Electrophysiological system and method for assessing small reentry sites

Abstract

At least some embodiments of the present disclosure are directed to 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, wherein the cardiac electrical signals are acquired during a cardiac beat; receive an indication of a measurement location corresponding to each of the cardiac electrical signals; analyze the one or more cardiac electrical signals to compute a plurality of duty cycle values, the computed duty cycle values corresponding to a plurality of preselected cycle length windows; and compute an average duty cycle value of the computed duty cycle values over the plurality of preselected cycle length windows.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to provisional application No. 63 / 085,683, filed on September 30, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to an electrophysiological system and method for processing cardiac electrical signals. Background Technology

[0004] The use of minimally invasive procedures, such as catheter ablation, to treat a variety of heart conditions, including supraventricular and ventricular arrhythmias, is becoming increasingly common. Such procedures involve (e.g., based on cardiac signals) mapping the electrical activity in the heart at various locations on the endocardial surface (“cardiac mapping”) to identify the site of origin of the arrhythmia, which is then targeted for ablation. To perform this cardiac mapping, a catheter with one or more electrodes can be inserted into the patient's heart chambers.

[0005] Conventional three-dimensional (3D) mapping techniques include contact mapping, non-contact mapping, and combinations of both. In both contact and non-contact mapping, one or more catheters are advanced into the heart. For some catheters, once inside a chamber, the catheter can be unfolded to present a 3D shape. In contact mapping, after establishing firm and stable contact between the distal end of the catheter and the endocardial surface of a specific cardiac chamber, physiological signals generated by the heart's electrical activity can be acquired using one or more electrodes located at the distal end of the catheter. In non-contact-based mapping systems, the system provides physiological information about the endocardium of the cardiac chambers using signals detected by non-contact electrodes and information about chamber anatomy and relative electrode positions. Typically, location and electrical activity are sequentially measured point-by-point at approximately 50 to 200 points on the inner surface of the heart to construct an electroanatomical profile of the heart. The resulting mapping map can then be used as a basis for determining therapeutic procedures (e.g., tissue ablation) to alter the propagation of cardiac electrical activity and restore normal heart rhythm.

[0006] In many conventional mapping systems, clinicians visually examine or review the captured electrograms (EGMs), which increases examination time and cost. However, during automated electroanatomical mapping, approximately 6,000 to 20,000 intracardiac electrograms (EGMs) may be captured, which is unsuitable for a comprehensive manual examination by clinicians (e.g., physicians) for diagnostic assessment and / or EGM classification. Typically, mapping systems extract scalar values ​​from each EGM to construct voltage, activation, or other mapping types, thereby depicting the overall pattern of intracardiac activity. While mapping reduces the need to examine the captured EGMs, they also condense often complex and useful information within the EGMs. Furthermore, mapping can be misleading due to electrical artifacts or inappropriate selection of features such as activation time. Additionally, due to the complex nature of conventional techniques, cardiac mapping is often unsuitable for accurate and effective interpretation.

[0007] Cardiac mapping can also be used to detect the location of atrial tachycardia. Atrial tachycardia (AT) is a cardiac arrhythmia. It occurs when the electrical signals controlling the heartbeat begin and repeat rapidly from an unusual location in the atria, causing the atria to beat too fast. Atrial tachycardia can be classified into three main categories: focal AT, macro-reentry AT, and micro-reentry AT. Focal AT can occur in structurally normal hearts, but it can also occur in patients with heart disease. Macro-reentry AT can occur in cases of atrial fibrosis. Micro-reentry AT can occur in diseased atrial myocardium supporting very slow conduction. Summary of the Invention

[0008] As illustrated 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 a cardiac chamber, wherein the cardiac electrical signals are acquired during cardiac contraction; 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 a corresponding one within a plurality of pre-selected cycle length windows; calculate the average duty cycle of the calculated duty cycle values ​​over the plurality of pre-selected cycle length windows; and facilitate the presentation on a display device of a three-dimensional electroanatomical mapping superimposed with annotations, the annotations representing the average duty cycle calculated based on the measurement location of the associated cardiac electrical signals.

[0009] Example 2 is the system described in Example 1, wherein the cardiac electrical signals include an intracardiac electrogram (EGM).

[0010] Example 3 is the system described in Example 2, wherein the processing unit is configured to calculate multiple duty cycle values ​​by determining the excitation duration associated with a heartbeat and dividing the excitation duration by each of a multiple preselected cycle length windows.

[0011] Example 4 is the system described in Example 2, wherein the analysis of one or more cardiac electrical signals includes generating an excitation waveform from one or more cardiac electrical signals, the excitation waveform being based on the deflection of one or more analyzed cardiac electrical signals from a signal baseline.

[0012] Example 5 is the system described in Example 4, wherein the processing unit is further configured to determine the excitation duration based on the excitation waveform.

[0013] Example 6 is the system described in Example 4 or 5, wherein the excitation waveform includes a value indicating the probability that the deflection represents excitation of cardiac tissue.

[0014] Example 7 is the system described in any one of Examples 4-6, wherein the processing unit is configured to calculate a plurality of duty cycle values ​​by calculating the average excitation waveform value over each of a plurality of preselected period length windows.

[0015] Example 8 is the system of any one of Examples 1-7, wherein the calculated average duty cycle represents the probability of a small reentry site defined by cardiac tissue corresponding to the measurement location.

[0016] Example 9 is the system of any one of Examples 1-8, wherein multiple pre-selected period length windows fall within the range of 140 milliseconds to 2000 milliseconds.

[0017] Example 10 is the system of any one of Examples 1-9, and further includes a display device operatively connected to the processing unit and configured to display an annotated three-dimensional anatomical mapping.

[0018] Example 11 is a system as described in any one of Examples 1-10, wherein the processing unit is configured to calculate an average duty cycle value for each cardiac electrical signal.

[0019] Example 12 is a system according to any one of Examples 1-10, wherein the processing unit is configured to aggregate multiple cardiac electrical signals having associated measurement locations within a specified region and calculate an average duty cycle value for the aggregated cardiac electrical signals.

[0020] Example 13 is a method for processing cardiac information. The method includes: receiving one or more cardiac electrical signals acquired during a heartbeat; analyzing the one or more cardiac electrical signals and calculating multiple duty cycle values, each calculated duty cycle value corresponding to a corresponding one in a plurality of pre-selected cycle length windows; calculating the average of the calculated duty cycle values ​​over the plurality of pre-selected cycle length windows; and displaying on a display device a three-dimensional anatomical mapping overlaid with annotations representing the calculated average duty cycle.

[0021] Example 14 is the method described in Example 13, wherein analyzing one or more cardiac electrical signals includes generating an excitation waveform from the one or more cardiac electrical signals, the excitation waveform being based on a deflection from a signal baseline of the one or more analyzed cardiac electrical signals.

[0022] Example 15 is the method described in Example 13 or 14, wherein calculating multiple duty cycle values ​​includes calculating multiple duty cycle values ​​based on the excitation waveform and each of multiple pre-selected period length windows.

[0023] 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, wherein the cardiac electrical signals are acquired during cardiac contraction; 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 a corresponding one within a plurality of pre-selected cycle length windows; calculate the average duty cycle of the calculated duty cycle values ​​over the plurality of pre-selected cycle length windows; and facilitate the presentation on a display device of a superimposed, annotated three-dimensional electroanatomical mapping, the annotation indicating the average duty cycle calculated based on the measurement location of the associated cardiac electrical signals.

[0024] Example 17 is the system described in Example 16, wherein the cardiac electrical signals include an intracardiac electrogram (EGM).

[0025] Example 18 is the system described in Example 17, wherein the processing unit is configured to calculate multiple duty cycle values ​​by determining the excitation duration associated with a heartbeat and dividing the excitation duration by each of a multiple preselected cycle length windows.

[0026] Example 19 is the system described in Example 17, wherein the analysis of one or more cardiac electrical signals includes generating an excitation waveform from one or more cardiac electrical signals, the excitation waveform being based on a deflection of the one or more analyzed cardiac electrical signals from a signal baseline.

[0027] Example 20 is the system described in Example 19, wherein the processing unit is further configured to determine the excitation duration based on the excitation waveform.

[0028] Example 21 is the system described in Example 19, wherein the excitation waveform includes a value indicating the probability that the deflection represents excitation of cardiac tissue.

[0029] Example 22 is the system described in Example 19, wherein the processing unit is configured to calculate a plurality of duty cycle values ​​by calculating the average excitation waveform value over each of a plurality of preselected period length windows.

[0030] Example 23 is the system described in Example 16, wherein the calculated average duty cycle represents the probability of a small reentry site defined by cardiac tissue corresponding to the measurement location.

[0031] Example 24 is the system described in Example 16, wherein multiple pre-selected period length windows fall within the range of 140 milliseconds to 2000 milliseconds.

[0032] Example 25 is the system described in Example 16, and also includes a display device operatively connected to the processing unit and configured to display an annotated three-dimensional anatomical mapping.

[0033] Example 26 is the system described in Example 16, wherein the processing unit is configured to calculate an average duty cycle value for each cardiac electrical signal.

[0034] Example 27 is the system described in Example 16, wherein the processing unit is configured to aggregate multiple cardiac electrical signals having associated measurement locations within a specified region and calculate an average duty cycle value for the aggregated cardiac electrical signals.

[0035] Example 28 is a method for processing cardiac information. The method includes: receiving one or more cardiac electrical signals acquired during a heartbeat; analyzing the one or more cardiac electrical signals and calculating multiple duty cycle values, each calculated duty cycle value corresponding to a corresponding one in a plurality of pre-selected cycle length windows; calculating the average of the calculated duty cycle values ​​over the plurality of pre-selected cycle length windows; and displaying on a display device a three-dimensional anatomical mapping overlaid with annotations representing the calculated average duty cycle.

[0036] Example 29 is the method described in Example 28, wherein analyzing one or more cardiac electrical signals includes generating an excitation waveform from the one or more cardiac electrical signals, the excitation waveform being based on a deflection from a signal baseline of the one or more analyzed cardiac electrical signals.

[0037] Example 30 is the method described in Example 28, wherein calculating multiple duty cycle values ​​includes calculating multiple duty cycle values ​​based on the excitation waveform and each of multiple pre-selected period length windows.

[0038] Example 31 is the method described in Example 28, wherein the cardiac electrical signals include an intracardiac electrogram (EGM).

[0039] Example 32 is the method described in Example 29, wherein the excitation waveform includes a value indicating the probability that the deflection represents excitation of cardiac tissue.

[0040] Example 33 is the method described in Example 29, wherein calculating multiple duty cycle values ​​includes calculating the average excitation waveform value over each of multiple preselected period length windows.

[0041] Example 34 is the method described in Example 28, wherein the calculated average duty cycle represents the probability of a small reentry site defined by cardiac tissue corresponding to the measurement location.

[0042] Example 35 is the method described in Example 28, wherein calculating multiple duty cycle values ​​includes aggregating multiple cardiac electrical signals with associated measurement locations within a specified region and calculating an average duty cycle value for the aggregated cardiac electrical signals.

[0043] While several embodiments have been disclosed, other embodiments of the invention will become apparent to those skilled in the art from the following detailed description illustrating exemplary embodiments. Therefore, the drawings and detailed description are to be considered illustrative rather than restrictive in nature. Attached Figure Description

[0044] Figure 1 A schematic diagram illustrating an exemplary embodiment of an electrophysiological system according to the subject matter disclosed herein is shown.

[0045] Figure 2 This is a block diagram of an illustrative processing unit according to an embodiment of the present disclosure.

[0046] Figure 3 This is a flowchart illustrating an automated anatomical mapping process according to embodiments of the present disclosure.

[0047] Figure 4A This is an example flowchart depicting an illustrative method for processing cardiac electrical signals and generating excitation waveforms according to some embodiments of the present disclosure.

[0048] Figure 4B This is an example flowchart depicting an illustrative method 400B for processing cardiac electrical signals and generating excitation waveforms according to some embodiments of the present disclosure.

[0049] Figure 5A An exemplary graphical representation illustrating the electrical signals received from the mapping catheter is depicted.

[0050] Figure 5B The waveforms of the original cardiac electrical signal and the corresponding excitation waveforms of the cardiac electrical signal are depicted.

[0051] Figure 6An illustrative example of an anatomical mapping diagram with annotations representing the average duty cycle value is depicted.

[0052] While the invention may have various modifications and alternatives, specific embodiments are shown by way of example in the accompanying drawings and are described in detail below. However, the invention is not intended to be limited to the specific embodiments described. Rather, the invention is intended to cover all modifications, equivalents, and alternatives that fall within the scope of the invention as defined by the appended claims. Detailed Implementation

[0053] Since the terminology used herein refers to measurements (e.g., size, characteristics, attributes, components, etc.) and their ranges of tangible things (e.g., products, inventory, 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.), "about" and "approximately" are used interchangeably to refer to a measurement result that includes the prescribed measurement result and also includes any measurement result that is quite close to the prescribed measurement result but may differ by a considerably small amount, such as that of a person of ordinary skill in the relevant field. The following are attributable to the following: measurement error; differences in measurement results and / or manufacturing equipment calibration; human error in reading and / or setting measurement results; adjustments made to optimize performance and / or structural parameters in light of other measurement results (e.g., measurement results related to other things); specific implementation schemes; imprecise adjustments and / or manipulations of things, settings and / or measurement results by people, computing devices and / or machines; system tolerances; control loops; machine learning; foreseeable variations (e.g., statistically insignificant variations, chaotic variations, system and / or model instability, etc.); and / or preferences, etc.

[0054] Although illustrative methods may be represented by one or more figures (e.g., flowcharts, communication flows, etc.), the figures should not be construed as implying any requirement for the various steps disclosed herein or a particular order among or between them. However, some embodiments may require certain steps and / or a certain order between certain steps, as can be explicitly described herein and / or understood from the nature of the steps themselves (e.g., the execution 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. “Multiple” means more than one.

[0055] As used herein, the term "based on" is not intended to be restrictive, but rather indicates that determination, identification, prediction, and / or calculation are performed by using at least the term following "based on" as input. For example, predicting an outcome based on a particular piece of information may alternatively or otherwise base the same determination on another piece of information.

[0056] Identifying small reentrant atrial tachycardia (AT) in cardiac mapping (e.g., activation mapping) is challenging. Even when small reentry is suspected, tracing its location can be difficult and time-consuming. Furthermore, slow small reentrant ATs may resemble focal ATs, but with a small area of ​​mixed time at the center. In many cases, the electroanatomical mapping of small reentrant ATs contains numerous disordered and dissociated areas, giving the mapping a similar appearance to atrial fibrillation (AF). Embodiments of this disclosure facilitate the assessment of small reentrant AT sites. In some embodiments, certain electrorecording properties (e.g., average duty cycle values ​​over multiple cycle-length windows) are generated and evaluated to determine the probability of a small reentry site. In some embodiments, an annotated anatomical mapping with electrorecording properties is presented to facilitate the assessment of small reentry sites. In some embodiments, activation waveforms and activation waveform values ​​are used in the assessment.

[0057] An excitation waveform (or annotation waveform) is a set of excitation waveform values ​​and may include, for example, a set of discrete excitation waveform values ​​(e.g., a set of excitation waveform values, a set of excitation time annotations, etc.) and / or a function that defines the excitation waveform curve, etc. In some embodiments, each data point of the excitation waveform represents the "probability" of each sample of tissue excitation. In some embodiments, the excitation waveform may be displayed, used to present excitation propagation mapping, used to facilitate diagnosis, and used to facilitate the classification of electrical signals, etc. 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 equipment commonly used in electrophysiology laboratories, such as recording systems, coronary sinus (CS) catheters or other reference catheters, ablation catheters, storage devices (e.g., local storage, cloud servers, etc.), communication components, and / or medical devices (e.g., implantable medical devices, external medical devices, telemetry devices, etc.), etc.

[0058] As used herein, a sensed cardiac electrical signal can refer to one or more sensed signals. Each cardiac electrical signal may include multiple intracardiac electrograms (EGMs) sensed within the patient's cardiac chambers and may include any number of features that can be determined by various 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 derivative of voltage, instantaneous potential, voltage amplitude, dominant frequency, and / or peak-to-peak voltage. Cardiac electrical signal features can refer to one or more features extracted from one or more cardiac electrical signals, and / or one or more features derived from one or more features extracted from one or more cardiac electrical signals. Furthermore, the representation of cardiac electrical signal features on cardiac and / or surface mapping can represent one or more cardiac electrical signal features and / or interpolation of multiple cardiac electrical signal features.

[0059] Each cardiac signal can also be associated with a set of corresponding location coordinates for the location where the cardiac electrical signal was sensed. Each of the corresponding location coordinates for the sensed cardiac signal can include three-dimensional Cartesian coordinates and / or polar coordinates, etc. In some cases, other coordinate systems may be used. In some embodiments, an arbitrary origin is used, and the corresponding location coordinates refer to a position in space relative to an arbitrary origin. In some embodiments, since cardiac signals can be sensed on the surface of the heart, the corresponding location coordinates can be near the endocardial surface, the epicardial surface, the middle of the myocardium of the patient's heart, and / or one of these.

[0060] Figure 1A schematic diagram of an exemplary embodiment of the electrophysiological system 100 is shown. As indicated above, embodiments of the subject matter disclosed herein can be implemented in mapping systems (e.g., cardiac mapping systems), while other embodiments can be implemented in ablation systems, recording systems, and / or computer analysis systems, etc. 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 multiple locations within the cardiac chamber into which the catheter 110 is inserted. In some embodiments, the distal end of the catheter 110 is fitted with a plurality of electrodes that are somewhat uniformly distributed on the catheter. For example, the electrodes may be mounted on the catheter 110 following a 3D olive shape and / or basket shape, etc. The electrodes are mounted on a device capable of unfolding the electrodes into a desired shape inside the heart and retracting the electrodes when the catheter is removed from the heart. To allow unfolding into a 3D shape within the heart, the electrodes may be mounted on a balloon, a shape memory material (such as nitinol), and / or an actuable hinge structure, etc. According to embodiments, catheter 110 may be a mapping catheter, an ablation catheter, a diagnostic catheter, and / or a CS catheter, etc. For example, aspects of embodiments of catheter 110, electrical signals obtained using catheter 110, and subsequent processing of electrical signals, as described herein, may also be applied to implementations of any other system having a recording system, an ablation system, and / or a catheter having electrodes that can be configured to obtain cardiac electrical signals.

[0061] At each of the locations to which catheter 110 is moved, multiple electrodes of the catheter acquire signals generated by electrical activity in the heart. Therefore, reconstructing and presenting physiological data related to cardiac electrical activity to users (e.g., physicians and / or technicians) can be based on information acquired at multiple locations, thus providing a more accurate and faithful reconstruction of the physiological behavior of the endocardial surface. The acquisition of signals at multiple catheter locations within the heart chambers enables the catheter to effectively function as a “mega-cathether,” with the number of effective electrodes and electrode span proportional to the product of the number of locations where signal acquisition is performed and the number of electrodes the catheter possesses.

[0062] To enhance the quality of the reconstructed physiological information at the endocardial surface, in some embodiments, catheter 110 is moved to more than three locations within the heart cavity (e.g., 5, 10, or even more than 50 locations). Furthermore, the spatial range of catheter movement can be greater than one-third (1 / 3) of the heart cavity diameter (e.g., greater than 35%, 40%, 50%, or even 60% of the heart cavity diameter). Additionally, in some embodiments, the reconstructed physiological information is calculated based on signals measured at a single catheter location within the heart cavity or on several heartbeats at several locations. In cases where the reconstructed physiological information is based on multiple measurements on several heartbeats, the measurements can be synchronized with each other, such that the measurements occur at approximately the same phase of the cardiac cycle. The synchronization of signal measurements on multiple beats can be based on features detected from physiological data such as surface electrocardiograms (ECG) and / or intracardiac electrograms (EGM).

[0063] The electrophysiological system 100 also includes a processing unit 120 that performs several operations related to assessment, including processing cardiac electrical signals collected from electrodes and / or catheters. The processing unit 120 may perform mapping procedures, including, for example, a reconstruction procedure for determining physiological information at the endocardial surface (e.g., as described above) and / or within the cardiac chambers. The processing unit 120 may also perform catheter registration procedures. The processing unit 120 may also generate a 3D grid for aggregating information captured by the catheter 110 and facilitating the partial display of that information.

[0064] 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 such as that established by the sensing and tracking system. These 3D spatial locations can be used to construct a 3D grid. Embodiments of system 100 can use a hybrid positioning technique that combines impedance positioning with magnetic positioning. This combination enables system 100 to accurately track catheters connected to system 100. Magnetic positioning uses a magnetic field generated by a positioning generator positioned under the bed to track catheters equipped with magnetic sensors. Impedance positioning can be used to track catheters that may not be equipped with magnetic positioning sensors, and it can be used in conjunction with surface ECG patches.

[0065] In some embodiments, in order to perform mapping procedures and reconstruct physiological information on 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 transformation function that converts the 3D spatial coordinates of the catheter location to coordinates expressed in 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 can be used to capture contact and non-contact EGMs and select mapping values ​​based on the statistical distribution 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 the operator of system 100 and / or other persons (e.g., physicians).

[0066] According to an embodiment, signals acquired from multiple electrodes of catheter 110 are transmitted to processing unit 120 via electrical module 140, which may include, for example, signal conditioning components. Electrical module 140 receives signals transmitted from catheter 110 and performs signal enhancement operations on them before forwarding them to processing unit 120. Electrical module 140 may include signal conditioning hardware, software, and / or firmware for amplifying, filtering, and / or sampling intracardiac potentials measured by one or more electrodes. Intracardiac signals typically have a maximum amplitude of 60 mV and an average of several millivolts.

[0067] In some embodiments, the signal is bandpass filtered within a frequency range (e.g., 0.5-500 Hz) and sampled using an analog-to-digital converter (e.g., at 15-bit resolution at 1 kHz). To avoid interference with electrical equipment 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 may also occur, such as spectral equalization, automatic gain control, etc. In some embodiments, the intracardiac signal may be a unipolar signal measured relative to a reference (which may be a virtual reference). In such embodiments, the reference may be, for example, a coronary sinus catheter or a Wilson central terminal (WCT), from which signal processing operations can calculate differences 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, sampled, etc.) before and / or after generating the multipolar signal. The resulting processed signal is forwarded by electrical module 140 to processing unit 120 for further processing.

[0068] like Figure 1As further shown, 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, which can be used to store data acquired by various interconnected modules, including volumetric images, raw data measured by electrodes and / or the resulting endocardial representation calculated therefrom, partially calculated transformations for accelerating mapping procedures, and / or physiological information corresponding to the reconstruction of the endocardial surface, etc.

[0069] In some embodiments, the processing unit 120 may be configured to automatically improve the accuracy of its algorithm by using one or more artificial intelligence (i.e., machine learning models, deep learning models), classifiers, etc. 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 (SVM), k-nearest neighbors, neural networks, convolutional neural networks, recurrent neural networks, etc. In some embodiments, feedback information from the user and / or other metrics may be used to train and / or tune the classifier.

[0070] Figure 1 The exemplary electrophysiological system 100 shown is not intended to suggest any limitation on the scope or functionality of the embodiments of this disclosure. The illustrative electrophysiological system 100 should also not be construed as having any dependency or requirement relating to any individual component or combination of components shown herein. Additionally, in some embodiments, Figure 1 The various components depicted herein can be integrated with various other components (and / or components not shown) depicted herein, all of which are considered to be within the scope of the subject matter disclosed herein. For example, electrical module 140 can be integrated with processing unit 120. Alternatively or additionally, aspects of embodiments of the electrophysiological system 100 can 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, mapping system memory, etc.) and perform aspects of embodiments of the methods described herein for processing cardiac information (e.g., determining annotated waveforms, etc.). That is, for example, the computer analysis system may include processing unit 120 but not mapping catheters.

[0071] Figure 2 This is a block diagram of an illustrative processing unit 200 according to an embodiment of the present disclosure. The processing unit 200 may be, similar to, or include... Figure 1 The processing unit 120 depicted herein may be included therein. Figure 2As shown, processing unit 200 can be implemented on a computing device including one or more processors 202 and one or more memories 204. Although processing unit 200 is referred to herein in the singular, processing unit 200 can be implemented in multiple instances (e.g., as a server cluster), distributed across multiple computing devices, and / or instantiated within multiple virtual machines, etc. One or more components of the electrophysiology system can be stored in memory 204. In some embodiments, processor 202 can be configured to instantiate one or more components to generate excitation waveforms, sets of signal analysis results, electrogrammatic characteristics, histograms, and cardiac mappings, any one or more of which can be stored in data repository 206.

[0072] like Figure 2 As depicted, the processing unit 200 may include a receiver 212 configured to receive signals from a mapping catheter (e.g., Figure 1 The receiver 212 receives electrical signals from the mapping catheter 110 depicted in the diagram. The measured electrical signals may include multiple intracardiac electrograms (EGMs) sensed within the patient's heart. The receiver 212 may also receive indications of the measurement location corresponding to each of the electrical signals. In some embodiments, the receiver 212 may be configured to determine whether to accept a received electrical signal. The receiver 212 may utilize any number of different components and / or techniques to determine which electrical signal or pulsation to accept, such as filtering, pulsation matching, morphological analysis, location information (e.g., catheter motion), and / or respiratory gating. The received and / or processed electrical signals may be stored in a data repository 206.

[0073] In an embodiment, the received electrical signal is received by an excitation waveform generator 214, which is configured to extract at least one annotation feature from each of the electrical signals if the electrical signal includes excitation features to be extracted. In some embodiments, the at least one excitation feature includes at least one value corresponding to at least one annotation metric. The at least one feature may include at least one event, wherein the at least one event includes at least one value corresponding to the at least one metric and / or at least one corresponding time (a corresponding time may not necessarily exist for each excitation feature). In some embodiments, the at least one metric may include, for example, excitation time, minimum voltage value, maximum voltage value, maximum negative time derivative of voltage, instantaneous potential, voltage amplitude, dominant frequency, inter-peak voltage, and / or excitation duration, etc. 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 of the excitation waveform embodiments, for example, those described in U.S. Patent Publication 2018 / 0296113 entitled “ANNOTATIONWAVEFORM,” the disclosure of which is expressly incorporated herein by reference.

[0074] like Figure 2 As shown, processing unit 200 includes a signal analyzer 216 to analyze received cardiac electrical signals and / or excitation waveforms generated by excitation waveform generator 214. In an embodiment, signal analyzer 216 is configured to determine certain characteristics of the received cardiac electrical signals (e.g., average duty cycle, excitation duration, etc.). In an embodiment, the signal analyzer may determine a small re-entry probability based on the determined characteristics of the cardiac electrical signals. Additionally, processing unit 200 includes a mapping engine 220 configured to facilitate the presentation of a mapping map corresponding to the cardiac surface based on the electrical signals. In some embodiments, the mapping map may include voltage mapping maps, excitation mapping maps, subdivision mapping maps, velocity mapping maps, and / or confidence mapping maps, etc. In some embodiments, the mapping map may include overlay annotations representing characteristics of the cardiac electrical signals at corresponding measurement locations.

[0075] Figure 2 The illustrative processing unit 200 shown is not intended to suggest any limitation on the scope or functionality of the embodiments of this disclosure. Nor should the illustrative processing unit 200 be construed as having any dependency or requirement relating to any individual component or combination of components shown herein. Additionally, in some embodiments, Figure 2Any one or more of the components depicted herein can be integrated with various other components (and / or components not shown) depicted herein, all of which are considered to be within the scope of the subject matter disclosed herein. For example, receiver 212 can be integrated with mapping engine 220. In some embodiments, processing unit 200 may not include receiver 212, while in other embodiments, receiver 212 may be configured to receive electrical signals from memory devices and / or communication components, etc.

[0076] Additionally, the processing unit 200 can (alone and / or with) Figure 1 Other components of the system 100 depicted herein and / or combinations of other components not shown perform any number of different functions and / or processes (e.g., triggering, blanking, field mapping, etc.) associated with electrophysiological mapping, such as those described in, for example, U.S. Patent Publication 2018 / 0296113 entitled “ANNOTATION WAVEFORM”; U.S. Patent 8,428,700 entitled “ELECTROANATOMICAL MAPPING”; U.S. Patent 8,948,837 entitled “ELECTROANATOMICAL MAPPING”; U.S. Patent 8,615,287 entitled “CATHETER TRACKING AND ENDOCARDIUM REPRESENTATION GENERATION”; U.S. Patent Publication 2015 / 0065836 entitled “ESTIMATING THEPREVALENCE OF ACTIVATION PATTERNS IN DATA SEGMENTS DURING ELECTROPHYSIOLOGY MAPPING”; and U.S. Patent Publication 2015 / 0065836 entitled “SYSTEMS AND METHODS FOR The disclosures of U.S. Patent 6,070,094 entitled “Guiding Movable Electrode Elements with In-Multiple Electrode Structure”; U.S. Patent 6,233,491 entitled “Cardiac Mapping and Ablation Systems”; and U.S. Patent 6,735,465 entitled “Systems and Processes for Refining a Registered Map of a Body Cavity” are hereby expressly incorporated herein by reference.

[0077] According to an embodiment, it can be implemented on one or more computing devices. Figure 1 The electrophysiological system 100 and / or shown in the figure Figure 2The various components of the processing unit 200 shown are illustrated. The computing device may include any type of computing device suitable for implementing embodiments of this disclosure. Examples of computing devices include dedicated or general-purpose computing devices, such as “workstations,” “servers,” “laptops,” “desktops,” “tablet computers,” “handheld devices,” and “general-purpose graphics processing units (GPGPUs),” all of which are... Figure 1 and Figure 2 The various components of the reference system 100 and / or processing unit 200 are conceived within the scope of the reference system 100 and / or processing unit 200.

[0078] In some embodiments, a computing device includes buses that are directly and / or indirectly coupled to processors, memory, input / output (I / O) ports, I / O components, and power supplies. The computing device may also include any number of additional components, different components, and / or combinations of components. A bus representation may be one or more buses (such as, for example, an address bus, a data bus, or a combination thereof). Similarly, in some embodiments, a 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.

[0079] In some embodiments, the memory (e.g., Figure 1 The storage device 160 depicted in the image Figure 2 The memory 204 and / or data repository 206 depicted include computer-readable media, temporary and / or non-temporary storage media in the form of volatile and / or non-volatile memory, 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 tape, magnetic tape, disk storage, or other magnetic storage devices; data transfer; and / or any other media that can be used to store information and is accessible by a computing device, such as, for example, quantum state memory. In some embodiments, memory 160 and / or 204 stores information for enabling a processor (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2 The processor 202 described herein implements computer-executable instructions for aspects of the system components discussed herein and / or for aspects of embodiments of the methods and procedures discussed herein.

[0080] Computer-executable instructions may include, for example, computer code and machine-usable instructions, such as program components that can be executed by one or more processors associated with a computing device. Examples of such program components include receiver 212, waveform generator 214, signal analyzer 216, and calibration engine 220. Program components can be programmed using any number of different programming environments, including various languages, development kits, and / or frameworks. Some or all of the functionality envisioned herein may also be implemented, or alternatively, in hardware and / or firmware.

[0081] Data repository 206 can be implemented using any of the configurations described below. The data repository may include random access storage, 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 system may be a relational database management system (RDBMS), a hierarchical database management system (HDBMS), a multidimensional database management system (MDBMS), an object-oriented database management system (ODBMS or OODBMS), or an object relational database management system (ORDBMS), 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 through data integration processes or software applications. In an exemplary embodiment, at least a portion of 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 other cases, the data repository may be hosted on a network of networked computers, servers, or devices. In some cases, data repositories can be hosted on data storage tiers that include local, regional, and central locations.

[0082] Figure 3 This is a flowchart illustrating an automated electroanatomical mapping process 300 according to embodiments of the present disclosure. Aspects of embodiments of the illustrative process 300 may be derived, for example, by a processing unit (e.g., Figure 1The processing unit 120 and / or depicted in the figure Figure 2 The processing unit 200 depicted in the image executes this. First, a data stream 302 containing multiple signals is input to the system (e.g., ...). Figure 1 In the cardiac mapping system 100 depicted in the figure, during the automated electroanatomical mapping process, data stream 302 provides the collection of physiological and non-physiological signals, which are used as inputs to the mapping process. Signals can be collected directly by the mapping system and / or obtained from another system using an analog or digital interface. Data stream 302 may include signals such as unipolar and / or bipolar intracardiac electrograms (EGM), surface electrocardiograms (ECG), electrode location information derived from one or more of various methods (magnetic, impedance, ultrasound, 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 properties, respiratory phase, blood pressure, and / or other physiological information, etc.

[0083] To generate a specific type of mapping, during the trigger / alignment process 304, one or more signals can be used as one or more references to trigger and align the data stream 302 relative to the heart, other biological cycles, and / or asynchronous system clocks, thereby generating a pulsation dataset. Additionally, for each incoming pulsation dataset, several pulsation metrics are calculated during the pulsation metric determination process 306. Pulsation metrics can be calculated using information from a single signal from multiple signals spanning the same pulsation and / or from signals spanning multiple pulsations. Pulsation metrics provide various types of information about the quality of a particular pulsation dataset and / or the likelihood that the pulsation data is suitable for inclusion in the mapping dataset. The pulsation acceptance process 308 aggregates criteria and determines which pulsation datasets will constitute the mapping dataset 310. The mapping dataset 310 can be stored in association with a 3D raster dynamically generated during data acquisition.

[0084] A surface geometry construction process 312 can be used to simultaneously generate surface geometry data 318 during the same data acquisition process using the same and / or different trigger and / or pulsatility acceptance metrics. This process uses data such as electrode location and catheter shape contained in the data stream to construct the surface geometry. Alternatively or concurrently, previously or concurrently collected surface geometry 316 can be used as input to surface geometry data 318. This geometry can be previously collected using different mapping datasets and / or different modalities such as CT, MRI, ultrasound, and / or rotational angiography, and registered to the catheter positioning system using the same procedure. The system executes a source selection process 314, which selects a source of surface geometry data and provides the surface geometry data 318 to a surface mapping generation process 320. The surface mapping generation process 320 generates surface mapping data 322 from the mapping dataset 310 and the surface geometry data 318.

[0085] A surface geometry construction algorithm generates an anatomical surface on which an electroanatomical mapping is displayed. The surface geometry can be constructed, for example, using aspects of systems described below: U.S. Patent 8,103,338 entitled "Impedance Based Anatomy Generation"; and / or U.S. Patent 8,948,837 entitled "Electroanatomical Mapping," the contents of each of which are incorporated herein by reference in their entirety. Alternatively or additionally, the anatomical shell can be constructed by the processing unit by adapting the surface to electrode locations determined by the user or automatically determined to be located on the surface of the chamber. Additionally, the surface can be fitted at the outermost electrode and / or catheter locations within the chamber.

[0086] As described, the mapping dataset 310 from its constructed surface can employ the same or different pulsation acceptance criteria as those used for electrical mapping and other types of mapping. The mapping dataset 310 for surface geometry construction can be collected simultaneously with or separately from electrical data. Surface geometry can be represented as a collection of grids containing vertices (points) and the connectivity (e.g., triangles) between them. Alternatively, surface geometry can be represented by different features, such as higher-order grids, non-uniform rational basis splines (NURBS), and / or curvilinear shapes.

[0087] The generation process 320 generates surface mapping data 322. Surface mapping data 322 can provide information about cardiac electrical excitation, cardiac motion, tissue proximity, tissue impedance, force information, and / or any other collected information required by the clinician. The combination of mapping dataset 310 and surface geometry data 318 allows for surface mapping generation. Surface mapping is a collection of values ​​or waveforms (e.g., EGM) on the surface of the chamber of interest, while the mapping dataset may contain data not on the cardiac surface. A method for processing mapping dataset 310 and surface geometry data 318 to obtain surface mapping dataset 322 is described in U.S. Patent Application No. 7,515,954, filed June 13, 2006, entitled “NON-CONTACT CARDIAC MAPPING, INCLUDING MOVING CATHETER AND MULTI-BEAT INTEGRATION,” the contents of which are incorporated herein by reference in their entirety.

[0088] Alternatively, or in combination with the methods described above, algorithms that apply acceptance criteria to individual electrodes can be employed. For example, electrode locations geometrically more than a set distance (e.g., 3 mm) from the surface can be rejected. Another algorithm can use impedance to incorporate tissue proximity information for inclusion in the surface mapping data. In this case, only electrode locations with a proximity value less than 3 mm may be included. Additional measures of the underlying data can also be used for this purpose. For example, EGM properties similar to pulsatility measures can be evaluated on a per-electrode basis. In this case, measures such as far-field overlap and / or EGM consistency can be used. It should be understood that variations may exist for methods of projecting points from the mapping dataset 310 onto the surface and / or selecting appropriate points.

[0089] Once obtained, the surface mapping data 322 can be further processed to annotate the expected characteristics from the base data; this process is defined as surface mapping annotation 324. Once data is collected into the surface mapping data 322, characteristics related to the collected data can be automatically presented to the user. These characteristics can be automatically determined by the computer system and applied to the data, and are referred to herein as annotations. Exemplary annotations include excitation time, the presence of dual excitation or subdivision, voltage amplitude, spectral content, and / or average duty cycle, etc. Because there is a large amount of data available in automated mapping (e.g., mapping performed by a computer system with minimal human input related to the incoming data), it is impractical for operators to manually review and annotate the data. However, user input can be a valuable supplement to the data, and therefore, when providing user input, the computer system must automatically propagate and apply it to more than one data point at a time.

[0090] Computer systems can be used to automatically annotate the average duty cycle and other characteristics of individual or aggregated EGMs. The calculation of the average duty cycle and other characteristics is described in detail in the disclosure herein. Once calculated, the annotations can be overlaid on the chamber geometry. In some embodiments, gap-filled surface mapping interpolation 326 can be employed. For example, in some embodiments, gap-filled interpolation can be employed when the distance between a point on the surface and the measured EGM exceeds a threshold, which may indicate that grid-based interpolation, as described herein, may be less effective in this case. The displayed mappings 328 can be calculated and displayed separately, and / or overlaid on each other.

[0091] Figure 3 The illustrative process 300 shown is not intended to suggest any limitation on the scope or functionality of the embodiments of this disclosure. Nor should the illustrative process 300 be construed as having any dependency or requirement relating to any individual component or combination of components shown therein. Additionally, for example, Figure 3 Any one or more of the components depicted herein may be integrated with various components (and / or components not shown) of other components depicted herein, all of which are considered to be within the scope of this disclosure.

[0092] Figure 4A This is an example flowchart depicting an illustrative method 400A for processing cardiac electrical signals and generating excitation waveforms according to some embodiments of the present disclosure. Aspects of embodiments of method 400A may be implemented, for example, by an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 depicted in the figure, and / or Figure 2 The processing unit 200 depicted herein executes the method. One or more steps of method 400A are optional and / or can be modified by one or more steps of other embodiments described herein. Furthermore, one or more steps of other embodiments described herein can be added to method 400A. First, the electrophysiological system receives one or more cardiac electrical signals (410A) collected from one or more electrodes positioned within the heart chambers, wherein the cardiac electrical signals are acquired during heartbeat. In some cases, the cardiac electrical signals include intracardiac electrograms (EGM). Figure 5A An exemplary graphical representation 500 illustrating electrical signals (in this case, EGM) received from a mapping catheter is depicted, each representing the size of a depolarization sequence of the heart during a predetermined time period. In this example, the EGM of a mapping catheter with 64 electrodes is shown. Each waveform may represent a unipolar signal received from the electrodes of the mapping catheter. In some cases, each waveform representing the cardiac electrical signal may present a multipolar (e.g., bipolar, tripolar) signal received from the electrodes.

[0093] The system can also receive indications (415A) corresponding to the measurement location of each cardiac electrical signal. In some embodiments, the system can analyze one or more cardiac electrical signals to determine the excitation duration associated with a heartbeat (420A). In embodiments, the excitation duration can represent the length of the excitation. That is, for example, a cardiac electrical signal (e.g., EGM) may include a portion for which all amplitudes deviate from a signal baseline according to a specified standard. The length of the time period corresponding to that portion of the cardiac electrical signal can be identified as the excitation duration.

[0094] In some cases, the system analyzes the cardiac electrical signal to calculate multiple duty cycle values, where each calculated duty cycle value corresponds to a specific one (430A) within a plurality of pre-selected cycle length windows. In other cases, each duty cycle value is calculated by dividing the excitation duration by the corresponding one within the plurality of pre-selected cycle length windows.

[0095] The system further calculates the average (435A) of the calculated duty cycle value over multiple pre-selected cycle length windows. In this embodiment, the calculated average duty cycle represents the probability of a small reentry site defined by cardiac tissue at the measurement location. In some cases, the multiple pre-selected cycle length windows fall within the range of 140 milliseconds to 2000 milliseconds.

[0096] In one embodiment, the system facilitates the presentation on a display device of a three-dimensional electroanatomical mapping (440A) overlaid with annotations, the annotations representing the average duty cycle calculated based on the measurement locations of the associated cardiac electrical signals. In some cases, the system determines multiple average duty cycles at multiple sites. In some cases, the system includes a display device (e.g., Figure 1 (170), the display device is operatively connected to a processing unit (e.g., Figure 1 120 Figure 2 (200) and is configured to display annotated 3D anatomical maps. Figure 6 Illustrative examples of anatomical mapping diagrams with annotations representing average duty cycle values ​​are depicted. In some cases, the average duty cycle value is represented by color. In other cases, the average duty cycle value is represented by grayscale values.

[0097] In some cases, the system is configured to calculate an average duty cycle value for each cardiac electrical signal. In some embodiments, the system is configured to aggregate multiple cardiac electrical signals having associated measurement locations within a specified region and calculate an average duty cycle value for the aggregated cardiac electrical signals. In some cases, the specified region has a predetermined geometry and size.

[0098] Figure 4BThis is an example flowchart depicting an illustrative method 400B for processing cardiac electrical signals and generating excitation waveforms according to some embodiments of the present disclosure. Aspects of embodiments of method 400B may be implemented, for example, by an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 depicted in the figure, and / or Figure 2 The processing unit 200 depicted herein performs the method. One or more steps of method 400B are optional and / or can be modified by one or more steps of other embodiments described herein. Furthermore, one or more steps of other embodiments described herein can be added to method 400B. First, the electrophysiological system receives one or more cardiac electrical signals (410B) collected by one or more electrodes positioned in the heart chambers during a heartbeat. 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.

[0099] In some embodiments, the electrophysiological system generates an excitation waveform (420B) based on cardiac electrical signals. The excitation waveform includes multiple excitation waveform values. In some cases, generating the excitation waveform includes identifying one or more deflections of the cardiac electrical signal from a signal baseline. In some embodiments, each excitation waveform value is associated with a probability that the identified deflection represents excitation. For example, in an embodiment, the system may include determining the probability (e.g., a value between 0 and 1, inclusive) that a given sampling point represents excitation based on its relationship to the signal baseline. In an embodiment, other numerical scales may be used to assign probabilities, such as values ​​between 0 and 100. In an embodiment, the likelihood (e.g., probability) that the signal deflection represents excitation may be determined based on the deviation of the deflection from the signal baseline. For example, a deflection with a maximum amplitude deviating from the signal baseline by at least a specified amount may be assigned a probability of 1, while a deflection with a maximum amplitude deviating from the signal baseline by at most a specified amount may be assigned a probability of 0.

[0100] Figure 5A An exemplary graphical representation 500 illustrating electrical signals (in this case, EGM) received from a mapping catheter is depicted, each representing the size of a depolarization sequence of the heart during a predetermined time period. In this example, the EGM of a mapping catheter with 64 electrodes is shown. Each waveform may represent a unipolar signal received from the electrodes of the mapping catheter. In some cases, each waveform representing the cardiac electrical signal may present a multipolar (e.g., bipolar, tripolar) signal received from the electrodes. Figure 5B The waveform of the original cardiac electrical signal 502 and the excitation waveform 504 corresponding to the cardiac electrical signal 502 are depicted.

[0101] Return to reference Figure 4BIn some embodiments, the electrophysiological system receives or selects multiple cycle length windows (425B). In some cases, the system selects the multiple cycle length windows as a predetermined range, for example, a range of 120 ms to 2000 ms. In some embodiments, the system receives input associated with the multiple cycle length windows, for example, through 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., via an application programming interface, web service, etc.). In such embodiments, the system selects the multiple cycle length windows based on the input. In one example, the system receives a timing reference input, such as 400 ms, and sets a range of 200 ms to 1600 ms. In one embodiment, the multiple cycle length windows increase linearly (e.g., every 10 ms). In another embodiment, the multiple cycle length windows increase non-linearly.

[0102] In some embodiments, the system may analyze one or more cardiac electrical signals to determine the duration of excitation associated with a heartbeat. In embodiments, the duration of excitation may represent the length of the excitation. In some cases, the system determines the duration of excitation based on the excitation waveform. In embodiments, the excitation waveform may be represented along a time scale, in which case the waveform may represent the duration of excitation. For example, the width of a deflection in the excitation waveform may represent the duration of the corresponding excitation.

[0103] In some embodiments, the system determines multiple duty cycle values ​​(430B) corresponding to multiple period length windows based on the excitation waveform. In some embodiments, the duty cycle value for a period length window can be determined as the average value of the excitation waveform values ​​within the period length window. Figure 5B In the example depicted, a period 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 excitation duration divided by the corresponding one of a plurality of period length windows.

[0104] The system further calculates the average of multiple 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 cardiac mapping (440B) superimposed with annotations representing the calculated average duty cycle values. In an embodiment, each annotation is located at the corresponding measurement location. In an embodiment, the calculated average duty cycle represents the probability that cardiac tissue at the measurement location defines a small reentry site. In some cases, the cardiac mapping is superimposed with annotations of small reentry probabilities, where the small reentry probability is represented by the calculated average of the duty cycle values. Figure 6An exemplary annotated electroanatomical mapping 600 according to an embodiment is depicted. In this example, the electroanatomical mapping 600 is superimposed with annotations of small return probabilities 610. An illustration of the small return probability is shown at 614. The 3D electroanatomical mapping can be a grayscale image or a color image. In some cases, the values ​​of the average duty cycle and / or small return probability are represented by color and / or grayscale values. Figure 6 In the example shown, region 612 has a high probability of small return AT.

[0105] Various modifications and additions can be made to the exemplary embodiments discussed herein without departing from the scope of the invention. For example, while the above embodiments refer to specific features, the scope of the invention also includes embodiments with different combinations of features and embodiments that do not include all the described features. Therefore, the scope of the invention is intended to include all substitutions, modifications, variations, and all equivalents thereof that fall within the scope of the claims.

Claims

1. A system for processing cardiac information, the system comprising: The processing unit is configured as follows: One or more cardiac electrical signals are received from one or more electrodes positioned within the heart chambers, wherein the cardiac electrical signals are acquired during a heartbeat; Receive an indication of the measurement location corresponding to each of the cardiac electrical signals; The one or more cardiac electrical signals are analyzed to calculate multiple duty cycle values, each calculated duty cycle value corresponding to a corresponding pre-selected cycle length window in multiple pre-selected cycle length windows; Calculate the average duty cycle of the calculated duty cycle value over the multiple pre-selected period length windows; and This facilitates the presentation of annotated 3D electroanatomical mappings on display devices, where the annotations represent the average duty cycle calculated based on the measurement locations of the associated cardiac electrical signals. The analysis of the one or more cardiac electrical signals includes generating an excitation waveform from the one or more cardiac electrical signals, the excitation waveform being based on a deflection of the one or more analyzed cardiac electrical signals from a signal baseline. The excitation waveform includes a value indicating the probability that the deflection represents excitation of cardiac tissue. The processing unit is configured to calculate the plurality of duty cycle values ​​by calculating the average excitation waveform value over each of the plurality of preselected period length windows, and The three-dimensional electroanatomical mapping is superimposed with annotations indicating the probability of small reentrant atrial tachycardia, wherein the probability of small reentrant atrial tachycardia is represented by the average duty cycle.

2. The system according to claim 1, wherein, The cardiac electrical signals include intracardiac electrograms (EGM).

3. The system according to claim 1, wherein, The processing unit is also configured to determine the duration of excitation based on the excitation waveform.

4. The system according to any one of claims 1-3, wherein, The multiple pre-selected period length windows fall within the range of 140 milliseconds to 2000 milliseconds.

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

6. The system according to any one of claims 1-3, wherein, The processing unit is configured to calculate the average duty cycle value for each cardiac electrical signal.

7. The system according to any one of claims 1-3, wherein, The processing unit is configured to aggregate multiple cardiac electrical signals with associated measurement locations within a specified area and calculate the average duty cycle value for the aggregated cardiac electrical signals.

8. A method for processing cardiac information, the method comprising: Receive one or more cardiac electrical signals acquired during a heartbeat; Analyze the one or more cardiac electrical signals and calculate multiple duty cycle values, each calculated duty cycle value corresponding to a preselected cycle length window in multiple preselected cycle length windows; Calculate the average of the calculated duty cycle values ​​over the multiple pre-selected period length windows; and A three-dimensional anatomical mapping map, overlaid with annotations representing the calculated average duty cycle, is displayed on a display device. The analysis of the one or more cardiac electrical signals includes generating an excitation waveform from the one or more cardiac electrical signals, the excitation waveform being based on a deflection of the one or more analyzed cardiac electrical signals from a signal baseline. The excitation waveform includes a value indicating the probability that the deflection represents excitation of cardiac tissue. The calculation of the plurality of duty cycle values ​​includes calculating the plurality of duty cycle values ​​based on the excitation waveform and each of the plurality of preselected period length windows, and The three-dimensional anatomical mapping is superimposed with annotations indicating the probability of small reentrant atrial tachycardia, wherein the probability of small reentrant atrial tachycardia is represented by the average duty cycle.

Images