cycle values over the plurality of preselected cycle length windows, and displaying on a display device a three-dimensional anatomical map overlaid with an annotation representing the calculated average duty cycle.

[0026] Example 29 is the method of Example 28, wherein analyzing the one or more cardiac electrical signals includes generating an activation waveform therefrom, the activation waveform being based on deflections of the one or more analyzed cardiac electrical signals from a signal baseline.

[0027] Example 30 is the method described in Example 28, wherein calculating the plurality of duty cycle values includes calculating the plurality of duty cycle values based on each of the activation waveform and a plurality of preselected cycle length windows.

[0028] Example 31 is the method described in Example 28, wherein the cardiac electrical signal includes an intracardiac electrogram (EGM). Example 32 is the method of Example 29, wherein the activation waveform includes a value indicating a probability that the deflection represents activation of cardiac tissue.

[0029] Example 33 is the method of Example 29, wherein calculating the plurality of duty cycle values includes calculating an average activation waveform value over each of the plurality of preselected cycle length windows.

[0030] Example 34 is the method of Example 28, wherein the calculated average duty cycle represents a probability that cardiac tissue corresponding to the measurement location defines a microreentry site. Example 35 is the method of Example 28, which includes calculating a plurality of duty cycle values, aggregating a plurality of cardiac electrical signals having associated measurement positions within a specified region, and calculating an average duty cycle value for the aggregated cardiac electrical signals.

[0031] Although a plurality of embodiments have been disclosed, further other embodiments of the present invention will become apparent to those skilled in the art from the following detailed description showing and describing exemplary embodiments of the present invention. Therefore, the drawings and the detailed description should be considered to be illustrative in nature and not restrictive.

Brief Description of the Drawings

[0032]

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Best Mode for Carrying Out the Invention

[0033] The present invention is subject to various modifications and alternative forms, but specific embodiments are shown by way of example in the drawings and will be described in detail below. However, the intention is not to limit the present invention to the specific embodiments described. On the contrary, the present invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims.

[0034] As used herein with respect to measurements (e.g., dimensions, characteristics, attributes, components, etc.) and ranges thereof of tangible things (e.g., products, inventories, etc.) and / or intangible things (e.g., data, electronic representations of currency, accounts, information, parts of things (e.g., percentages, fractions), calculations, data models, dynamic system models, algorithms, parameters, etc.), the terms “about” and “approximately” include the recited measurements and are reasonably close to the recited measurements, but may vary by an amount that is reasonably small and readily ascertainable by one of ordinary skill in the art due to measurement error, differences in measurement and / or calibration of manufacturing apparatus, human error in reading and / or setting of measurements, adjustments made to optimize performance and / or structural parameters in view of other measurements (e.g., measurements related to other things), improper adjustment and / or operation of things, settings, and / or measurements by a particular implementation scenario, person, computing device, and / or machine, system tolerances, control loops, machine learning, predictable variations (e.g., statistically insignificant variations, chaotic variations, system and / or model instabilities, etc.), preferences, and / or the like.

[0035] Exemplary methods may be represented by one or more drawings (e.g., flow diagrams, communication flows, etc.), but the drawings should not be construed as suggesting any requirements for the various steps disclosed herein or a particular order among them. However, some specific embodiments may require specific steps and / or a specific order between specific steps, as may be explicitly described herein and / or understood from the nature of the steps themselves (e.g., the operation of some steps may depend on the result of a previous step). Additionally, a “set,” “subset,” or “group” of items (e.g., inputs, algorithms, data values, etc.) may include one or more items, and similarly, a subset or subgroup of items may include one or more items. “Plurality” means more than one.

[0036] As used herein, the term “based on” is not meant to be limiting; rather, it indicates that a determination, identification, prediction, calculation, and / or the like is performed by using, at a minimum, the term(s) preceding “based on” as input. For example, predicting a result based on specific information may alternatively or additionally involve making the same determination based on different information.

[0037] Identification of microreentry atrial tachycardia (AT) in a cardiac map (e.g., an activation map) is difficult. Even when microreentry is suspected, it is difficult and time-consuming to track the location of the microreentry. In addition, slow microreentry AT may appear similar to focal AT but has a small area of mixed timing at the center. In many cases, the electroanatomical map of microreentry AT contains many disorganized and dissociated regions and has an appearance similar to atrial fibrillation (AF). Embodiments of the present disclosure facilitate the evaluation of microreentry AT sites. In some embodiments, certain electrogram characteristics (e.g., average duty cycle values over multiple cycle length windows) are generated and evaluated to determine the probability of a microreentry site. In some embodiments, an anatomical map with annotations of electrogram characteristics is presented to facilitate the evaluation of microreentry sites. In some embodiments, activation waveforms and activation waveform values are used in the evaluation.

[0038] An activation waveform, or what is referred to as an annotation waveform, is a set of activation waveform values and may include, for example, a set of discrete activation waveform values (e.g., a set of activation waveform values, a set of activation time annotations, etc.), a function defining an activation waveform curve, and / or the like. In some embodiments, each data point of the activation waveform represents a “probability” per sample of tissue activation. In some embodiments, the activation waveform is displayed, used to present in an activation propagation map, used to facilitate diagnosis, used to facilitate classification of electrical signals, and / or the like. To perform aspects of embodiments of the methods described herein, cardiac electrical signals may be obtained from a mapping catheter (e.g., associated with a mapping system), which may be used in conjunction with other devices commonly used in an electrophysiology laboratory, such as a recording system, a coronary sinus (CS) catheter or other reference catheter, an ablation catheter, a memory device (e.g., local memory, cloud server, etc.), a communication component, a medical device (e.g., an implantable medical device, an external medical device, a remote measurement device, etc.) and / or the like.

[0039] The sensed cardiac electrical signal, when the term is used herein, may refer to one or more sensed signals. Each cardiac electrical signal may include a plurality of intracardiac electrograms (EGMs) sensed within the heart chamber and may include any number of features that can be identified by aspects of the electrophysiological system. Examples of cardiac electrical signal features include, but are not limited to, activation time, activation, activation waveform, filtered activation waveform, minimum voltage value, maximum voltage value, maximum negative time-derivatives of voltage, instantaneous potential, voltage amplitude, dominant frequency, peak-to-peak voltage, and / or the like. The features of the cardiac electrical signal may refer to one or more features extracted from one or more cardiac electrical signals, one or more features derived from one or more features extracted from one or more cardiac electrical signals, and / or the like. Additionally, the representation of the features of the cardiac electrical signal on the cardiac and / or surface map may represent one or more features of the cardiac electrical signal, interpolation of the features of a plurality of cardiac electrical signals, and / or the like.

[0040] Each cardiac signal may be associated with a respective set of position coordinates corresponding to the location where the cardiac electrical signal was sensed. Each of the respective position coordinates of the sensed cardiac signal may include three-dimensional Cartesian coordinates, polar coordinates, and / or the like. In some cases, other coordinate systems can be used. In some embodiments, an arbitrary origin is used and each position coordinate refers to a position in space relative to that arbitrary origin. In some embodiments, since the cardiac signal can be sensed on the cardiac surface, each position coordinate may be on the endocardial surface, epicardial surface, middle of the myocardium, and / or in the vicinity of one of these of the patient's heart.

[0041] FIG. 1 shows a schematic diagram of an exemplary embodiment of an electrophysiological system 100. As described above, embodiments of the subject matter disclosed herein may be implemented in a mapping system (e.g., a cardiac mapping system), and other embodiments may be implemented in an ablation system, a recording system, a computer analysis system, and / or the like. The electrophysiological system 100 includes a movable catheter 110 having a plurality of spatially distributed electrodes. During the signal acquisition phase, the catheter 110 is moved to a plurality of positions within the heart chamber into which the catheter 110 is inserted. In some embodiments, a plurality of electrodes are attached to the distal end of the catheter 110 and are spread somewhat uniformly over the catheter. For example, the electrodes may be attached to the catheter 110 according to a 3D olive shape, a basket shape, and / or the like. The electrodes are attached to a device that can deploy the electrodes into a desired shape while they are within the heart and can store the electrodes when the catheter is removed from the heart. To enable deployment into a 3D shape within the heart, the electrodes may be attached to a balloon, a shape memory material such as nitinol, an operable hinge structure, and / or the like. According to an embodiment, the catheter 110 may be a mapping catheter, an ablation catheter, a diagnostic catheter, a CS catheter, and / or the like. For example, aspects of an embodiment of the catheter 110, the electrical signals acquired using the catheter 110, and the subsequent processing of the electrical signals may be applicable to implementations having a recording system, an ablation system, and / or any other system having a catheter with electrodes configured to acquire cardiac electrical signals, as described herein.

[0042] At each location where the catheter 110 is moved, the plurality of electrodes of the catheter acquire signals resulting from the electrical activity within the heart. As a result, reconstructing physiological data regarding the electrical activity of the heart and presenting it to a user (such as a physician and / or technician) can be based on information acquired at multiple locations, thereby providing a more accurate and faithful reconstruction of the physiological behavior of the endocardial surface. Acquisition of signals at multiple catheter positions within the heart cavity enables the catheter to function effectively as a "mega catheter", and the effective number of its electrodes and electrode spans is proportional to the product of the number of positions where signal acquisition is performed and the number of electrodes the catheter has.

[0043] To improve the quality of the reconstructed physiological information on the endocardial surface, in some embodiments, the catheter 110 is moved to more than three positions within the heart cavity (e.g., more than 5, 10, or 50 positions). Further, the spatial range over which the catheter is moved may be greater than one-third (1 / 3) of the diameter of the heart cavity (e.g., greater than 35%, 40%, 50%, or 60% of the diameter of the heart cavity). Additionally, in some embodiments, the reconstructed physiological information is calculated based on signals measured over several cardiac beats at a single catheter position within the heart cavity or at several positions. In situations where the reconstructed physiological information is based on multiple measurements over several cardiac beats, the multiple measurements may be synchronized with respect to each other such that the measurements are performed at approximately the same phase of the cardiac cycle. The signal measurements over multiple beats may be synchronized based on features detected from physiological data such as surface electrocardiogram (ECG) and / or intracardiac electrogram (EGM).

[0044] The electrophysiological system 100 further includes a processing unit 120 that performs some of the operations related to evaluation, including processing cardiac electrical signals collected from electrodes and / or catheters. The processing unit 120 can perform, for example, mapping procedures including reconstruction procedures for determining physiological information on the endocardial surface (e.g., as described above) and / or within the heart chamber. The processing unit 120 may perform catheter registration procedures. The processing unit 120 may aggregate the information captured by the catheter 110 and generate a 3D grid used to facilitate the display of portions of that information.

[0045] The position of the catheter 110 inserted into the heart chamber can be determined using a conventional sensing and tracking system 180, which provides 3D spatial coordinates of the catheter and / or its plurality of electrodes relative to the coordinate system of the catheter established by the sensing and tracking system. These 3D spatial positions can be used when constructing the 3D grid. Embodiments of the system 100 can use a hybrid localization technique that combines impedance localization with magnetic localization techniques. This combination may enable the system 100 to accurately track the catheter connected to the system 100. The magnetic localization technique uses a magnetic field generated by a localization generator placed under the patient table to track the catheter with a magnetic sensor. The impedance localization technique can be used to track a catheter that may not be equipped with a magnetic position sensor for use with a surface ECG patch.

[0046] In some embodiments, to perform the mapping procedure and reconstruct physiological information on the endocardial surface, the processing unit 120 may align the coordinate system of the catheter 110 with the coordinate system of the endocardial surface. The processing unit 120 (or some other processing component of the system 100) can determine a coordinate transformation function that transforms the 3D spatial coordinates of the catheter's position into coordinates expressed with respect to the coordinate system of the endocardial surface and / or vice versa. In some cases, such a transformation may not be necessary because some embodiments of the 3D grid are used to capture contact and non-contact EGMs and mapping values can be selected based on the statistical distribution associated with the nodes of the 3D grid. The processing unit 120 may perform post-processing operations on the physiological information to extract useful features of the information and display them to the operator of the system 100 and / or other persons (e.g., physicians).

[0047] According to an embodiment, signals acquired by the plurality of electrodes of the catheter 110 are passed to the processing unit 120 via an electrical module 140 that can include, for example, signal conditioning components. The electrical module 140 receives the signals communicated from the catheter 110 and performs signal enhancement operations on the signals before they are transferred to the processing unit 120. The electrical module 140 may include signal conditioning hardware, software, and / or firmware that can be used to amplify, filter, and / or sample the intracardiac potentials measured by one or more electrodes. Intracardiac signals typically have a maximum amplitude of 60 mV and an average of several millivolts.

[0048] In some embodiments, the signal is filtered by a bandpass filter having a frequency range (e.g., 0.5 - 500 Hz) and sampled using an analog-to-digital converter (e.g., having a 15-bit resolution at 1 kHz). To avoid interference with electrical equipment in the room, the signal can be filtered to remove the frequency corresponding to the power supply (e.g., 60 Hz). Other types of signal processing operations such as spectral equalization, automatic gain control, etc. can also be performed. In some implementations, the intracardiac signal can be a unipolar signal measured relative to a reference (which may be a virtual reference). In such implementations, the reference can be, for example, a coronary sinus catheter or Wilson's Central Terminal (WCT), and the signal processing operation can calculate the difference therefrom to generate a multipolar signal (e.g., a bipolar signal, a tripolar signal, etc.). In some other implementations, the signal can be processed (e.g., filtered, sampled, etc.) before and / or after generating the multipolar signal. The resulting processed signal is transferred by the electrical module 140 to the processing unit 120 for further processing.

[0049] As further shown in FIG. 1, the electrophysiological system 100 may also include peripheral devices such as a printer 150 and / or a display device 170, both of which can be interconnected to the processing unit 120. Additionally, the electrophysiological system 100 includes a storage device 160 that can be used to store data obtained by various interconnected modules, including volumetric images, raw data measured by electrodes and / or resulting endocardial representations calculated therefrom, partially calculated transforms used to speed up mapping procedures, reconstructed physiological information corresponding to the endocardial surface, and / or the like.

[0050] In some embodiments, the processing unit 120 may be configured to automatically improve the accuracy of its algorithms by using one or more artificial intelligence techniques (e.g., machine learning models, deep learning models), classifiers, and / or the like. In some embodiments, for example, the processing unit may use one or more supervised and / or unsupervised techniques such as, for example, support vector machines (SVMs), k-nearest neighbors, neural networks, convolutional neural networks, recurrent neural networks, and / or the like. In some embodiments, the classifier may be trained and / or adapted using feedback information from the user, other metrics, and / or the like.

[0051] The exemplary electrophysiological system 100 shown in FIG. 1 is not intended to suggest any limitation as to the use or functionality scope of the embodiments of the present disclosure. Also, the exemplary electrophysiological system 100 should not be construed as having dependencies or requirements related to any single component or combination of components illustrated therein. Additionally, the various components shown in FIG. 1 may, in some embodiments, be integrated with various ones of the other components (and / or components not shown) shown therein, and all of them are considered to be within the scope of the subject matter disclosed herein. For example, the electrical module 140 may be integrated with the processing unit 120. Additionally or alternatively, aspects of embodiments of the electrophysiological system 100 may be implemented in a computer analysis system configured to receive cardiac electrical signals and / or other information from a memory device (e.g., a cloud server, a mapping system memory, etc.) and perform aspects of embodiments of the methods described herein for processing cardiac information (e.g., determining annotated waveforms). That is, for example, the computer analysis system may include the processing unit 120 but may not include the mapping catheter.

[0052] FIG. 2 is a block diagram of an exemplary processing unit 200 according to an embodiment of the present disclosure. The processing unit 200 may be, may be similar to, may include, or may be included in the processing unit 120 shown in FIG. 1. As shown in FIG. 2, the processing unit 200 may be implemented on a computing device including one or more processors 202 and one or more memories 204. Although the processing unit 200 is referred to herein in the singular, the processing unit 200 may be implemented in multiple instances (e.g., as a server cluster), may be distributed across multiple computing devices, may be instantiated within multiple virtual machines, and / or may be similar. One or more components of the electrophysiological system may be stored in the memory 204. In some embodiments, the processor 202 may be configured to instantiate one or more components to generate activation waveforms, a set of signal analysis results, electrogram characteristics, histograms, and cardiac maps, any one or more of which may be stored in the data repository 206.

[0053] As shown in FIG. 2, the processing unit 200 may include an acceptor 212 configured to receive electrical signals from a mapping catheter (e.g., the mapping catheter 110 shown in FIG. 1). The measured electrical signals may include a plurality of intracardiac electrograms (EGMs) sensed within a patient's heart. The acceptor 212 may receive an indication of the measurement location corresponding to each of the electrical signals. In some embodiments, the acceptor 212 may be configured to determine whether to accept the received electrical signals. The acceptor 212 may utilize any number of different components and / or techniques to determine which electrical signals or beats to accept, such as filtering, beat matching, morphology analysis, position information (e.g., catheter movement), respiratory gating, and / or the like. The received electrical signals and / or the processed electrical signals may be stored in the data repository 206.

[0054] In an embodiment, an accepted electrical signal is received by an activation waveform generator 214 configured to extract at least one activation feature from each of the electrical signals when the electrical signal includes an activation feature to be extracted. In some embodiments, the at least one activation feature includes at least one value corresponding to at least one annotation metric. The at least one feature can include at least one event, and the at least one event can include at least one value corresponding to at least one metric and / or at least one corresponding time (the corresponding time does not necessarily exist for each activation feature). In some embodiments, the at least one metric can include, for example, activation time, minimum voltage value, maximum voltage value, maximum negative time derivative of voltage, instantaneous potential, voltage amplitude, dominant frequency, peak-to-peak voltage, activation duration, and / or the like. In some embodiments, the activation waveform generator 214 can be configured to detect an activation and generate an activation waveform. In some cases, the waveform generator 214 can use any one of the activation waveform embodiments, including, for example, those described in U.S. Patent Application Publication No. 2018 / 0296113, entitled "ANNOTATION WAVEFORM", the disclosure of which is hereby incorporated by reference in its entirety.

[0055] As shown in FIG. 2, the processing unit 200 includes a signal analyzer 216 that analyzes the received cardiac electrical signal and / or the activation waveform generated by the activation waveform generator 214. In an embodiment, the signal analyzer 216 is configured to determine certain characteristics of the received cardiac electrical signal (e.g., average duty cycle value, activation duration, etc.). In an embodiment, the signal analyzer can determine the micro reentry probability based on the determined characteristics of the cardiac electrical signal. Additionally, the processing unit 200 includes a map engine 220 configured to facilitate the presentation of a map corresponding to the cardiac surface based on the electrical signal. In some embodiments, the map may include a voltage map, an activation map, a fractionation map, a velocity map, a reliability map, and / or the like. In some embodiments, the map may include overlaid annotations representing the characteristics of the cardiac electrical signal at the corresponding measurement locations.

[0056] The exemplary processing unit 200 shown in FIG. 2 is not intended to suggest any limitation as to the use or functionality of the embodiments of the present disclosure. The exemplary processing unit 200 should not be construed as having dependencies or requirements related to any single component or combination of components illustrated therein. Additionally, any one or more of the components shown in FIG. 2 may, in some embodiments, be integrated with various ones of the other components shown therein (and / or components not shown), and all of them are considered to be within the scope of the subject matter disclosed herein. For example, the acceptor 212 may be integrated with the mapping engine 220. In some embodiments, the processing unit 200 may not include the acceptor 212, but in other embodiments, the acceptor 212 may be configured to receive electrical signals from a memory device, a communication component, and / or the like.

[0057] In addition, the processing unit 200 may perform any number of different functions and / or processes related to electroanatomical mapping (e.g., triggering, blanking, field mapping, etc.), such as those described in U.S. Patent Application Publication No. 2018 / 0296113 entitled "ANNOTATION WAVEFORM", U.S. Patent No. 8,428,700 entitled "ELECTROANATOMICAL MAPPING", U.S. Patent No. 8,948,837 entitled "ELECTROANATOMICAL MAPPING", U.S. Patent No. 8,615,287 entitled "CATHETER TRACKING AND ENDOCARDIUM REPRESENTATION GENERATION", U.S. Patent Application Publication No. 2015 / 0065836 entitled "ESTIMATING THE PREVALENCE OF ACTIVATION PATTERNS IN DATA SEGMENTS DURING ELECTROPHYSIOLOGY MAPPING", U.S. Patent No. 6,070,094 entitled "SYSTEMS AND METHODS FOR GUIDING MOVABLE ELECTRODE ELEMENTS WITHIN MULTIPLE-ELECTRODE STRUCTURE", U.S. Patent No. 6,233,491 entitled "CARDIAC MAPPING AND ABLATION SYSTEMS", U.S. Patent No. 6,735,465 entitled "SYSTEMS AND PROCESSES FOR REFINING A REGISTERED MAP OF A BODY CAVITY", etc. (either alone and / or in combination with other components of the system 100 shown in FIG. 1 and / or other components not shown).

[0058] According to an embodiment, various components of the electrophysiological system 100 shown in FIG. 1 and / or the processing unit 200 shown in FIG. 2 can be implemented on one or more computing devices. The computing device can include any type of computing device suitable for implementing embodiments of the present disclosure. Examples of computing devices include dedicated computing devices or general-purpose computing devices such as "workstations", "servers", "laptops", "desktops", "tablet computers", "handheld devices", "general-purpose graphics processing units (GPGPUs)", etc., all of which are contemplated within the scope of FIGS. 1 and 2 in relation to the various components of the system 100 and / or the processing unit 200.

[0059] In some embodiments, the computing device includes the following devices, namely, a processor, a memory, an input / output (I / O) port, an I / O component, and a bus that directly and / or indirectly couples the power supply. Any number of additional components, different components, and / or combinations of components may be included in the computing device. The bus represents what may be one or more buses (such as an address bus, a data bus, or a combination thereof). Similarly, in some embodiments, the computing device may include multiple processors, multiple memory components, multiple I / O ports, multiple I / O components, and / or multiple power supplies. Additionally, any number of these components, or combinations thereof, may be distributed and / or replicated across multiple computing devices.

[0060] In some embodiments, the memory (e.g., storage device 160 shown in FIG. 1, memory 204 and / or data repository 206 shown in FIG. 2) includes computer-readable media in the form of volatile and / or non-volatile memory, temporary and / or non-temporary storage media, and may be removable, non-removable, or a combination thereof. Examples of media include random access memory (RAM), read only memory (ROM), electronically erasable programmable read only memory (EEPROM), flash memory, optical or holographic media, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, data transmission, and / or any other media that can be used to store information and can be accessed, for example, by a computing device such as a quantum state memory and / or the like. In some embodiments, memory 160 and / or 204 stores computer-executable instructions for causing a processor (e.g., processing unit 120 shown in FIG. 1 and / or processor 202 shown in FIG. 2) to implement aspects of the embodiments of the system components described herein and / or to execute aspects of the embodiments of the methods and procedures described herein.

[0061] Computer-executable instructions can include, for example, computer code, machine-usable instructions, and program components such as, for example, one or more program components executable by one or more processors associated with a computing device. Examples of such program components include acceptor 212, waveform generator 214, signal analyzer 216, and mapping engine 220. Program components can be programmed using any number of different programming environments, including various languages, development kits, frameworks, and / or the like. Some or all of the functions contemplated herein can also or alternatively be implemented in hardware and / or firmware.

[0062] The data repository 206 can be implemented using any one of the configurations described below. The data repository may include random access memory, flat files, XML files, and / or one or more database servers or one or more database management systems (DBMSs) running on a data center. The database management system may be a relational (RDBMS), hierarchical (HDBMS), multidimensional (MDBMS), object-oriented (ODBMS or OODBMS), or object-relational (ORDBMS) database management system, etc. The data repository may be, for example, a single relational database. In some cases, the data repository may include multiple databases that can exchange and aggregate data by a data integration process or a software application. In an exemplary embodiment, at least a part of the data repository 206 may be hosted in a cloud data center. In some cases, the data repository may be hosted on a single computer, server, storage device, cloud server, etc. In some other cases, the data repository may be hosted on a series of networked computers, servers, or devices. In some cases, the data repository may be hosted on a tier of data storage devices including local, regional, and central.

[0063] FIG. 3 is a flow diagram of an exemplary process 300 for automated anatomical mapping according to an embodiment of the present disclosure. Aspects of the exemplary process 300 embodiments may be performed, for example, by a processing unit (e.g., the processing unit 120 shown in FIG. 1 and / or the processing unit 200 shown in FIG. 2). First, a data stream 302 including a plurality of signals is input into a system (e.g., the cardiac mapping system 100 shown in FIG. 1). During the automated electroanatomical mapping process, the data stream 302 provides a set of physiological and non-physiological signals that function as inputs to the mapping process. The signals may be collected directly by the mapping system and / or obtained from another system using an analog or digital interface. The data stream 302 may include signals such as unipolar and / or bipolar intracardiac electrograms (EGMs), surface electrocardiograms (ECGs), electrode position information resulting from one or more of various methods (magnetic, impedance, ultrasonic, real-time MRI, etc.), tissue proximity information, catheter force and / or contact information obtained from one or more of various methods (force spring sensing, piezoelectric sensing, optical sensing, etc.), catheter tip and / or tissue temperature, acoustic information, catheter electrical coupling information, catheter deployment shape information, electrode characteristics, respiratory phase, blood pressure, other physiological information, and / or the like.

[0064] For generating a particular type of map, one or more signals are used as one or more references during a trigger / alignment process 304 to trigger and align a data stream 302 with respect to a cardiac cycle, other biological cycles, and / or an asynchronous system clock, resulting in a beat data set. Additionally, for each input beat data set, a plurality of beat metrics are calculated during a beat metric determination process 306. The beat metrics may be calculated using information from a single signal across a plurality of signals within the same beat and / or information from a plurality of signals across a plurality of beats. The beat metrics provide multiple types of information regarding the quality of a particular beat data set and / or the likelihood that the beat data is suitable for inclusion in a map data set. A beat acceptance process 308 aggregates the criteria and determines which beat data sets make up a map data set 310. The map data set 310 may be stored in relation to a 3D grid that is dynamically generated during data collection.

[0065] Surface geometry data 318 may be generated simultaneously during the same data acquisition process using a surface geometry construction process 312 that employs the same and / or different trigger and / or beat acceptance metrics. This process constructs the surface geometry using data such as electrode positions and catheter shapes included in the data stream. Additionally or alternatively, previously or simultaneously acquired surface geometry 316 may be used as an input to the surface geometry data 318. Such geometry may have been previously collected in the same procedure and registered to a catheter position determination system using different map data sets and / or different modalities such as CT, MRI, ultrasound, rotational angiography, and / or the like. The system performs a source selection process 314 to select a source for the surface geometry data and provides the surface geometry data 318 to a surface map generation process 320. The surface map generation process 320 is used to generate surface map data 322 from the map data set 310 and the surface geometry data 318.

[0066] The surface geometry construction algorithm generates an anatomical surface on which an electroanatomical map is displayed. The surface geometry can be constructed using system aspects such as those described in, for example, U.S. Patent No. 8,103,338 entitled "Impedance Based Anatomy Generation" and / or U.S. Patent No. 8,948,837 entitled "Electroanatomical Mapping", the entire contents of each of which are hereby incorporated by reference in their entirety. Additionally or alternatively, an anatomical shell can be constructed by the processing unit by conforming the surface at the electrode positions determined by the user or automatically to be the surface of the cardiac chamber. Additionally, the surface can be conformed to the outermost electrode and / or catheter positions within the cardiac chamber.

[0067] As described, the map data set 310 on which the surface is constructed can employ the same or different beat acceptance criteria as those used for electrical and other types of maps. The map data set 310 for surface geometry construction can be collected simultaneously with or separately from the electrical data. The surface geometry can be represented as a mesh that includes a set of vertices (points) and the connectivity between them (e.g., triangles). Alternatively, the surface geometry can be represented by different functions such as higher order meshes, non-uniform rational basis splines (NURBS), and / or curved shapes.

[0068] The generation process 320 generates surface map data 322. The surface map data 322 can provide information regarding cardiac electrical excitation, cardiac motion, tissue proximity information, tissue impedance information, force information, and / or any other collected information desired by the clinician. The combination of the map data set 310 and the surface geometry data 318 enables surface map generation. The surface map is a set of values or waveforms (e.g., EGM) on the surface of the cardiac chamber of interest, although the map data set can include data not on the cardiac surface. One approach for processing the map data set 310 and the surface geometry data 318 to obtain the surface map data set 322 is described in U.S. Patent No. 7,515,954, titled "NON-CONTACT CARDIAC MAPPING, INCLUDING MOVING CATHETER AND MULTI-BEAT INTEGRATION," filed on June 13, 2006, the content of which is incorporated herein by reference in its entirety.

[0069] Alternatively, or in combination with the above method, an algorithm can be used that applies acceptance criteria to individual electrodes. For example, electrode positions that are beyond a set distance (e.g., 3 mm) from the surface geometry can be rejected. Another algorithm can incorporate tissue proximity information using impedance for inclusion in the surface map data. In this case, only electrode positions with a proximity value less than 3 mm can be included. Additional metrics of the underlying data can also be used for this purpose. For example, EGM characteristics similar to the fibrillation metric can be evaluated for each electrode. In this case, metrics such as far-field overlap and / or EGM consistency can be used. It should be understood that there can be variations of the method for projecting points from the map data set 310 to the surface and / or for selecting appropriate points.

[0070] Once obtained, the surface map data 322 may be further processed to annotate the underlying data with desired characteristics, and the process is defined as surface map annotation 324. When data is collected into the surface map data 322, characteristics regarding the collected data may be automatically presented to the user. These characteristics are automatically determined by a computer system and may be applied to the data, and are referred to herein as annotations. Exemplary annotations include activation time, presence of double activation or fractionation, voltage amplitude, spectral content, average duty cycle, and / or the like. In automated mapping (e.g., mapping completed by a computer system with minimal human input related to the input data), since there is abundant available data, it is not practical for an operator to manually review and annotate the data. However, since user input can add value to the data, if user input is provided, the computer system needs to automatically propagate it and apply it to multiple data points at once.

[0071] It may be possible to automatically annotate the average duty cycle and other characteristics of individual or aggregated EGMs using a computer system. The calculation and determination of the average duty cycle and other characteristics are described in detail in the disclosure herein. Once calculated, the annotations may be displayed overlaid on the cardiac chamber geometry. In some embodiments, gap-filling surface map interpolation may be employed (326). For example, in some embodiments, if the distance between a point on the surface and the measured EGM exceeds a threshold, this may indicate that grid-based interpolation, as described herein, may not be effective in that situation, and gap-filling interpolation may be used. The displayed maps 328 may be calculated and displayed separately and / or overlaid on top of each other.

[0072] The exemplary process 300 shown in FIG. 3 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. The exemplary process 300 should not be construed as having dependencies or requirements related to any single component or combination of components shown therein. Additionally, any one or more of the components shown in FIG. 3 may be integrated with various ones of the other components shown therein (and / or components not shown), and all of them are considered to be within the scope of the present disclosure.

[0073] FIG. 4A is an exemplary flow diagram showing an exemplary method 400A for processing cardiac electrical signals and generated activation waveforms according to some embodiments of the present disclosure. Aspects of embodiments of method 400A may be performed, for example, by an electrophysiological system or processing unit (e.g., the processing unit 120 shown in FIG. 1 and / or the processing unit 200 shown in FIG. 2). One or more steps of method 400A are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to method 400A. First, the electrophysiological system receives (410A) one or more cardiac electrical signals collected from one or more electrodes disposed within the heart chamber, and the cardiac electrical signals are acquired over the heartbeat. In some cases, the cardiac electrical signals include an intracardiac electrogram (EGM). FIG. 5A shows an exemplary graphical representation 500 of electrical signals (in this case, EGM) received from a mapping catheter, each representing the magnitude of the depolarization sequence of the heart over a predetermined period. In this example, the EGM of a mapping catheter having 64 electrodes is shown. Each waveform can represent a unipolar signal received from an electrode of the mapping catheter. In some cases, each waveform representing a cardiac electrical signal can represent a multipolar (e.g., bipolar, tripolar) signal received from the electrode.

[0074] The system may receive an indication of a measurement location corresponding to each of the cardiac electrical signals (415A). In some embodiments, the system can analyze one or more cardiac electrical signals to determine an activation duration (420A) associated with a heartbeat of the heart. In an embodiment, the activation duration may represent the length of activation. That is, for example, a cardiac electrical signal (e.g., an EGM) may include portions where all of the amplitudes deviate beyond a signal baseline according to a specified criterion. The length of the period corresponding to that portion of the cardiac electrical signal can be identified as the activation duration.

[0075] In some cases, the system analyzes the cardiac electrical signals to calculate a plurality of duty cycle values, where each calculated duty cycle value corresponds to one of a plurality of preselected cycle length windows (430A). In some cases, each duty cycle value is calculated by dividing the activation duration by one of the plurality of preselected cycle length windows.

[0076] The system further calculates an average of the calculated duty cycle values over a plurality of preselected cycle length windows (435A). In an embodiment, the calculated average duty cycle represents the probability that the cardiac tissue corresponding to the measurement location defines a microreentry site. In some cases, the plurality of preselected cycle length windows are in the range of 140 milliseconds to 2000 milliseconds.

[0077] In an embodiment, the system facilitates (440A) the presentation on a display device of a three-dimensional electroanatomical map overlaid with an annotation representing a calculated average duty cycle based on the measurement location of the cardiac electrical signals associated therewith. In some cases, the system determines a plurality of average duty cycles at a plurality of sites. In some cases, the system includes a display device (e.g., 170 of FIG. 1) operably connected to a processing unit (e.g., 120 of FIG. 1, 200 of FIG. 2) and configured to display a three-dimensional anatomical map overlaid with an annotation. FIG. 6 shows an example for the illustration of an anatomical map with an annotation representing an average duty cycle value. In some cases, the value of the average duty cycle is represented by color. In some cases, the value of the average duty cycle is represented by a grayscale value.

[0078] In some cases, the system is configured to calculate an average duty cycle value for each cardiac electrical signal. In some embodiments, the system aggregates a plurality of cardiac electrical signals having associated measurement locations within a specified region and is configured to calculate an average duty cycle value for the aggregated cardiac electrical signals. In some cases, the specified region has a predetermined geometry shape and size.

[0079] Figure 4B is an exemplary flow diagram showing an exemplary method 400B for processing cardiac electrical signals and generated activation waveforms, according to some embodiments of the present disclosure. Aspects of embodiments of method 400B may be performed, for example, by an electrophysiology system or processing unit (e.g., processing unit 120 shown in FIG. 1 and / or processing unit 200 shown in FIG. 2). One or more steps of method 400B are optional and / or may be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to method 400B. First, the electrophysiology system receives one or more cardiac electrical signals collected by one or more electrodes disposed within the heart chamber over a cardiac beat (410B). In one embodiment, the cardiac electrical signals include an intracardiac electrogram (EGM). The system further receives an indication of the measurement location of the cardiac electrical signals (415B). In some cases, each of the cardiac electrical signals has a corresponding measurement location.

[0080] In some embodiments, the electrophysiology system generates activation waveforms (420B) based on cardiac electrical signals. The activation waveforms include a plurality of activation waveform values. In some cases, generating the activation waveforms includes identifying deflections of one or more cardiac electrical signals from a signal baseline. In some embodiments, each activation waveform value is associated with a probability of a specified deflection representing activation. For example, in an embodiment, the system may include determining a probability (e.g., a value between 0 and 1, including 0 and 1) that a given sample point represents activation based on its relationship to the signal baseline. In embodiments, other numerical scales may be used, for example, to assign probabilities such as values between 0 and 100 and / or the like. In embodiments, the likelihood (e.g., probability) that a signal deflection represents activation may be determined based on the deviation of that deflection from the signal baseline. For example, a deflection having a maximum amplitude that deviates from the signal baseline by at least a specified amount may be assigned a probability of 1, while a deflection having a maximum amplitude that deviates from the signal baseline by at most the specified amount may be assigned a probability of 0.

[0081] FIG. 5A shows an exemplary graphical representation 500 of electrical signals (in this case, EGMs) received from a mapping catheter, each representing the magnitude of a cardiac depolarization sequence over a predetermined period. In this example, the EGMs of a mapping catheter having 64 electrodes are shown. Each waveform can represent a unipolar signal received from an electrode of the mapping catheter. In some cases, each waveform representing a cardiac electrical signal can represent a multipolar (e.g., bipolar, tripolar) signal received from the electrode. FIG. 5B shows the waveform of the raw cardiac electrical signal 502 and the activation waveform 504 corresponding to the cardiac electrical signal 502.

[0082] Returning to FIG. 4B, in some embodiments, the electrophysiology system receives or selects a plurality of cycle length windows (425B). In some cases, the system selects a plurality of cycle length windows to be in a predetermined range, for example, in the range of 120 ms to 2000 ms. In some embodiments, the system receives inputs associated with the plurality of cycle length windows by, for example, user input (e.g., input via a user interface such as a graphical user interface), system input (e.g., system configuration), software input (e.g., input via an application programming interface, web service, etc.), and / or the like. In such embodiments, the system selects the plurality of cycle length windows based on the input. In one example, the system receives an input of a timing reference such as 400 ms and sets a range of 200 ms to 1600 ms. In one embodiment, the plurality of cycle length windows are increased linearly (e.g., every 10 ms). In another embodiment, the plurality of cycle length windows are increased non-linearly.

[0083] In some embodiments, the system can analyze one or more cardiac electrical signals to determine an activation duration associated with a heartbeat of the heart. In an embodiment, the activation duration can represent the length of the activation. In some cases, the system determines the activation duration based on an activation waveform. In an embodiment, the activation waveform may be represented along a time scale, in which case the waveform can represent the activation duration. For example, the width of a deflection in the activation waveform can represent the corresponding activation duration.

[0084] In some embodiments, the system determines a plurality of duty cycle values corresponding to a plurality of cycle length windows based on the activation waveform (430B). In some embodiments, the duty cycle value of a cycle length window can be determined as the average of the activation waveform values within the cycle length window. In the example shown in FIG. 5B, a cycle length window 506 of 250 ms is selected and the duty cycle is determined to be 0.26. In some embodiments, the duty cycle value is the activation duration divided by each of the plurality of cycle length windows.

[0085] The system further calculates an average of the plurality of duty cycles (435B). In some embodiments, the average is an arithmetic mean. In some embodiments, the average is a weighted average. The electrophysiological system can facilitate the presentation of a 3D heart map overlaid with an annotation representing the calculated average of the duty cycle values (440B). In an embodiment, each annotation is at the corresponding measurement location. In an embodiment, the calculated average duty cycle represents the probability that the heart tissue corresponding to the measurement location defines a microreentry site. In some cases, the heart map is overlaid with an annotation of the microreentry probability, and the microreentry probability is represented by the calculated average of the duty cycle values. FIG. 6 shows an exemplary electroanatomical map 600 annotated according to one embodiment. In this example, the electroanatomical map 600 is overlaid with an annotation of the microreentry probability 610. A legend for the microreentry probability is shown at 614. The 3D electroanatomical map can be a grayscale image or a color image. In some cases, the values of the average duty cycle and / or the microreentry probability are represented by color and / or grayscale values. In the example shown in FIG. 6, the region 612 has a high probability of microreentry AT.

[0086] Without departing from the scope of the present invention, various modifications and additions can be made to the exemplary embodiments described. For example, the above embodiments refer to specific features, but the scope of the present invention also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present invention is intended to encompass all such alternatives, modifications, and variations that fall within the scope of the claims, together with all of their equivalents. The technical idea included in the present disclosure is described below as an appendix. (Appendix 1) A system for processing cardiac information, comprising a processing unit configured to receive one or more cardiac electrical signals from one or more electrodes disposed within a cardiac chamber, the cardiac electrical signals being acquired over a cardiac beat, the processing unit receives an indication of a measurement location corresponding to each of the cardiac electrical signals, analyzes the one or more cardiac electrical signals to calculate a plurality of duty cycle values, each calculated duty cycle value corresponding to one of a plurality of preselected cycle length windows, the processing unit calculates an average duty cycle of the calculated duty cycle values over the plurality of preselected cycle length windows, facilitates presentation on a display device of a three-dimensional electroanatomical map overlaid with an annotation representing the calculated average duty cycle based on the measurement location of the associated cardiac electrical signal A system configured as described above. (Appendix 2) The cardiac electrical signal includes an intracardiac electrogram (EGM), the system according to Appendix 1. (Appendix 3) The processing unit is configured to determine an activation duration associated with the cardiac beat and calculate the plurality of duty cycle values by dividing the activation duration by each of the plurality of preselected cycle length windows, the system according to Appendix 2. (Appendix 4) Analysis of the one or more cardiac electrical signals includes generating an activation waveform therefrom, the activation waveform being based on deflections of the one or more analyzed cardiac electrical signals from a signal baseline, the system according to Appendix 2. (Appendix 5) The processing unit is further configured to determine an activation duration based on the activation waveform, the system according to Appendix 4. (Appendix 6) The activation waveform includes a value indicating the probability that the deflection represents activation of cardiac tissue, the system according to Appendix 4 or 5. (Appendix 7) The processing unit is configured to calculate the plurality of duty cycle values by calculating an average activation waveform value across each of the plurality of pre-selected cycle length windows, the system according to any one of Appendices 4 to 6. (Appendix 8) The calculated average duty cycle represents the probability that the heart tissue corresponding to the measurement location defines a micro-reentry site, the system according to any one of Appendices 1 to 7. (Appendix 9) The plurality of pre-selected cycle length windows are in the range of 140 milliseconds to 2000 milliseconds, the system according to any one of Appendices 1 to 8. (Appendix 10) The system according to any one of Appendices 1 to 9, further comprising a display device operably connected to the processing unit and configured to display a three-dimensional anatomical map overlaid with the annotation. (Appendix 11) The processing unit is configured to calculate an average duty cycle value for each cardiac electrical signal, the system according to any one of Appendices 1 to 10. (Appendix 12) The processing unit is configured to aggregate a plurality of cardiac electrical signals having associated measurement locations within a specified region and calculate an average duty cycle value for the aggregated cardiac electrical signals, the system according to any one of Appendices 1 to 10. (Appendix 13) A method for processing cardiac information, Receiving one or more cardiac electrical signals acquired over a heartbeat of the heart, Analyzing the one or more cardiac electrical signals and calculating a plurality of duty cycle values Each calculated duty cycle value corresponds to one of a plurality of pre-selected cycle length windows, The method includes: Calculating an average of the calculated duty cycle values over the plurality of pre-selected cycle length windows, Displaying on a display device a three-dimensional anatomical map overlaid with an annotation representing the calculated average duty cycle A method. (Appendix 14) Analyzing the one or more cardiac electrical signals includes generating an activation waveform therefrom, the activation waveform being based on the deflection of the one or more analyzed cardiac electrical signals from a signal baseline, the method according to Appendix 13. (Appendix 15) Calculating the plurality of duty cycle values includes calculating the plurality of duty cycle values based on the activation waveform and each of the plurality of preselected cycle length windows, the method according to appendix 13 or 14.

Claims

1. A system for processing cardiac information, comprising a processing unit, wherein the processing unit receives one or more cardiac electrical signals from one or more electrodes disposed within the cardiac cavity, wherein the cardiac electrical signals are acquired over a cardiac beat and include one or more cardiac electrical signals including an intracardiac electrogram (EGM), receives an indication of a measurement position corresponding to each of the cardiac electrical signals, for each cardiac electrical signal, generates an activation waveform based on a deviation of the cardiac electrical signal from a signal baseline, wherein the activation waveform comprises activation waveform values indicating a probability that a deviation in the amplitude of the cardiac electrical signal represents activation of cardiac tissue, determines an activation duration defined by a width of a deviation in the activation waveform, analyzes the cardiac electrical signal to calculate a plurality of duty cycle values, wherein each calculated duty cycle value is determined by dividing the activation duration by one of a plurality of preselected cycle length windows, calculates an average duty cycle of the calculated duty cycle values over the plurality of preselected cycle length windows, facilitates presentation on a display device of a three-dimensional electroanatomical map overlaid with an annotation representing the calculated average duty cycle at an associated measurement position configured to perform. A system.

2. The system according to claim 1, wherein the calculated average duty cycle represents a probability that cardiac tissue corresponding to the measurement position defines a microreentry site.

3. The system according to claim 1 or 2, wherein the plurality of preselected cycle length windows are in the range of 140 milliseconds to 2000 milliseconds.

4. The system according to any one of claims 1 to 3, further comprising a display device operably connected to the processing unit and configured to display a three-dimensional anatomical map overlaid with the annotation.

5. The system according to any one of claims 1 to 4, wherein the processing unit is configured to aggregate a plurality of cardiac electrical signals having associated measurement positions within a specified region and to calculate an average duty cycle value for the aggregated cardiac electrical signals. **Claim 6** A method for processing cardiac information by a processing unit, the processing unit receiving one or more cardiac electrical signals acquired over a cardiac beat, for each cardiac electrical signal, the processing unit generating an activation waveform based on the deviation of the cardiac electrical signal from a signal baseline, the activation waveform comprising activation waveform values indicative of the probability that the deviation in the amplitude of the cardiac electrical signal represents activation of cardiac tissue, generating an activation waveform; the processing unit determining an activation duration defined by the width of the deviation in the activation waveform; the processing unit analyzing the cardiac electrical signal and calculating a plurality of duty cycle values, each calculated duty cycle value being determined by dividing the activation duration by one of a plurality of preselected cycle length windows, calculating a plurality of duty cycle values; the processing unit calculating an average of the calculated duty cycle values over the plurality of preselected cycle length windows; the processing unit causing a display device to display a three-dimensional anatomical map annotated with an annotation representing the calculated average duty cycle comprising a method.

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