Electrophysiological systems and methods for identifying significant electrograms - Patents.com

Abstract

At least some embodiments of the present disclosure are directed to a system for processing cardiac information, including a processing unit configured to receive a plurality of cardiac electrical signals acquired from a plurality of electrodes disposed in a cardiac chamber, the plurality of cardiac electrical signals acquired over a cardiac beat having a cycle length, and to calculate a significance index for each of the plurality of cardiac electrical signals, the significance index representing a contribution of each cardiac electrical signal to an overall duty cycle of the cardiac beat according to the cycle length.

Description

[Technical Field]

[0001] This disclosure relates to electrophysiological systems and methods for processing cardiac electrical signals. This application claims priority to U.S. Provisional Patent Application No. 63 / 085,671, filed September 30, 2020, which is incorporated herein by reference in its entirety. [Background technology]

[0002] The use of minimally invasive procedures, such as catheter ablation, to treat various cardiac conditions, including supraventricular and ventricular arrhythmias, is becoming increasingly popular. Such procedures involve mapping electrical activity (based on cardiac signals) at various locations within the heart, such as on the endocardial surface, to identify the site of origin of the arrhythmia and subsequently perform targeted ablation of that site ("cardiac mapping"). To perform such cardiac mapping, a catheter with one or more electrodes can be inserted into a patient's heart chambers.

[0003] Conventional three-dimensional (3D) mapping techniques include contact mapping, non-contact mapping, and combined contact and non-contact mapping. In both contact and non-contact mapping, one or more catheters are advanced into the heart. Some catheters can be deployed to assume a 3D shape once inside a heart chamber. In contact mapping, physiological signals resulting from cardiac electrical activity are acquired by one or more electrodes located at the distal tip of the catheter after determining that the tip is stable and in steady contact with the endocardial surface of a specific heart chamber. In non-contact mapping systems, the system uses signals detected by the non-contact electrodes, as well as information about the anatomical structure of the heart chamber and its relative location, to provide physiological information about the endocardium of the heart chamber. Location and electrical activity are typically measured continuously at approximately 50 to 200 points on the interior surface of the heart to construct an electroanatomical depiction of the heart. The generated map can then serve as the basis for determining a course of treatment, such as tissue ablation, to alter the propagation of cardiac electrical activity and restore normal heart rhythm.

[0004] In many conventional mapping systems, clinicians visually inspect or review captured electrograms (EGMs), which increases inspection time and costs. However, during automated electroanatomical mapping, approximately 6,000–20,000 intracardiac electrograms (EGMs) may be captured, which, in and of themselves, are not suitable for manual inspection by a clinician (e.g., a physician) for diagnostic evaluation, EGM categorization, etc. Typically, mapping systems extract scalar values ​​from each EGM to construct voltage, activation, or other map types to depict the overall pattern of activity within the heart. While this map reduces the need to inspect captured EGMs, it also simplifies the often complex and useful information within the EGM. Furthermore, maps can be subject to misinterpretation due to electrical artifacts or inappropriate selection of features such as activation time. Additionally, due to the complex nature of conventional techniques, cardiac maps are often unsuitable for accurate and efficient interpretation. Summary of the Invention

[0005] As described in the embodiments, Example 1 is a system for processing cardiac information, including a processing unit configured to receive a plurality of cardiac electrical signals acquired from a plurality of electrodes disposed in a cardiac chamber, the plurality of cardiac electrical signals acquired over a cardiac beat having a cycle length, calculate a significance index for each of the plurality of cardiac electrical signals, the significance index representing a contribution of each cardiac electrical signal to an overall duty cycle of the cardiac beat according to the cycle length, and enable presentation of a graphical representation of a selected plurality of cardiac electrical signals on a display device, the selected plurality of cardiac electrical signals each satisfying a selection criterion based on the corresponding significance index.

[0006] Example 2 is the system of example 1, wherein each of the plurality of cardiac electrical signals includes an intracardiac electrogram (EGM). Example 3 is the system of example 1 or 2, wherein the processing unit is further configured to generate a plurality of excitation waveforms based on the plurality of cardiac electrical signals.

[0007] Example 4 is the system of example 3, wherein the processing unit is further configured to identify, for each of the plurality of cardiac electrical signals, a deflection comprising a deviation from a signal baseline; and wherein each of the plurality of excitation waveforms is generated based on each identified deflection, the excitation waveform comprising an excitation waveform value corresponding to a likelihood that the identified deflection represents activation of cardiac tissue.

[0008] Example 5 is the system of Example 3, wherein the importance index is calculated based on an excitation index and a novelty index, the excitation index representing a first contribution of the corresponding cardiac electrical signal to an excitation zone, and the novelty index representing a second contribution of the corresponding cardiac electrical signal to outside the excitation zone.

[0009] A sixth embodiment is the system of the fifth embodiment, in which the excitation index is calculated based on an average value of the excitation waveform values. Example 7 is the system of example 6, wherein the excitation index is calculated by normalizing the average value of the excitation waveform values.

[0010] Example 8 is the system of Example 5, further comprising selecting one or more excitation waveforms based on the excitation index, and determining an excitation zone waveform representing an excitation zone based on the selected one or more excitation waveforms.

[0011] Example 9 is the system of example 8, wherein each of the selected one or more excitation waveforms has an excitation index greater than a predetermined excitation index threshold. Example 10 is the system of Example 8, wherein the novelty index is calculated using multiple weighting factors based on the excitation zone waveform, a first weighting factor corresponds to a first excitation zone value, a second weighting factor corresponds to a second excitation zone value, and the first weighting factor is greater than the second weighting factor when the first excitation zone value is less than the second excitation zone value.

[0012] Example 11 is the system of any one of Examples 5 to 10, wherein the importance index is determined by applying a non-linear function to the excitement index and the novelty index. Example 12 is the system of any one of Examples 1 to 11, wherein the selection criteria include the importance index being greater than a predetermined threshold.

[0013] Example 13 is a method for processing cardiac information, the method comprising: receiving a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a cardiac chamber, the plurality of cardiac electrical signals acquired over a cardiac beat having a cycle length; calculating an importance index for each of the plurality of cardiac electrical signals, the importance index representing a contribution of each cardiac electrical signal to an overall duty cycle of the cardiac beat according to the cycle length; and enabling presentation of a graphical representation on a display device of a selected plurality of cardiac electrical signals, each of the selected plurality of cardiac electrical signals meeting a selection criterion based on a corresponding importance index.

[0014] Example 14 is the method of Example 13, further comprising generating a plurality of excitation waveforms based on the plurality of cardiac electrical signals, each of the plurality of excitation waveforms being generated based on a deflection of the plurality of cardiac electrical signals from a signal baseline, the excitation waveform including an excitation waveform value corresponding to a likelihood that the identified deflection represents excitation of cardiac tissue.

[0015] Example 15 is the method of Example 13 or 14, wherein the importance index is calculated based on an excitation index and a novelty index, the excitation index representing a first contribution of the corresponding cardiac electrical signal to an excitation zone, and the novelty index representing a second contribution of the corresponding cardiac electrical signal to outside the excitation zone, and the excitation index and the excitation zone are determined based on the excitation waveform value.

[0016] Example 16 is a system for processing cardiac information, comprising a processing unit configured to: receive a plurality of cardiac electrical signals collected from a plurality of electrodes disposed in a cardiac chamber, the plurality of cardiac electrical signals acquired over a cardiac beat having a cycle length; calculate a significance index for each of the plurality of cardiac electrical signals, the significance index representing a contribution of each cardiac electrical signal to an overall duty cycle of the cardiac beat according to the cycle length; and enable presentation of a graphical representation on a display device of the selected plurality of cardiac electrical signals, each of the selected cardiac electrical signals satisfying a selection criterion based on a corresponding significance index.

[0017] Example 17 is the system of example 16, wherein each of the plurality of cardiac electrical signals comprises an intracardiac electrogram (EGM). Example 18 is the system of example 16, wherein the processing unit is further configured to generate a plurality of excitation waveforms based on the plurality of cardiac electrical signals.

[0018] Example 19 is the system of Example 18, wherein the processing unit is further configured to identify, for each of the plurality of electrical signals, a deflection comprising a deviation from a signal baseline, and each of the plurality of excitation waveforms is generated based on each identified deflection, the excitation waveform including an excitation waveform value corresponding to a likelihood that the identified deflection represents excitation of cardiac tissue.

[0019] Example 20 is the system of Example 18, wherein the importance index is calculated based on an excitation index and a novelty index, the excitation index representing a first contribution of the corresponding cardiac electrical signal to an excitation zone, and the novelty index representing a second contribution of the corresponding cardiac electrical signal to outside the excitation zone.

[0020] A twenty-first embodiment is the system of the twentieth embodiment, wherein the excitation index is calculated based on an average value of the excitation waveform values. Example 22 is the system of example 21, wherein the excitation index is calculated by normalizing the average value of the excitation waveform values.

[0021] Example 23 is the system of Example 20, further comprising the steps of selecting one or more excitation waveforms based on the excitation index, and determining an excitation zone waveform representing an excitation zone based on the selected one or more excitation waveforms.

[0022] Example 24 is the system of example 23, wherein each of the selected one or more excitation waveforms has an excitation index greater than a predetermined excitation index threshold. Example 25 is the system of Example 23, wherein the novelty index is calculated using multiple weighting factors based on the excitation zone waveform, a first weighting factor corresponds to a first excitation zone value, a second weighting factor corresponds to a second excitation zone value, and the first weighting factor is greater than the second weighting factor when the first excitation zone value is less than the second excitation zone value.

[0023] Example 26 is the system of example 20, wherein the importance index is determined by applying a non-linear function to the excitement index and the novelty index. Example 27 is the system of example 16, wherein the selection criteria include the importance index being greater than a predetermined threshold.

[0024] Example 28 is a method for processing cardiac information, the method comprising: receiving a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a ventricle, the plurality of cardiac electrical signals acquired over a cardiac beat having a cycle length; calculating an importance index for each of the plurality of cardiac electrical signals, the importance index representing the contribution of each cardiac electrical signal to an overall duty cycle of the cardiac beat according to the cycle length; and enabling presentation of a graphical representation of a selected plurality of cardiac electrical signals on a display device, the selected plurality of cardiac electrical signals each satisfying a selection criterion based on a corresponding importance index.

[0025] Example 29 is the method of Example 28, further comprising generating a plurality of excitation waveforms based on the plurality of cardiac electrical signals, each of the plurality of excitation waveforms being generated based on a deflection of the plurality of cardiac electrical signals from a signal baseline, and the excitation waveform including an excitation waveform value corresponding to a likelihood that the identified deflection represents excitation of cardiac tissue.

[0026] Example 30 is the system of Example 29, wherein the importance index is calculated based on an excitation index and a novelty index, the excitation index representing a first contribution of the corresponding cardiac electrical signal to the excitation zone, and the novelty index representing a second contribution of the corresponding cardiac electrical signal to outside the excitation zone, and the excitation index and the excitation zone are determined based on the excitation waveform value.

[0027] Example 31 is the method of example 28, wherein each of the plurality of cardiac electrical signals comprises an intracardiac electrogram (EGM). Example 32 is the method of Example 30, wherein the excitation index is calculated based on an average value of the excitation waveform values.

[0028] Example 33 is the method of example 32, wherein the excitation index is calculated by normalizing the average value of the excitation waveform values. Example 34 is the method of example 30, further comprising selecting one or more excitation waveforms based on the excitation index, and determining an excitation zone waveform representing an excitation zone based on the selected one or more excitation waveforms.

[0029] Example 35 is the method of example 34, wherein each of the selected one or more excitation waveforms has an excitation index greater than a predetermined excitation index threshold. While multiple embodiments are disclosed, still other embodiments of the present invention will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. [Brief explanation of the drawings]

[0030] [Figure 1] 1 is a schematic diagram of an exemplary embodiment of an electrophysiology system. [Figure 2] FIG. 2 is a block diagram of an exemplary processing unit, according to an embodiment of the present disclosure. [Figure 3A] 1 is an exemplary flow diagram illustrating an exemplary method for processing cardiac electrical signals to assess a significance index. [Figure 3B] 1 is an exemplary flow diagram illustrating an exemplary method for processing cardiac electrical signals to assess a significance index. [Figure 4A] 1A and 1B show illustrative examples of multiple cardiac electrical signals and multiple corresponding excitation waveforms. [Figure 4B] 1A-1C show illustrative examples of cardiac electrical signals, excitation waveforms and normalized mean values. [Figure 4C] FIG. 10 shows illustrative examples of excitation zones. [Figure 4D] FIG. 10 shows illustrative examples of excitation waveforms, novelty waveforms, and novelty scores. [Figure 4E] 1A-1C show illustrative examples of cardiac electrical signals and cardiac electrical signal selection with corresponding importance indices. [Figure 4F] 1A-1C illustrate illustrative examples of multiple cardiac electrical signals and selected cardiac electrical signals. DETAILED DESCRIPTION OF THE INVENTION

[0031] While the invention is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. However, there is no intention to limit the invention to the specific embodiments described, and the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.

[0032] When the terms are used herein with respect to measurements (e.g., dimensions, characteristics, attributes, components, etc.) and ranges thereof of tangible (e.g., products, inventory, etc.) and / or intangible (e.g., data, electronic representations of currency, accounts, information, portions of things (e.g., percentages, portions), calculations, data models, dynamic system models, algorithms, parameters, etc.), "about" and "approximately" may be used interchangeably to indicate a measurement that includes the referenced measurement and also includes any measurement that is reasonably close to the referenced measurement but may differ by a reasonably small amount, where such a reasonably small amount differs by measurement error, measurement variation, and / or or manufacturing equipment calibration, human error in reading and / or setting measurements, adjustments made to optimize performance and / or structural parameters in light of other measurements (e.g., measurements relative to other objects), specific implementation scenarios, imprecise adjustment and / or manipulation of objects, set points, and / or measurements by humans, computing devices, and / or machines, system tolerances, control loops, machine learning, predictable variations (e.g., statistically insignificant variations, chaotic variations, system and / or model instability, etc.), preferences, and / or the like, as would be understood and readily ascertainable by one of ordinary skill in the relevant art.

[0033] While an example method may be represented by one or more diagrams (e.g., flow charts, communication flows, etc.), the diagrams should not be construed as indicating any requirement of, or a particular order among, or between, the various steps disclosed herein. However, some embodiments may require certain steps and / or a particular order among certain steps, as explicitly described herein and / or as can be understood from the nature of the steps themselves (e.g., performance of some steps may depend on the results of previous steps). 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 two or more.

[0034] As used herein, the term "based on" is not meant to be limiting but rather indicates that a determination, identification, prediction, calculation, and / or the like is performed using at least the term following "based on" as an input. For example, predicting an outcome based on particular information may additionally or alternatively be performed when the same determination is based on other information.

[0035] Embodiments of the present disclosure facilitate evaluating importance metrics for signals collected from electrodes in terms of their contribution to a cardiac beat. In embodiments, the importance metrics represent contributions to a duty cycle of a cardiac beat having a cycle length, where the duty cycle represents the percentage of activation during that cycle length. Embodiments of the present disclosure facilitate evaluating importance metrics for signals collected from electrodes based on an excitation waveform generated from a cardiac electrical signal. An excitation waveform, or an annotation waveform, is a set of excitation waveform values, which may include, for example, discrete excitation waveform values ​​(e.g., a set of excitation waveform values, a set of activation time annotations, etc.), a function defining an excitation waveform curve, and / or the like. In some embodiments, each data point of an excitation waveform represents a sample-by-sample “likelihood” of tissue activation. In some embodiments, cardiac electrical signals and / or excitation waveforms may be displayed, used for presentation in an excitation propagation map, used to facilitate diagnosis, used to facilitate classification (e.g., importance metrics) of cardiac electrical signals, and / or other similar forms. To perform aspects of the method embodiments 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 equipment typically used in electrophysiology laboratories, such as recording systems, coronary sinus (CS) catheters or other reference catheters, ablation catheters, memory devices (e.g., local memory, cloud servers, etc.), communication components, medical devices (e.g., implantable medical devices, external medical devices, telemetry devices, etc.), and / or the like.

[0036] As used herein, the term sensed cardiac electrical signal may refer to one or more sensed signals. Each cardiac electrical signal may include an intracardiac electrogram (EGM) sensed within a patient's heart and may include any number of features that can be ascertained by an embodiment of an electrophysiological system. A cardiac electrical signal, also referred to as an electrical signal, may be an electrogram (EGM), a filtered EGM, a set of absolute values ​​of the EGM, a peak value of the EGM at the peak location, a combination of these, and / or the like. For example, a cardiac electrical signal may be represented as a set of ordered values ​​(e.g., the amplitude of each sample point may be one value in the set), the specified percentile and / or multiplier of which may be used to define a signal baseline.

[0037] Examples of cardiac electrical signal features include, but are not limited to, excitation time, excitation, excitation waveform, filtered excitation waveform, minimum voltage value, maximum voltage value, maximum negative time-derivatives of voltages, instantaneous potential, voltage amplitude, dominant frequencies, peak-to-peak voltage, and / or the like. The cardiac electrical signal features may represent one or more features extracted from one or more cardiac electrical signals, one or more features extracted from one or more features extracted from one or more cardiac electrical signals, and / or the like. In embodiments, the cardiac electrical signal features may include a significance indicator. Additionally, the representation of the cardiac electrical signal features may represent one or more cardiac electrical signal features, an interpolation of several cardiac electrical signal features, and / or the like. In some cases, the representation of the cardiac electrical signal is on a cardiac map and / or a surface map.

[0038] Each cardiac signal may also be associated with a respective set of position coordinates corresponding to where the cardiac electrical signal was sensed. Each of the 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 may be used. In some embodiments, an arbitrary origin is used, and each position coordinate indicates a spatial location relative to the arbitrary origin. In some embodiments, the cardiac signals may be sensed on the cardiac surface, and thus each position coordinate may be on the endocardial surface, on the epicardial surface, within the midmyocardium of the patient's heart, and / or near one of these.

[0039] FIG. 1 shows a schematic diagram of an exemplary embodiment of an electrophysiology system 100. As indicated above, embodiments of the subject matter disclosed herein may be implemented in an electrophysiology system (e.g., a mapping system, a cardiac mapping system), although other embodiments may be implemented in an ablation system, a recording system, a computer analysis system, and / or the like. The electrophysiology system 100 includes a steerable catheter 110 having multiple spatially distributed electrodes. During a signal acquisition phase, the catheter 110 is displaced to multiple locations within a heart chamber into which the catheter 110 is inserted. In some embodiments, the distal end of the catheter 110 is fitted with multiple electrodes that are spread somewhat uniformly over the catheter. For example, the electrodes may be mounted on the catheter 110 following a 3D olive shape, a basket shape, and / or the like. The electrodes are mounted on a device that can deploy the electrodes into a desired shape while in the heart and retract the electrodes when the catheter is removed from the heart. To enable deployment into a 3D shape within the heart, the electrodes may be mounted on a balloon, a shape-memory material such as Nitinol, an actuable hinged structure, and / or the like. According to embodiments, catheter 110 may be a mapping catheter, an ablation catheter, a diagnostic catheter, a CS catheter, and / or the like. For example, aspects of embodiments of catheter 110, the electrical signals obtained using catheter 110, and the subsequent processing of the electrical signals as described herein may also be applicable in embodiments having recording systems, ablation systems, and / or any other system having a catheter with electrodes that may be configured to obtain cardiac electrical signals.

[0040] At each location along which the catheter 110 is moved, the catheter's multiple electrodes acquire signals resulting from electrical activity within the heart. As a result, physiological data regarding the heart's electrical activity can be reconstructed and presented to a user (e.g., a physician and / or technician) based on information acquired at multiple locations, thereby resulting in a more accurate and faithful reconstruction of the physiological behavior of the endocardial surface. Acquisition of signals at multiple catheter locations within the heart chamber allows the catheter to effectively act as a "mega-catheter," with the effective number of electrodes and electrode span of the catheter proportional to the product of the number of locations within which signal acquisition is performed and the number of electrodes the catheter possesses.

[0041] To improve the quality of the reconstructed physiological information at the endocardial surface, in some embodiments, the catheter 110 is moved to more than three locations within the heart chamber (for example, more than 5, 10, or even 50 locations). Furthermore, the spatial range over which the catheter is moved may be greater than one-third (⅓) of the diameter of the heart chamber (e.g., greater than 35%, 40%, 50%, or even 60% of the diameter of the heart chamber). Additionally, in some embodiments, the reconstructed physiological information is calculated based on signals measured over several heartbeats at a single catheter location within the heart chamber or across several locations. In situations where the reconstructed physiological information is based on multiple measurements across several heartbeats, the measurements may be synchronized with each other so that they are performed at approximately the same phase of the cardiac cycle. The signal measurements across several heartbeats may be synchronized based on features detected from physiological data such as an electrocardiogram (ECG) and / or an intracardiac electrogram (EGM).

[0042] Electrophysiology system 100 further includes processing unit 120, which performs some of the operations related to mapping procedures, including reconstruction procedures for determining physiological information at the endocardial surface and / or within the heart chambers (e.g., as described above). Processing unit 120 may also perform catheter registration procedures. Processing unit 120 may also generate a 3D grid, which is used to aggregate information captured by catheter 110 and enable display of portions of that information.

[0043] The location 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 multiple electrodes relative to a catheter coordinate system established by the system. These 3D spatial locations may be used to construct a 3D grid. Embodiments of the system 100 may use a hybrid localization technique that combines impedance and magnetic localization techniques. This combination may enable the system 100 to accurately track a catheter connected to the system 100. The magnetic localization technique uses a magnetic field generated by a localization generator positioned below the patient table to track the catheter via a magnetic sensor. The impedance localization technique may also be used to track catheters that may not be equipped with magnetic localization sensors, and may be used in conjunction with a surface ECG patch.

[0044] In some embodiments, to perform the mapping procedure and reconstruct physiological information related to the endocardial surface, processing unit 120 may align the coordinate system of catheter 110 with the coordinate system of the endocardial surface. Processing unit 120 (or some other processing component of system 100) may determine a coordinate system transformation function that converts 3D spatial coordinates of the catheter location to 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 a 3D grid may be used to capture contact and non-contact EGMs and select mapping values ​​based on statistical variances associated with the nodes of the 3D grid. Processing unit 120 may also perform post-processing operations on the physiological information to extract useful features of the information and display them to an operator of system 100 and / or other interested parties (e.g., a physician).

[0045] According to an embodiment, signals acquired by the multiple electrodes of catheter 110 are passed to processing unit 120 via electrical module 140, which may include, for example, signal conditioning components. Electrical module 140 receives signals communicated from catheter 110 and performs signal enhancement operations on the signals before forwarding them to processing unit 120. Electrical module 140 may include signal conditioning hardware, software, and / or firmware that may be used to amplify, filter, and / or sample intracardiac potentials measured by one or more electrodes. Intracardiac signals typically have a maximum amplitude of 60 mV and average a few millivolts.

[0046] In some embodiments, the signal is filtered by a bandpass filter having a certain frequency range (e.g., 0.5-500 Hz) and sampled by an analog-to-digital converter (e.g., with 15-bit resolution at 1 kHz). To avoid interference with electrical devices in the room, the signal may be filtered to remove frequencies corresponding to the power supply (e.g., 60 Hz). Other types of signal processing operations, such as spectral equalization and automatic gain control, may also be performed. In some embodiments, the intracardiac signal may be a unipolar signal measured against a reference (which may be a virtual reference). In such embodiments, the reference may be, for example, a coronary sinus catheter or a Wilson's central electrode (WCT), from which the signal processing operation may calculate a difference to generate a multipolar signal (e.g., a bipolar signal, a tripolar signal, etc.). In some other embodiments, the signal may be processed (e.g., filtered and sampled) before and / or after generating the multipolar signal. The resulting processed signal is forwarded by the electrical module 140 to the processing unit 120 for further processing.

[0047] 1, electrophysiology system 100 may also include peripheral devices such as a printer 150 and / or a display device 170, any of which may be interconnected to processing unit 120. Additionally, electrophysiology system 100 may include a storage device 160, which may be used to store data acquired by the various interconnected modules, including volumetric images, raw data measured by the electrodes and / or resulting endocardial representations calculated therefrom, partially calculated transformations used to expedite the mapping procedure, reconstructed physiological information corresponding to the endocardial surface, and / or the like.

[0048] In some embodiments, processing unit 120 may be configured to automatically improve the accuracy of its algorithms 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 classifiers may be trained and / or adapted using feedback information from a user, other metrics, and / or the like.

[0049] The exemplary electrophysiology system 100 illustrated in FIG. 1 is not intended to suggest any limitations on the scope of use or functionality of embodiments of the present disclosure. Nor should the exemplary electrophysiology system 100 be interpreted as having any dependencies or requirements relating to any one component or combination of components illustrated herein. Additionally, various components illustrated in FIG. 1 may, in some embodiments, be integrated with various other components illustrated herein (and / or components not illustrated), all of which 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 electrophysiology system 100 may be implemented within 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 methods described herein for processing the cardiac information (e.g., determining annotation waveforms, etc.). That is, for example, a computer analysis system may include the processing unit 120 but not a mapping catheter.

[0050] 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 similar to, include, or 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), distributed across multiple computing devices, instantiated within multiple virtual machines, and / or in other similar manners. One or more components for facilitating cardiac mapping may be stored in the memory 204. In some embodiments, the processor 202 may be configured to instantiate one or more components to generate an activation waveform, a set of metric analyses, a set of waveform analyses, electrogram features, histograms, and a cardiac map, any one or more of which may be stored in a data repository 206.

[0051] As shown in FIG. 2 , processing unit 200 may include an acceptor 212 configured to receive electrical signals from a mapping catheter (e.g., mapping catheter 110 shown in FIG. 1 ). The measured electrical signals may include several intracardiac electrograms (EGMs) sensed within the patient's heart. Acceptor 212 may also receive an indication of the measurement location corresponding to each of the electrical signals. In some embodiments, acceptor 212 may be configured to determine whether to accept the received electrical signals. Acceptor 212 may utilize any number of different components and / or techniques, such as filtering, beat-to-beat matching, morphological analysis, location information (e.g., catheter movement), respiratory gating, and / or the like, to determine which electrical signals or heartbeats to accept. The received electrical signals and / or processed electrical signals may be stored in data repository 206.

[0052] The received electrical signals are received by the excitation waveform generator 214, which is configured to extract at least one annotation feature from each of the electrical signals, where the electrical signals include the annotation feature to be extracted. In some embodiments, the at least one annotation feature includes at least one value corresponding to at least one excitation indicator. The at least one feature may include at least one event, where the at least one event includes at least one value corresponding to at least one indicator and / or at least one corresponding time (the corresponding time does not necessarily exist for each excitation feature). In some embodiments, the at least one indicator may include, for example, an excitation time, a minimum voltage value, a maximum voltage value, a time derivative of the maximum negative voltage, an instantaneous potential, a voltage amplitude, a dominant frequency, a peak-to-peak voltage, an excitation duration, and / or the like. In some embodiments, the excitation waveform generator 214 may be configured to detect excitation and generate an excitation waveform. In some cases, the waveform generator 214 may use any one of the excitation waveform embodiments, including, for example, those described in U.S. Patent Application Publication No. 2018 / 0296113, entitled "ANNOTATION WAVEFORM," the disclosure of which is expressly incorporated herein by reference.

[0053] 2 , processing unit 200 includes an index analyzer 216 for analyzing the received cardiac electrical signal and / or the excitation waveform generated by excitation waveform generator 214 to determine an index associated with the cardiac electrical signal. In an embodiment, index analyzer 216 is configured to determine significance indexes and other indexes of the cardiac electrical signal. In some embodiments, index analyzer 216 is configured to determine whether the cardiac electrical signal contributes meaningfully to the heartbeat by evaluating the excitation waveform corresponding to the signal. Additionally, processing unit 200 includes a representation engine 220 configured to enable presentation of representations of the cardiac electrical signal and / or index analysis results, e.g., selected cardiac electrical signals having significance indexes that are equal to or greater than a predetermined threshold.

[0054] The exemplary processing unit 200 illustrated in FIG. 2 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. Nor should the exemplary processing unit 200 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated herein. Additionally, any one or more of the components illustrated in FIG. 2 may, in some embodiments, be integrated with a variety of other components shown therein (and / or components not shown), all of which are considered within the scope of the subject matter disclosed herein. For example, the waveform generator 214 may be integrated with the indicator analyzer 216. In some embodiments, the processing unit 200 may not include the acceptor 212, while in other embodiments, the acceptor 212 may be configured to receive electrical signals from a memory device, a communication component, and / or the like.

[0055] Additionally, processing unit 200 (alone and / or in combination with other components of system 100 shown in FIG. 1 and / or other components not shown) may be used in conjunction with any of the techniques described in, for example, 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," and U.S. Patent Application Publication No. 2015 / 0065836 entitled "SYSTEMS AND METHODS FOR GUIDING MOVABLE ELECTRODE The present invention may perform any number of different functions and / or methods associated with cardiac mapping (e.g., triggering, blanking, field mapping, etc.), such as those described in U.S. Patent No. 6,070,094, entitled "ELEMENTS WITHIN A MULTIPLE-ELECTRODE STRUCTURE," U.S. Patent No. 6,233,491, entitled "CARDIAC MAPPING AND ABLATION SYSTEMS," and U.S. Patent No. 6,735,465, entitled "SYSTEMS AND PROCESSES FOR REFINING A REGISTERED MAP OF A BODY CAVITY," the disclosures of which are expressly incorporated herein by reference.

[0056] According to embodiments, various components of the electrophysiological system shown in FIG. 1 and / or the processing unit 200 shown in FIG. 2 may be implemented on one or more computing devices. The computing devices may include any type of computing device suitable for implementing embodiments of the present disclosure. Examples of computing devices include special-purpose or general-purpose computing devices, such as "work stations," "servers," "laptops," "desktops," "tablet computers," "handheld devices," "general-purpose graphics processing units (GPGPUs)," and / or the like, all of which are contemplated within the scope of FIGS. 1 and 2 with reference to various components of the system 100 and / or the processing unit 200.

[0057] In some embodiments, a computing device includes a bus that directly and / or indirectly couples multiple devices, such as a processor, memory, input / output (I / O) ports, I / O components, and a power supply. Any number of additional components, different components, and / or combinations of components may also be included within a computing device. A bus may represent one or more buses (e.g., an address bus, a data bus, or a combination thereof). Similarly, in some embodiments, a computing device may include several processors, several memory components, several I / O ports, several I / O components, and / or several power supplies. Additionally, any number of these components, or combinations thereof, may be distributed and / or replicated across several computing devices.

[0058] In some embodiments, 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, transient and / or non-transitory storage media, which may be removable, non-removable, or a combination thereof. Examples of media include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, optical or holographic media, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, data transmission, and / or any other medium usable to store information and accessible by a computing device, such as, for example, quantum state memory and / or the like. In some embodiments, memory 160 and / or 204 stores computer-executable instructions that cause a processor (e.g., processing unit 120 shown in FIG. 1 and / or processor 202 shown in FIG. 2) to implement aspects of embodiments of the system components described herein and / or to execute aspects of embodiments of the methods and procedures described herein.

[0059] Computer-executable instructions may include, for example, computer code, machine-usable instructions, and the like, as well as program components that can be executed by, for example, one or more processors associated with a computing device. Examples of such program components include the acceptor 212, the waveform generator 214, the indicator analyzer 216, and the expression engine 220. The program components may be programmed using any number of different programming environments, including various languages, development kits, frameworks, and / or the like. Alternatively, some or all of the functionality described herein may be implemented in hardware and / or firmware.

[0060] The data repository 206 may 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 management systems (DBMS) running on one or more database servers or data centers. The database management systems may be relational (RDBMS), hierarchical (HDBMS), multidimensional (MDBMS), object-oriented (ODBMS or OODBMS), or object-relational (ORDBMS) database management systems, and / or the like. The data repository may be, for example, a single relational database. In some cases, the data repository may include multiple databases, which may exchange and aggregate data through a data integration process or software application. In an exemplary embodiment, at least a portion of the data repository 206 may be hosted within 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 tiers of data storage devices, including local, regional, and central.

[0061] FIG. 3A is an exemplary flow diagram illustrating an exemplary method 300A for processing cardiac electrical signals to evaluate a significance index, according to some embodiments of the present disclosure. Aspects of the embodiment of method 300A may be performed, for example, by an electrophysiological 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 300A 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 300A. Initially, the electrophysiological system receives (310A) multiple cardiac electrical signals collected from multiple electrodes disposed within a cardiac chamber, where the multiple cardiac electrical signals are acquired over a cardiac beat having a cycle length. FIG. 4A illustrates an exemplary example of multiple cardiac electrical signals (e.g., EGM) over one cardiac beat. FIG. 4F illustrates an exemplary example of multiple cardiac electrical signals 440 over several cardiac beats.

[0062] The system calculates (320A) a significance index for each of the plurality of cardiac electrical signals. In embodiments, the significance index represents the contribution of each cardiac electrical signal to the overall duty cycle of the heartbeat according to cycle length. In some cases, the contribution to the overall duty cycle includes a contribution to the primary excitation period of the heartbeat, also referred to as an excitation index. In some cases, the primary excitation period of the heartbeat is determined based on one or more cardiac electrical signals that are primary contributors to the excitation of the heartbeat (signals that contribute more than 60% of the duty cycle of the heartbeat). In embodiments, the contribution to the overall duty cycle includes contributions outside the primary excitation period of the heartbeat, also referred to as a novelty index. The novelty index can represent the uniqueness of signals collected by one or more electrodes.

[0063] The electrophysiology system may select the cardiac electrical signals based on the importance index (330A). In some embodiments, the system may select the cardiac electrical signals based on one or more criteria, where at least one of the criteria uses the importance index as a parameter. In one embodiment, at least one of the criteria is an importance index equal to or greater than a predetermined threshold. In one embodiment, at least one of the criteria aggregates the importance index with at least one other feature of the cardiac electrical signals. The system may generate a representation of the selected cardiac electrical signals (340A). In one embodiment, the representation is a graphical representation. FIG. 4F shows an illustrative example of a graphical representation of the selected cardiac electrical signals 445.

[0064] 3B is an exemplary flow diagram illustrating an exemplary method 330B for processing cardiac electrical signals to evaluate a significance index, according to some embodiments of the present disclosure. Aspects of the embodiment of method 330B may be performed, for example, by an electrophysiological 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 300B 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 300B. Initially, an electrophysiological system receives multiple cardiac electrical signals collected from multiple electrodes over a cardiac beat (310B). The system may generate multiple excitation waveforms corresponding to the multiple cardiac electrical signals (315B).

[0065] In one embodiment, the electrophysiological system identifies a deflection for each of a plurality of electrical signals, where the deflection is a deviation from a signal baseline. Each of a plurality of excitation waveforms is generated based on the identified one or more deflections of the electrical signals. The excitation waveform includes a value corresponding to the likelihood that the identified deflection represents activation of cardiac tissue. The system may also calculate an excitation index for each of the cardiac electrical signals (320B). In some cases, the excitation index is calculated based on the corresponding excitation waveform. In one example, the excitation index can be calculated based on the excitation waveform value. In some embodiments, the excitation index represents the contribution of the cardiac electrical signal within an excitation zone, which will be described in more detail below. FIG. 4A shows an illustrative example of a plurality of cardiac electrical signals 400 (e.g., signals 401, 402, 403, 404, and 405) and each of a plurality of corresponding excitation waveforms 410 (e.g., waveforms 411, 412, 413, 414, and 415) for each cardiac electrical signal. In one implementation, an arithmetic mean of the excitation values ​​of the excitation waveforms is calculated. In the example shown in FIG. 4A, the arithmetic mean values ​​of excitation waveforms 411, 412, 413, 414, and 415 are 0, 0.05, 0.20, 0.25, and 0.08, respectively.

[0066] In one embodiment, the system further normalizes the arithmetic mean values, where the normalized values ​​are referred to as normalized mean values ​​of the excitation waveforms of the multiple cardiac electrical signals. In one example, the largest arithmetic mean value is set to 1 by a multiplier, and the other arithmetic mean values ​​are normalized using the same multiplier. FIG. 4B shows an illustrative example of an excitation waveform 410 and the normalized mean values. In this example, the normalized mean values ​​of excitation waveforms 411, 412, 413, 414, and 415 are 0, 0.20, 0.80, 1.00, and 0.32, respectively. In some embodiments, the activation index of a cardiac electrical signal is set to the normalized mean value of the corresponding excitation waveform.

[0067] Referring back to FIG. 3B , the electrophysiology system may determine an excitation zone (330B) based on multiple excitation waveforms corresponding to multiple cardiac electrical signals. In one embodiment, the system selects one or more excitation waveforms based on their normalized average values. For example, an excitation waveform is selected if its normalized average value is greater than a predetermined average value threshold. In some cases, the predetermined average value threshold is 0.6. The system then determines an excitation zone based on the selected excitation waveform. In one example, the excitation zone is represented by being determined using the following equation (1):

[0068]

number

[0069] 4C shows an illustrative example of an excitation zone. In the example shown in FIG. 4C, the system selects excitation waveforms 413 and 414 corresponding to cardiac electrical signals 403 and 404 that have a predetermined mean value threshold of 0.6, and uses the selected excitation waveforms 413 and 414 to determine an excitation zone 420 represented by waveform 425.

[0070] In some embodiments, the system calculates (335B) a novelty index for each of the plurality of cardiac electrical signals. In some embodiments, the novelty index is calculated based on each excitation waveform outside of the excitation zone. In some embodiments, the excitation zone is used as an inverted weighting factor (i.e., higher excitation values ​​correspond to lower weighting factors) to determine the novelty index. In one example, the novelty waveform is generated using the following equation (2):

[0071]

number

[0072] In some embodiments, the electrophysiology system calculates (340B) a significance index for each of the plurality of cardiac electrical signals based on the corresponding excitation index and novelty index. In one embodiment, the significance index is determined using Equation (3) below:

[0073]

number

[0074]

number

[0075] In some embodiments, the system selects a cardiac electrical signal based on the calculated importance index (350B). In one example, a cardiac electrical signal is selected if its calculated importance index is greater than a predetermined threshold. FIG. 4E shows illustrative examples of cardiac electrical signals 401, 402, 403, 404, and 405 and cardiac electrical signals 403, 404, and 405 selected based on their respective importance indexes. In some cases, a cardiac electrical signal is selected if a set of criteria is met. In some designs, the criteria use the importance index as a parameter. For example, the set of criteria includes a criterion of a calculated importance index greater than a predetermined threshold. FIG. 4F shows illustrative examples of multiple cardiac electrical signals 440 and a selected cardiac electrical signal 445, where signal selection and selection of corresponding electrodes (such as unipolar, bipolar, or tripolar electrodes) are based on criteria using the importance index as a parameter.

[0076] Various modifications and additions can be made to the described exemplary embodiments without departing from the scope of the present invention. For example, while the embodiments described above set forth certain features, 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 embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.

Claims

1. 1. A system for processing cardiac information, comprising: a processing unit, the processing unit comprising: receiving a plurality of cardiac electrical signals acquired from a plurality of electrodes disposed within a cardiac chamber, the plurality of cardiac electrical signals acquired over a cardiac beat having a cycle length; generating a plurality of excitation waveforms based on the plurality of cardiac electrical signals; calculating an importance index for each of the plurality of cardiac electrical signals, the importance index representing a contribution of each cardiac electrical signal to an overall duty cycle of the heartbeat according to the cycle length, the importance index being calculated based on an excitation index and a novelty index, the excitation index representing a first contribution of the corresponding cardiac electrical signal to an excitation zone, and the novelty index representing a second contribution of the corresponding cardiac electrical signal to outside the excitation zone; enabling presentation of a graphical representation of a selected plurality of cardiac electrical signals on a display device, the selected plurality of cardiac electrical signals each satisfying a selection criterion based on a corresponding importance index; The system is configured as follows:

2. The system of claim 1 , wherein each of the plurality of cardiac electrical signals comprises an intracardiac electrogram (EGM).

3. the processing unit is further configured to identify, for each of the plurality of cardiac electrical signals, a deviation comprising a deviation from a signal baseline; 2. The system of claim 1, wherein each of the plurality of excitation waveforms is generated based on a respective identified deflection, the excitation waveforms including an excitation waveform value corresponding to a likelihood that the identified deflection represents activation of cardiac tissue.

4. The system of claim 3 , wherein the excitation index is calculated based on an average value of the excitation waveform values.

5. The system of claim 4 , wherein the excitation index is calculated by normalizing the mean value of the excitation waveform values.

6. selecting one or more excitation waveforms based on the excitation index; determining an excitation zone waveform representative of the excitation zone based on the selected one or more excitation waveforms; The system of claim 1 further comprising:

7. The system of claim 6 , wherein each of the selected one or more excitation waveforms has an excitation index greater than a predetermined excitation index threshold.

8. 7. The system of claim 6, wherein the novelty index is calculated using a plurality of weighting factors based on the excitation zone waveform, a first weighting factor corresponding to a first excitation zone value, a second weighting factor corresponding to a second excitation zone value, and the first weighting factor being greater than the second weighting factor when the first excitation zone value is less than the second excitation zone value.

9. The system of claim 1 , wherein the importance index is determined by applying a non-linear function to the excitement index and the novelty index.

10. The system of any one of claims 1 to 9, wherein the selection criteria include the importance index being greater than a predetermined threshold.

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