Interactive 2D scatter plot of EGM feature measure

Abstract

At least some embodiments of this disclosure relate to a system for processing cardiac information. The system includes a processing unit configured to: receive an activation waveform comprising a set of activation waveform data of multiple signal segments collected at multiple locations; and receive a range of window sizes. For each of the multiple signal segments, the processing unit is further configured to: determine a set of confidence values ​​by traversing the multiple window sizes within the range of the window sizes. For each of the multiple signal segments, the processing unit is further configured to: determine one of a plurality of local cycle lengths based on the selected window size for each of the multiple signal segments; and generate a representation of the multiple local cycle lengths.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to provisional application number 63 / 085,659, filed on September 30, 2020, which is incorporated herein by reference in its entirety. Technical Field

[0003] This disclosure relates to electrophysiological systems and methods for processing cardiac electrical signals and cardiac mapping. Background Technology

[0004] The use of minimally invasive procedures, such as catheter ablation, to treat various heart conditions, including supraventricular and ventricular arrhythmias, is becoming increasingly common. Such procedures involve mapping the electrical activity in the heart (e.g., based on cardiac signals), such as at various locations on the endocardial surface (“cardiac mapping”), to identify the primary site of the arrhythmia, followed by targeted ablation of that site. To perform such 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 deployed to present a 3D shape. In contact mapping, after establishing stable and secure contact between the distal tip of the catheter and the endocardial surface of a specific cardiac chamber, physiological signals generated by cardiac electrical activity are acquired using one or more electrodes located at the distal tip 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. Position and electrical activity are typically measured sequentially on a point-by-point basis 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 the basis for determining therapeutic actions (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 inspect 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 not suitable for a comprehensive manual review 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 to depict the overall pattern of intracardiac activity. While mapping reduces the need to examine the captured EGMs, it also compresses the often complex and useful information within them. 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. Summary of the Invention

[0007] As described in the embodiments, Example 1 is a method for processing cardiac information. The method includes the following steps: receiving an activation waveform, which includes a set of activation waveform data of multiple signal segments collected at multiple locations; receiving a set of window parameters including a range of window sizes. For each of the multiple signal segments, the method includes the following steps: determining a set of confidence values ​​by interleaving multiple window sizes within the range of window sizes, each confidence value corresponding to a window size. Furthermore, for each of the multiple signal segments, the method includes the following steps: selecting a position of a central window for each of the multiple window sizes, the central window having each window size; calculating a set of correlations, each correlation in the correlation set being the correlation between the activation waveform in the central window and the activation waveform in a shifted window, the shifted window being a sample window shifted from the central window and having each window size; and determining a confidence value from the set of confidence values ​​based on the set of correlations. For each of the multiple signal segments, the method further includes the following steps: comparing the set of confidence values ​​to select a specified confidence value and a selected window size corresponding to the specified confidence value; and determining one of a plurality of local cycle lengths for each of the multiple signal segments based on the selected window size. The method also includes generating representations of multiple local period lengths.

[0008] Example 2 is the method of Example 1, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local cycle lengths superimposed on a cardiac mapping.

[0009] Example 3 is a method of Example 1 or 2, and further includes: receiving input of representation parameters of multiple local cycle lengths; and adjusting the representation of the multiple local cycle lengths based on the input.

[0010] Example 4 is a method of any of Examples 1-3, where multiple positions are selected based on the input.

[0011] Example 5 is the method of Example 2, wherein the input indicates the probe position in the heart chamber, and wherein multiple positions are within a predetermined radius from the probe position.

[0012] Example 6 is a method of any one of Examples 1-5, wherein the set of relevances includes a set of backward relevances and a set of forward relevances, wherein each backward relevance in the set of backward relevances is a relevance between the central window and the back-shifted window, wherein the back-shifted window is the central window that has been shifted backward, and each forward relevance in the set of forward relevances is a relevance between the central window and the forward-shifted window, wherein the forward-shifted window is the central window that has been shifted forward.

[0013] Example 7 is a method of any one of Examples 1-6, further comprising: for each of a plurality of signal segments, determining one of a plurality of local duty cycles based on the activation waveform of a selected central window having a selected window size, wherein the selected central window corresponds to a specified confidence value.

[0014] Example 8 is a method of Example 7, further comprising: generating representations of multiple duty cycles, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local duty cycles superimposed on a cardiac mapping.

[0015] Example 9 is the method of Example 8, and further includes: receiving input of parameters representing multiple local duty cycles; and adjusting the representations of the multiple local duty cycles based on the input.

[0016] Example 10 is a method of any one of Examples 1-9, and further includes: for each of a plurality of signal segments, determining one of the plurality of segment confidence values ​​based on a set of confidence values.

[0017] Example 11 is a method of Example 10, further comprising: generating a representation of multiple segment confidence values, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local duty cycles superimposed on a cardiac mapping.

[0018] Example 12 is a system for processing cardiac information. The system includes a processing unit configured to: receive an activation waveform comprising a set of activation waveform data of multiple signal segments collected at multiple locations; and receive a set of window parameters comprising a range of window sizes. For each of the multiple signal segments, the processing unit is further configured to: determine a set of confidence values ​​by traversing multiple window sizes within the range of window sizes, each confidence value corresponding to a window size. Furthermore, for each of the multiple signal segments, the processing unit is further configured to: select a position of a central window for each of the multiple window sizes, the central window having each window size; calculate a set of correlations, each correlation in the set being the correlation between the activation waveform in the central window and the activation waveform in a shifted window, the shifted window being a sample window shifted from the central window and having each window size; and determine a confidence value from the set of confidence values ​​based on the set of correlations. For each of the multiple signal segments, the processing unit is further configured to: compare the set of confidence values ​​to select a specified confidence value and a selected window size corresponding to the specified confidence value; and determine one of a plurality of local cycle lengths for each of the multiple signal segments based on the selected window size. The processing unit is further configured to generate representations of multiple local cycle lengths.

[0019] Example 13 is the system of Example 12, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local cycle lengths superimposed on a cardiac mapping.

[0020] Example 14 is a system of Example 12 or 13, wherein the processing unit is further configured to: receive input of parameters of a representation of multiple local period lengths; and adjust the representation of multiple local period lengths based on the input.

[0021] Example 15 is a system of Example 14, wherein the input indicates the probe position in the ventricle of the heart, and wherein multiple positions are within a predetermined radius from the probe position.

[0022] Example 16 is a method for processing cardiac information. The method includes the steps of: receiving an activation waveform comprising a set of activation waveform data of multiple signal segments collected at multiple locations; receiving a set of window parameters comprising a range of window sizes. For each of the multiple signal segments, the method includes the steps of: determining a set of confidence values ​​by traversing multiple window sizes within the range of window sizes, each confidence value corresponding to a window size. Furthermore, for each of the multiple signal segments, the method includes the steps of: selecting a position of a central window for each of the multiple window sizes, the central window having each window size; calculating a set of correlations, each correlation in the correlation set being the correlation between the activation waveform in the central window and the activation waveform in a shifted window, the shifted window being a sample window shifted from the central window and having each window size; and determining a confidence value from the set of confidence values ​​based on the set of correlations. For each of the multiple signal segments, the method further includes the steps of: comparing the set of confidence values ​​to select a specified confidence value and a selected window size corresponding to the specified confidence value; and determining one of a plurality of local cycle lengths for each of the multiple signal segments based on the selected window size. The method also includes generating a representation of the plurality of local cycle lengths.

[0023] Example 17 is a method of Example 16, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local cycle lengths superimposed on a cardiac mapping.

[0024] Example 18 is a method of Example 16, further comprising: receiving input of representation parameters of multiple local cycle lengths; and adjusting the representation of the multiple local cycle lengths based on the input.

[0025] Example 19 is the method of Example 16, where multiple positions are selected based on the input.

[0026] Example 20 is a method of Example 19, wherein the input indicates the probe position in the heart chamber, and wherein multiple positions are within a predetermined radius from the probe position.

[0027] Example 21 is a method of Example 16, further comprising: for each of a plurality of signal segments, determining one of a plurality of local duty cycles based on the activation waveform of a selected central window having a selected window size, wherein the selected central window corresponds to a specified confidence value.

[0028] Example 22 is a method of Example 21, further comprising: generating representations of multiple duty cycles, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local duty cycles superimposed on a cardiac mapping.

[0029] Example 23 is a method of Example 21, and further includes: receiving input of parameters representing multiple local duty cycles; and adjusting the representations of the multiple local duty cycles based on the input.

[0030] Example 24 is the method of Example 16, and further includes: for each of the multiple signal segments, determining one of the multiple segment confidence values ​​based on the set of confidence values.

[0031] Example 25 is the method of Example 24, and further includes: for each of the multiple signal segments, determining one of the multiple segment confidence values ​​based on a specified backward confidence value, a specified forward confidence value, a selected backward window size, and a selected forward window size.

[0032] Example 26 is a method of Example 24, wherein each confidence value in the set of confidence values ​​is based on the amplitude of the activation waveform in the central window of the selected window size and the set of correlations.

[0033] Example 27 is a method of Example 24, further comprising: generating a representation of multiple segment confidence values, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local duty cycles superimposed on a cardiac mapping.

[0034] Example 28 is a method of Example 27, further comprising: receiving input of parameters representing a plurality of segment confidence values; and adjusting the representation of the plurality of segment confidence values ​​based on the input.

[0035] Example 29 is a method of Example 16, further comprising: generating a representation of an annotated waveform data set superimposed on a cardiac mapping; receiving input associated with multiple local cycle lengths; updating the annotated waveforms based on the input; and updating the representation of the annotated waveform set superimposed on the cardiac mapping.

[0036] Example 30 is a system for processing cardiac information. The system includes a processing unit configured to: receive an activation waveform comprising a set of activation waveform data of multiple signal segments collected at multiple locations; and receive a set of window parameters comprising a range of window sizes. For each of the multiple signal segments, the processing unit is further configured to: determine a set of confidence values ​​by traversing multiple window sizes within the range of window sizes, each confidence value corresponding to a window size. Furthermore, for each of the multiple signal segments, the processing unit is further configured to: select a position of a central window for each of the multiple window sizes, the central window having each window size; calculate a set of correlations, each correlation in the set being the correlation between the activation waveform in the central window and the activation waveform in a shifted window, the shifted window being a sample window shifted from the central window and having each window size; and determine a confidence value from the set of confidence values ​​based on the set of correlations. For each of the multiple signal segments, the processing unit is further configured to: compare the set of confidence values ​​to select a specified confidence value and a selected window size corresponding to the specified confidence value; and determine one of a plurality of local cycle lengths for each of the multiple signal segments based on the selected window size. The processing unit is further configured to generate representations of multiple local cycle lengths.

[0037] Example 31 is a system of Example 30, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local cycle lengths superimposed on a cardiac mapping.

[0038] Example 32 is a system of Example 30, wherein the processing unit is further configured to: receive input of parameters of a representation of a plurality of local period lengths; and adjust the representation of the plurality of local period lengths based on the input.

[0039] Example 33 is a system of Example 32, wherein the input indicates the position of a probe in the ventricle of the heart, and wherein multiple positions are within a predetermined radius from the probe position.

[0040] Example 34 is a system of Example 30, further comprising: for each of a plurality of signal segments, determining one of a plurality of local duty cycles based on the activation waveform of a selected central window having a selected window size, wherein the selected central window corresponds to a specified confidence value.

[0041] Example 35 is a system of Example 34, and further includes: generating representations of multiple duty cycles, wherein the representation is at least one of a histogram, scatter plot, and graphical representation of multiple local duty cycles superimposed on a cardiac mapping.

[0042] 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, which shows and describes illustrative embodiments of the invention. Therefore, the drawings and detailed description should be considered illustrative in nature and not restrictive. Attached Figure Description

[0043] Figure 1 This is a conceptual schematic diagram depicting an illustrative electrophysiological system according to some embodiments of the present disclosure.

[0044] Figure 2 This is a block diagram depicting an illustrative processing unit for use with an electrophysiological system, based on embodiments of the subject matter disclosed herein.

[0045] Figure 3 This is a flowchart depicting an illustrative process for generating cardiac mapping according to embodiments of the subject matter disclosed herein.

[0046] Figures 4A-4D This is a flowchart depicting an illustrative method for processing electrophysiological information according to embodiments of the subject matter disclosed herein.

[0047] Figure 5A An exemplary graphical representation illustrating the electrical signals received from the mapping catheter is depicted.

[0048] Figure 5B The waveforms of the original cardiac electrical signal and the corresponding activation waveforms of the cardiac electrical signal are depicted.

[0049] Figure 5C Illustrative examples of central windows, back windows, and front windows are shown.

[0050] Figure 5D Example activation waveforms with a central window, a backward window, and a forward window are shown.

[0051] Figure 5E An illustrative example of the relevant value set for different window sizes is shown.

[0052] Figure 5F An illustrative example of a set of channel correlations for a window with a jitter range of -5ms to 5ms is shown.

[0053] Figure 5G This shows another illustrative example of the set of relevant values ​​for different window sizes.

[0054] Figure 5H Another illustrative example of the set of channel correlations for a window with a jitter range of -5ms to 5ms is shown.

[0055] Figure 5I An illustrative example of the maximum waveform is depicted.

[0056] Figure 6 This is a flowchart illustrating an illustrative method for processing electrophysiological information to generate histograms according to embodiments of the subject matter disclosed herein.

[0057] Figure 7A This is an illustrative example of a local period length histogram.

[0058] Figure 7B This is an illustrative example of a local duty cycle histogram.

[0059] Figure 7C This is an illustrative example of a confidence value histogram.

[0060] Figure 7D An illustrative example of a representation of a local cycle length histogram with cardiac mapping is shown.

[0061] Figure 8A This is a flowchart illustrating an illustrative method for processing electrophysiological information to generate a representation of electrogram characteristics according to some embodiments of the present disclosure.

[0062] Figure 8B This is a flowchart illustrating an illustrative method for improving cardiac mapping by using representations of electrogram characteristics according to some embodiments of the present disclosure.

[0063] Figure 9A An illustrative example of a cardiac mapping with electrogram characteristics is depicted.

[0064] Figure 9B An illustrative example of a graphical representation of a roving probe is depicted.

[0065] Figure 9C An illustrative example of a scatter plot is shown.

[0066] Figure 9D An illustrative example of a graphical representation with a scatter plot and one or more cardiac mapping plots is depicted.

[0067] Figure 9E An illustrative example depicting a set of characteristics of an electrograph is provided.

[0068] Figure 9F An illustrative example of a cardiac mapping overlaid with an activation waveform indicator is depicted.

[0069] Figure 9G Depicting based on Figure 9F An illustrative example of a reprocessed cardiac mapping of the cardiac mapping depicted in the image.

[0070] Although the invention is subject to various modifications and alternatives, specific embodiments are shown by way of example in the accompanying drawings and are described in detail below. However, it is not intended to limit the invention 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

[0071] In this document, the terms “about” and “approximately” are used interchangeably to refer to a measurement result (e.g., size, characteristic, attribute, component, etc.) and its range relative to tangible things (e.g., products, inventory, etc.) and / or intangible things (e.g., data, electronic representation of currency, accounts, information, parts of things (e.g., percentages, fractions), calculations, data models, dynamic system models, algorithms, parameters, etc.) and / or their ranges. This includes the specified measurement result as well as any measurement result that is quite close to the specified measurement result but may differ by a considerably small amount. Quantities such as those that a person skilled in the art would understand and readily identify as attributable to: measurement errors; differences in the calibration of measuring and / or manufacturing instruments; human error in reading and / or setting measurement results; adjustments made in consideration of other measurement results (e.g., measurement results related to other things) to optimize performance and / or structural parameters; specific implementation scenarios; 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; predictable variations (e.g., statistically negligible variations, chaotic variations, system and / or model instability, etc.); and / or preferences, etc.

[0072] While 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 may be explicitly described herein and / or as may be 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. “Plurality” means more than one.

[0073] 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.

[0074] During atrial fibrillation (AF), conventional activation mapping using a reference electrode on a coronary sinus (CS) catheter may be impossible due to the disordered and dissociative nature of activation on the CS. This limits the practicality of cardiac mapping systems during many AF cases, most notably persistent AF. Clear and consistent tissue with discrete cycle length patterns has been demonstrated in certain regions of the atrium during AF. Knowing how local cycle length and duty cycle data patterns spatially cluster is crucial for identifying AF drivers. In some cases, for a given cycle length, the duty cycle provides important information about the nature of the different patterns observed, e.g., the type of AF driver responsible for each observed cycle length, and how best to identify / exclude that driver. Embodiments of the systems and methods described herein facilitate the determination of characteristics (e.g., local cycle length, local duty cycle, and / or confidence values, etc.) of cardiac electrical signals recorded on a mapping catheter without a fixed or associated reference electrode or without reference to signals measured by a fixed or associated reference electrode, also known as waveform characteristics. The determination of the local cycle length according to this disclosure provides clinicians with a diagnostic estimate of the actual atrial fibrillation cycle length, which may be difficult to determine using conventional methods. In embodiments, the local cycle length can be determined without requiring a fixed or associated reference cycle and / or without referencing a signal measured by a fixed or associated reference electrode. The local duty cycle of the cardiac electrical signal can be determined based on the local cycle length. In embodiments, the local duty cycle can be determined without requiring a fixed or associated reference cycle length and / or without referencing a signal measured by a fixed or associated reference electrode.

[0075] Embodiments of this disclosure facilitate the discovery of meaningful deflections while suppressing noise and artifacts. An activation waveform, or annotated waveform, is a set of activation waveform values ​​and may include, for example, a set of discrete activation waveform values ​​(e.g., a set of activation waveform values, a set of activation time annotations, etc.) and / or a function defining the activation waveform curve, etc. In some embodiments, each data point of the activation waveform represents a "probability" of each sample of tissue activation. In some embodiments, waveform characteristics may be displayed for presentation in activation propagation mapping, for facilitating diagnosis, for facilitating the classification of electrical signals, etc. To perform aspects of embodiments of the methods described herein, cardiac electrical signals can be obtained from: mapping catheters (e.g., associated with a mapping system), which may be used in conjunction with other devices 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.), etc.

[0076] As used herein, a sensed cardiac electrical signal can refer to one or more sensed signals. Each cardiac electrical signal can include an intracardiac electrogram (EGM) sensed within the patient's heart and can 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 interpeak voltage, etc. A cardiac electrical signal feature 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, etc. In addition, 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, etc.

[0077] Each cardiac signal can also be associated with a set of corresponding location coordinates corresponding to the location where the sensed cardiac electrical signal is located. Each of the corresponding location coordinates of 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 that arbitrary origin. In embodiments, since cardiac signals can be sensed on the surface of the heart, the corresponding location coordinates can be on the endocardial surface, the epicardial surface, in the middle layer of myocardium of the patient's heart, and / or near one of these.

[0078] Figure 1A schematic diagram of an exemplary embodiment of the electrophysiological system 100 is shown. As noted above, embodiments of the subject matter disclosed herein can be implemented in electrophysiological systems (e.g., mapping systems, 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 can be 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 distributed somewhat uniformly on the catheter. For example, the electrodes can be mounted on the catheter 110 in a 3D olive, basket, and / or similar shape. The electrodes are mounted on a device that is capable of unfolding the electrodes into a desired shape while 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 can 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, a CS catheter, etc. For example, as described herein, aspects of embodiments of catheter 110, the electrical signals obtained using catheter 110, and the subsequent processing of the electrical signals may also be applied to implementations of any other system having a recording system, an ablation system, and / or a catheter with electrodes that can be configured to obtain cardiac electrical signals.

[0079] At each location where catheter 110 is moved, multiple electrodes of the catheter acquire signals generated by electrical activity in the heart. Thus, reconstructing physiological data related to the heart's electrical activity and presenting it to users (such as physicians and / or technicians) can be based on information acquired at multiple locations, providing a more accurate and faithful reconstruction of the physiological behavior of the endocardial surface. Signal acquisition at multiple catheter locations within the heart chambers allows the catheter to effectively function as a "mega-catheter," with the effective number of electrodes and electrode span proportional to the product of the number of signal acquisition locations and the total number of electrodes on the catheter.

[0080] To improve 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 chambers (e.g., 5, 10, or even more than 50 locations). Furthermore, the spatial range in which the catheter is moved can be greater than one-third (1 / 3) of the heart chamber diameter (e.g., greater than 35%, 40%, 50%, or even 60% of the heart chamber diameter). Additionally, in some embodiments, the reconstructed physiological information is calculated based on signals measured at a single catheter location within the heart chamber or on several heartbeats at multiple locations. In the case 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 are performed at approximately the same phase of the cardiac cycle. The signal measurements on multiple heartbeats can be synchronized based on features detected from physiological data such as surface electrocardiograms (ECG) and / or intracardiac electrograms (EGM).

[0081] The electrophysiological system 100 also includes a processing unit 120 that performs several operations related to mapping procedures, including 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 can also perform catheter registration procedures. The processing unit 120 can also generate a 3D grid used to aggregate information captured by the catheter 110 and facilitate the display of portions of that information.

[0082] The position of the catheter 110 inserted into the heart chamber can be determined using a conventional sensing and tracking system 180, which provides 3D spatial coordinates of the catheter and / or its multiple electrodes relative to a catheter coordinate system established by the sensing and tracking system. These 3D spatial positions 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 placed under the patient table 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 can be used in conjunction with surface ECG patches.

[0083] In some embodiments, 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 110 (or some other processing component of system 100) may determine a coordinate transformation function that transforms the 3D spatial coordinates of the catheter location into 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 both contact and non-contact EGMs, and mapping values ​​are selected 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 present them to the operator of system 100 and / or others (e.g., physicians).

[0084] According to an embodiment, signals acquired by 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 the signals before forwarding them to processing unit 120. Electrical module 140 may include signal conditioning hardware, software, and / or firmware that can 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 an average value of several millivolts.

[0085] In some embodiments, the signal is filtered by a bandpass filter having a frequency range (e.g., 0.5-500 Hz) and sampled using an analog-to-digital converter (e.g., with 15-bit resolution at 1 kHz). To avoid interference with indoor electrical equipment, 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, automatic gain control, etc., may also occur. 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 Wilson's 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., filtering, sampling, 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.

[0086] 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 that 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 reconstructed physiological information corresponding to the endocardial surface, etc.

[0087] 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 techniques (e.g., machine learning models, deep learning models) and / or classifiers. In some embodiments, for example, the processing unit may use one or more supervised and / or unsupervised techniques, such as support vector machines (SVM), k-nearest neighbors, neural networks, convolutional neural networks, and / or recurrent neural networks. In some embodiments, the classifier may be trained and / or tuned using feedback from the user, other metrics, etc.

[0088] Figure 1 The illustrative electrophysiological system 100 shown is not intended to suggest any limitation on the scope or functionality of the embodiments of this disclosure. Nor should the illustrative electrophysiological system 100 be construed as having any dependency or requirement associated with any individual component or combination of components described herein. Additionally, in some embodiments, Figure 1 The various components depicted herein may be integrated with various components (and / or components not shown) in other components depicted herein, all of which are considered to be within the scope of the subject matter disclosed herein. For example, electrical module 140 may be integrated with processing unit 120. Alternatively or additionally, aspects of embodiments of the electrophysiological system 100 may be implemented in a computer analysis system configured to receive cardiac electrical signals and / or other information from a memory device (e.g., a cloud server, 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.

[0089] 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, include, or be included in... Figure 1 In the processing unit 120 depicted in the image. For example... 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 for facilitating cardiac mapping can be stored in memory 204. In some embodiments, processor 202 can be configured to instantiate one or more components to generate activation waveforms, waveform analysis result sets, electrogrammatic features, histograms, and cardiac mapping maps, any one or more of which can be stored in data storage 206.

[0090] like Figure 2 As depicted, the processing unit 200 may include components configured to receive data from a mapping catheter (e.g., Figure 1 A receiver 212 for electrical signals from the catheter 110 depicted in the diagram. The measured electrical signals may include a number of intracardiac electrograms (EGMs) sensed within the patient's heart. The receiver 212 may also receive an indication of the measurement location corresponding to each electrical signal. In some embodiments, the receiver 212 may be configured to determine whether to accept an electrical signal that has been received. The receiver 212 may utilize any number of different components and / or techniques (such as filtering, pulse matching, morphological analysis, location information (e.g., catheter motion), and / or respiratory gating, etc.) to determine which electrical signals or pulses to accept. The received electrical signals and / or processed electrical signals may be stored in a data storage library 206.

[0091] The received electrical signals are received by an activation waveform generator 214, which is configured to extract at least one annotation feature from each electrical signal if the electrical signal includes annotation features to be extracted. In some embodiments, the at least one annotation 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 at least one metric and / or at least one corresponding time (a corresponding time may not necessarily exist for each annotation feature). In some embodiments, the at least one metric may include, for example, activation time, minimum voltage value, maximum voltage value, maximum negative time derivative of voltage, instantaneous potential, voltage amplitude, dominant frequency, peak-to-peak voltage, and / or activation duration, etc. In some embodiments, the activation waveform generator 214 may be configured to detect activation and generate an activation waveform. In some cases, the waveform generator 214 may use any of the activation waveform embodiments, for example, those described in U.S. Patent Publication 2018 / 0296113 entitled “ANNOTATION WAVEFORM,” the disclosure of which is hereby expressly incorporated herein by reference.

[0092] like Figure 2 As shown, the processing unit 200 includes a waveform analyzer 216 for analyzing the activation waveform generated by the activation waveform generator 214 and the received cardiac electrical signal. The waveform analyzer 216 is configured to determine one or more characteristics of the cardiac electrical signal, or electrogram characteristics, such as cycle length, local cycle length, duty cycle, local duty cycle, and associated confidence values.

[0093] like Figure 2 As shown, processing unit 200 includes a histogram generator 218 configured to generate an analysis histogram with multiple bins, within which analysis results (e.g., local cycle length, local duty cycle) from waveform analyzer 216 are included. Using histogram generator 218, processing unit 200 can be configured to aggregate a set of analysis results by including each analysis result in the histogram. For example, histogram generator 218 can be configured to aggregate a set of local cycle lengths, local duty cycles, and confidence levels in a histogram. Additionally, processing unit 200 includes a mapping engine 220 configured to facilitate the presentation of mappings corresponding to the surface of the heart based on electrical signals. In some embodiments, the mappings may include voltage mappings, activation mappings, subdivision mappings, velocity mappings, and / or confidence mappings, etc. In some embodiments, the mappings may include a superimposed representation of analysis results (e.g., local cycle length, local duty cycle, etc.) at corresponding locations within the heart chambers.

[0094] 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 associated with any individual component or combination of components described herein. Additionally, in some embodiments, Figure 2 Any one or more of the components depicted herein may be integrated with various components (and / or components not shown) in other components depicted herein, all of which are considered to be within the scope of the subject matter disclosed herein. For example, receiver 212 may be integrated with histogram generator 218 and / or mapping engine 220. In some embodiments, processing unit 200 may not include receiver 212; however, in other embodiments, receiver 212 may be configured to receive electrical signals from memory devices, communication components, etc.

[0095] Additionally, the processing unit 200 can (individually and / or with) Figure 1The system 100 depicted herein (and / or other components not shown in combination) performs any number of different functions and / or processes associated with cardiac mapping (e.g., triggering, blanking, field mapping, etc.), such as, for example, in U.S. Patent Publication 2018 / 0296113 entitled "ANNOTATION WAVEFORM"; U.S. Patent 8428700 entitled "ELECTROANATOMICAL MAPPING"; U.S. Patent 8948837 entitled "ELECTROANATOMICAL MAPPING"; U.S. Patent 8615287 entitled "CATHETER TRACKING AND ENDOCARDIUM REPRESENTATION GENERATION"; and U.S. Patent 8615287 entitled "ESTIMATING THE PREVALENCE OF ACTIVATION PATTERNS IN DATA SEGMENTSDURING ELECTROPHYSIOLOGY". The disclosures described in U.S. Patent Publication 2015 / 0065836 entitled "MAPPING (estimating the universality of activation patterns in data segments during electrophysiological mapping)"; U.S. Patent 6070094 entitled "SYSTEMS AND METHODS FOR GUIDING MOVABLE ELECTRODE ELEMENTS WITHIN MULTIPLE-ELECTRODESTRUCTURE"; U.S. Patent 6233491 entitled "CARDIAC MAPPING AND ABLATION SYSTEMS"; and U.S. Patent 6735465 entitled "SYSTEMS AND PROCESSES FOR REFINING A REGISTERED MAP OF A BODYCAVITY" are hereby expressly incorporated herein by reference.

[0096] According to an embodiment, Figure 1 The electrophysiological system 100 and / or shown Figure 2The various components of the processing unit 200 shown can be implemented on one or more computing devices. The computing device can include any type of computing device suitable for implementing embodiments of this disclosure. Examples of computing devices include dedicated computing devices or general-purpose computing devices such as “workstations,” “servers,” “laptops,” “desktops,” “tablets,” “handheld devices,” and “general-purpose graphics processing units (GPGPUs),” all referring to the various components of system 100 and / or processing unit 200. Figure 1 and Figure 2 Consider within the range.

[0097] In some embodiments, a computing device includes a bus that directly and / or indirectly couples to the following devices: 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 in the computing device. A bus represents something that 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. Furthermore, any number of these components or combinations thereof may be distributed and / or replicated across multiple computing devices.

[0098] In some embodiments, the memory (e.g., Figure 1 Storage device 160 depicted in the text Figure 2 The memory 204 and / or data storage 206 depicted include computer-readable media in the form of volatile and / or non-volatile memory, temporary and / or non-temporary storage media, and may be removable, non-removable, or a combination thereof. Examples of media include random access memory (RAM); read-only memory (ROM); electronically erasable programmable read-only memory (EEPROM); flash memory; optical or holographic media; magnetic tape cassettes, 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 204 and / or storage device 160 stores computer-executable instructions for causing a processor (e.g., ...) Figure 1 The processing unit 120 and / or depicted in the figure Figure 2The processor 202 described herein implements aspects of embodiments of the system components discussed herein and / or performs aspects of embodiments of the methods and processes discussed herein.

[0099] Computer-executable instructions may include, for example, computer code, machine-usable instructions, and program components such as those executable by one or more processors associated with a computing device. Examples of such program components include: receiver 212, waveform generator 214, waveform analyzer 216, histogram generator 218, 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 functions envisioned herein may also or alternatively be implemented in hardware and / or firmware.

[0100] Data store 206 can be implemented using any of the configurations described below. The data store 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 system may be a relational (RDBMS), hierarchical (HDBMS), multidimensional (MDBMS), object-oriented (ODBMS or OODBMS), or object-relational (ORDBMS) database management system, etc. The data store may be, for example, a single relational database. In some cases, the data store 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 the data store 206 may be hosted in a cloud data center. In some cases, the data store may be hosted on a single computer, server, storage device, or cloud server, etc. In other cases, the data store may be hosted on a network of networked computers, servers, or devices. In some cases, the data store may be hosted on a tier of data storage devices, including local, regional, and central data storage devices.

[0101] Figure 3 This is a flowchart illustrating an illustrative process / method 300 for automated electroanatomical mapping according to embodiments of the present disclosure. Aspects of embodiments of method 300 may be implemented, for example, by a processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2 The processing unit 200 depicted in the diagram executes this. First, a data stream 302 containing multiple signals is input into the system (e.g., ...). Figure 1The cardiac electrophysiological system 100 is depicted in the figure. During the automated electroanatomical mapping process, data stream 302 provides a collection of physiological and non-physiological signals to be used as inputs to the mapping process. The 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 position 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.

[0102] To generate a specific type of mapping dataset, during the trigger / alignment process 304, one or more signals may be used as one or more references to trigger and align data streams 302 relative to cardiac, other biological cycles, and / or asynchronous system clocks, resulting in a pulsation dataset. Additionally, for each incoming pulsation dataset, multiple pulsation metrics are calculated during the pulsation metric determination process 306. Pulsation metrics can be calculated using information from a single signal, multiple signals spanning the same pulsation, and / or signals spanning multiple pulsations. Pulsation metrics provide various types of information regarding the quality of a specific pulsation dataset and / or the likelihood of pulsation data being incorporated into 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 may be stored in association with a 3D raster dynamically generated during data acquisition.

[0103] Surface geometry data 318 can be generated simultaneously during the same data acquisition process using the same and / or different triggering and / or pulsatility measurement methods via surface geometry construction process 312. This process constructs the surface geometry using data such as electrode locations and catheter shapes contained in the data stream. Alternatively or concurrently, previously or concurrently collected surface geometry 316 can be used as input to surface geometry data 318. Such geometry may have previously been collected and registered with the catheter positioning system using different mapping datasets and / or different therapies (such as CT, MRI, ultrasound, and / or rotational angiography). The system performs a source selection process 314, in which it selects a source for the surface geometry data and provides the surface geometry data 318 to the surface mapping generation process 320. The surface mapping generation process 320 is used to generate surface mapping data 322 from the mapping dataset 310 and the surface geometry data 318.

[0104] Surface geometry construction algorithms generate anatomical surfaces on which electroanatomical mapping is displayed. For example, the surface geometry can be constructed using aspects of systems described in 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, which are determined by the user or automatically on the surface of the chamber. Additionally, the surface can be adapted to catheter locations within the chamber and / or to the outermost electrode.

[0105] As described above, the mapping dataset 310 from which the surface is constructed can employ the same or different pulsation acceptance criteria as those used for electronic and other types of mapping. The mapping dataset 310 for the surface geometry construction can be collected simultaneously with or separately from the electronic data. The surface geometry can be represented as a mesh containing a set of vertices (points) and their interconnections (e.g., triangles). Alternatively, the surface geometry can be represented by different functions, such as higher-order meshes, non-uniform rational basis splines (NURBS), and / or curved shapes.

[0106] 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, and / or any other information that clinicians expect to collect. The combination of mapping dataset 310 and surface geometry data 318 allows for surface mapping generation. A surface mapping is a collection of values ​​or waveforms (e.g., EGM) on the surface of a 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. 7515954, 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.

[0107] Alternatively, or in combination with the methods described above, an algorithm can be employed that applies acceptance criteria to individual electrodes. 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 into 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 the methods used to project points from the mapping dataset 310 onto the surface and / or select appropriate points may vary.

[0108] Once obtained, the surface mapping data 322 can be further processed to annotate desired features from the underlying data; this process is defined as surface mapping annotation 324. Once data is collected into the surface mapping data 322, attributes associated with the collected data can be automatically presented to the user. These attributes can be automatically determined by the computer system and applied to the data, and are referred to herein as annotations. Exemplary annotations include activation time, presence of dual activation or subdivision, voltage amplitude, and / or spectral content, 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 associated with the incoming data), it is impractical for operators to manually review and annotate the data. However, human input can be a valuable supplement to the data; therefore, when user input is provided, it is necessary for the computer system to automatically propagate and apply it to more than one data point at a time.

[0109] It is possible to use a computer system to automatically annotate the activation time, voltage, and other characteristics of each EGM. Activation time detection can use methods similar to those described previously for detection triggers and can similarly benefit from the use of blanking and power-on trigger operators. The desired annotations may include instantaneous potential, activation time, voltage amplitude, dominant frequency, and / or other signal attributes. Once calculated, the annotations can be overlaid on the chamber geometry. In some embodiments, gap-fill surface mapping interpolation 326 may be employed. For example, in some embodiments, gap-fill interpolation may be employed when the distance between a point on the surface and the measured EGM exceeds a threshold, because this can indicate, for example, that grid-based interpolation as described herein may be less effective in such cases. The displayed mappings 328 may be calculated and displayed individually and / or overlaid on each other.

[0110] Figure 3The 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 illustrated herein. Furthermore, Figure 3 Any one or more of the components depicted herein may be integrated, for example, with various components in other components (and / or components not shown) depicted herein, all of which are considered to be within the scope of this disclosure.

[0111] Figure 4A This is an example flowchart depicting an illustrative method 400A for processing cardiac electrical signals and generating activation waveforms according to some embodiments of the present disclosure. Aspects of embodiments of method 400A may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure 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. Additionally, one or more steps of other embodiments described herein can be added to method 400A. First, the electrophysiological system receives an activation waveform (410A). The activation waveform includes a set of activation waveform data. In some embodiments, the activation waveform is associated with a segment of cardiac signal, such as a signal segment associated with a heartbeat, a predetermined sample window, or a predetermined duration.

[0112] The activation waveform can be generated using an electrical signal collected from the catheter. This catheter can be any catheter with one or more electrodes configured to receive electrical signals (e.g., [insert example here]). Figure 1 (The catheter 110, ablation catheter, etc. depicted in the image). According to embodiments, cardiac electrical signal features can be extracted from cardiac electrical signals (e.g., EGM). Examples of cardiac electrical signal features include, but are not limited to: activation time, minimum voltage value, maximum voltage value, maximum negative time derivative of voltage, instantaneous potential, voltage amplitude, dominant frequency, and / or interpeak voltage, etc. Each of the corresponding points where the cardiac electrical signal is sensed can have a corresponding set of three-dimensional position coordinates. For example, the position coordinates of these points can be represented using Cartesian coordinates. Other coordinate systems can also be used. In some embodiments, an arbitrary origin is used, and the corresponding position coordinates are defined relative to an arbitrary origin. In some embodiments, these points have non-uniform spacing; however, in other embodiments, these points have uniform spacing. In some embodiments, the point corresponding to each sensed cardiac electrical signal can be located on and / or below the endocardial surface of the heart.

[0113] In some embodiments, identifying deflections that deviate beyond a signal baseline may include determining a corresponding activation waveform value for each sample point of the electrical signal. For example, in an embodiment, the system may include determining the probability (e.g., a value between 0 and 1, including 0 and 1) that a given sample point represents activation based on its relationship to the signal baseline. In 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 a signal deflection represents activation 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. Probabilities may be assigned linearly and / or non-linearly to deflections whose amplitude does not meet any of the foregoing criteria (e.g., based on the relative deviation of the deflection amplitude relative to the foregoing criteria). In this way, for example, the activation waveform value may be the probability that the identified deflection represents activation corresponding to a sample point.

[0114] Figure 5A An exemplary graphical representation 500 illustrating electrical signals (in this case, EGM) received from a mapping catheter is depicted, where each signal represents the amplitude of a depolarization sequence of the heart over 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. Figure 5B The waveform of the original cardiac electrical signal 502 and the activation waveform 504 corresponding to the cardiac electrical signal 502 are depicted.

[0115] Return to reference Figure 4AThe system receives a set of window parameters (415A), for example to facilitate the determination of local period lengths. In one embodiment, the set of window parameters includes a range of window sizes (e.g., a minimum window size and a maximum window size). In one example, the window size range can be from 120 to 300 milliseconds, although it should be emphasized that such window sizes are merely exemplary and by no means limiting. In one embodiment, the set of window parameters includes a window increment. The system determines a set of confidence values ​​(420A), where each confidence value corresponds to a window size, and the confidence values ​​are calculated by iterating through window sizes within the range of window sizes. In one embodiment, the window increment size is increased with each iteration of the window size. In some embodiments, multiple steps (e.g., 430A-450A) are performed for each window size (425A). First, the location of the central window (430A) is selected. As used herein, the location of the window (i.e., the time window or sample window) refers to the center point of the window. In one embodiment, the location of the central window is selected based on the pulsation location. For example, the center point of the central window is set to the pulsation location. For example, the pulsation location can be determined, for instance, using aspects of a system described in U.S. Patent No. 9,002,442 entitled “BEAT ALIGNMENT AND SELECTION FOR CARDIAC MAPPING,” the disclosure of which is hereby expressly incorporated herein by reference. In some embodiments, the position of the central window is not selected based on the pulsation location. In some cases, the position of the central window is selected from waveform data points, for example, having a regular interval (e.g., every 15 ms) between two adjacent central windows.

[0116] Next, the backward correlation (435A) between the activation waveform in the central window and the activation waveform in the backward-shifted window is calculated, where the backward-shifted window is the central window shifted backward. In some cases, the backward-shifted window is the central window shifted backward by the same size as the central window. Furthermore, the forward correlation (440A) between the activation waveform in the central window and the activation waveform in the forward-shifted window is calculated, where the forward-shifted window is the central window shifted forward. In some cases, the forward-shifted window is the central window shifted forward by the same size as the central window. Figure 5C An illustrative example is shown of a central window 510, a backward window 512, and a forward window 514, each with a window size D, wherein the central window has low correlation with the backward window and also low correlation with the forward window. Figure 5DThe example activation waveform 504 is shown, along with a central window 520 (window B), a backward window 522 (window A), and a forward window 524 (window C), each with a window size of 125 ms.

[0117] In one example, the correlation between two datasets A(s) and B(s) is calculated using the following equation (1):

[0118] C = f(A(s), B(s)) (1), where C is the correlation value and f is the selected correlation function. In one case, the correlation function is sensitive to amplitude similarity; for example, the correlation value C is highest when A(s) and B(s) have a high level of similarity and relatively high amplitude values. Figure 5E This example shows a set of correlation values ​​for different window sizes. In this example, the correlation between a central window and a backward-moving window of different sizes is shown. As the figure shows, the correlation value has its maximum value (i.e., 530) at a window size of 230ms. Figure 5G Another illustrative example showing the set of correlation values ​​for different window sizes is provided. This example demonstrates the correlation between a central window and a forward-moving window of different sizes. As shown, the correlation has a maximum value (i.e., 540) at a window size of 220ms.

[0119] Return to reference Figure 4A The backward confidence value is determined based on the backward correlation (445A), and the forward confidence value is determined based on the forward correlation (450A), wherein the backward and forward confidence values ​​are added to the confidence value set. In some cases, the confidence value is determined based on a function of the correlation value and the activation weights. The activation weights are related to the amplitude of the data points of the activation waveform in the corresponding window (e.g., the backward window, the central window, and the forward window). In some cases, the weighted activation is related to the maximum amplitude of the activation waveform in the corresponding window. For example, the confidence value is calculated using equation (2):

[0120] Cf = fc(fw(AW), C) (2), where Cf is the confidence value, AW is the activation weight, fw is the function used to determine the weighting factor, C is the correlation value of the corresponding window, and fc is the function used to determine the confidence value. In one example, the function fw is a linear function, for example, used to normalize the confidence value between 0 and 1, and is proportional to the activation weight. In another example, the function fw is a binary function, such as a weighting factor of 0 if the activation weight is below a threshold, and a weighting factor of 1 if the activation weight is above a threshold. In yet another example, the function fw is an error function.

[0121] In some cases, the system determines a central weighting factor associated with the amplitude of the active waveform in the central window of a specific window size during traversal of 425A. The backward confidence value for a specific window size can be determined based on the central weighting factor, and the forward confidence value for a specific window size can also be determined based on the central weighting factor. In some cases, the system also determines a backward weighting factor associated with the amplitude of the active waveform in the backward window of a specific window and / or a forward weighting factor associated with the amplitude of the active waveform in the forward window. The backward confidence value for a specific window size can be determined based on backward correlation, the central weighting factor, and the backward weighting factor, and the forward confidence value for a specific window size can be determined based on forward correlation, the central weighting factor, and the forward weighting factor.

[0122] In one embodiment, the central weighting factor is determined by applying a nonlinear function to the relevant amplitude (e.g., maximum amplitude) of the activated waveform in the central window. In another embodiment, the central weighting factor is determined by applying an error function to the relevant amplitude of the activated waveform in the central window. In yet another embodiment, the central weighting factor is determined by applying a linear function to the relevant amplitude of the activated waveform in the central window.

[0123] In some embodiments, the electrophysiological system determines correlation values ​​and confidence values ​​across different window sizes within a range, and compares a set of confidence values ​​to select a specified confidence value and a selected window size (455A) corresponding to the specified confidence value. In some cases, the specified confidence value is the largest confidence value in the set. As described above, each confidence value is determined to have a corresponding window size. Figure 5E In the example shown, the specified confidence value could be data point 530, corresponding to a window size of 230ms. Figure 5G In the example shown, the specified confidence value can be data point 540, with a corresponding window size of 220ms.

[0124] Return to reference Figure 4A The electrophysiological system can determine the local cycle length (460 Å) based on a selected window size. In some cases, the local cycle length is the selected window size. In other cases, the local cycle length is determined based on the selected window size, for example, with an adjustment amount. In some cases, the system determines the duty cycle (465 Å) based on the activation waveform and the selected window size. In some embodiments, the system determines the duty cycle based on the activation waveform in a central window with the selected window size. In some cases, the duty cycle is the average amplitude of the sampling points of the activation waveform in the central window.

[0125] In some embodiments, the activation waveform is associated with data collected from multiple channels. For example, Figure 5AThe diagram shows cardiac electrical signals collected from 64 channels. In such an embodiment, the activation waveforms include multiple channel activation waveforms, and each of the multiple channel activation waveforms includes activation waveform data for one of the multiple channels. Figure 4B This is another example flowchart depicting an illustrative method 400B for processing electrophysiological information across multiple channels according to some embodiments of this disclosure. Aspects of embodiments of method 400B may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2 The processing unit 200 depicted herein executes 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. Additionally, one or more steps of other embodiments described herein can be added to method 400B. The electrophysiological system receives an activation waveform (405B) comprising multiple channel activation waveforms, each of which corresponds to a corresponding one of multiple selected channels. In some cases, the selected channels include all channels of the mapping catheter. In some cases, the selected channels include channels conforming to specific criteria. In some embodiments, the activation waveform is associated with a segment of cardiac electrical signal, e.g., a signal segment associated with a heartbeat, a predetermined sample size, or a predetermined duration.

[0126] The system receives a set of window parameters (407B), which includes, for example, a range of window sizes and an increment of window sizes. The system is configured to traverse the window sizes within this range and determine the correlation and confidence values ​​associated with the activation waveform data. For each window size (410B), the system selects the position of the central window (415B). For each of the multiple selected channels (420B), for each channel associated with the channel activation waveform, the electrophysiological system calculates the channel backward correlation (422B) of the channel activation waveform in the central window with that in the backward window; and calculates the channel forward correlation (424B) of the channel activation waveform in the central window with that in the forward window. The system also determines a backward confidence value (430B) based on the backward correlation calculated for each selected channel. The system also determines a forward confidence value (435B) based on the forward correlation calculated for each selected channel. In one example, the confidence value is determined using the following equation (3):

[0127]

[0128] Where Cf is the confidence value, Ch is the number of channels, NCh is the total number of selected channels, AW(Ch) is the activation weight factor for each channel, C(Ch) is the correlation value of the channel for the corresponding window, and fc is the function used to determine the confidence value. In some embodiments, the calculated confidence value Cf is normalized, for example, in the range of 0-1.

[0129] In some embodiments, the electrophysiological system compares backward confidence values, each for a window size, to select a specified backward confidence value (440B) corresponding to a first selected window size (i.e., the backward local cycle length). In some embodiments, the electrophysiological system also compares forward confidence values, each for a window size, to select a specified forward confidence value (445B) corresponding to a second selected window size (i.e., the forward local cycle length). In some cases, the specified confidence value is the highest confidence value in the set. Next, the system may determine the local cycle length (450B) based on the first and second selected window sizes. In one embodiment, the local cycle length is the average of the first and second selected window sizes. For example, in the case where the first selected window size is 232 ms and the second selected window size is 228 ms, the local cycle length is 230 ms. In some embodiments, the system determines the local duty cycle (460B) based on the activation waveform and the local cycle length. In one embodiment, the system selects the maximum amplitude of the channel activation waveform across multiple selected channels for each sampling point within the central window of the local cycle length. In some embodiments, the system may calculate the average of these selected maximum amplitudes in the central window as the local duty cycle.

[0130] In some embodiments, the electrophysiological system further determines segment confidence values ​​(470B) for the activation waveform. In one embodiment, the segment confidence value is determined based on a specified backward confidence value and a specified forward confidence value. In another embodiment, the segment confidence value is determined based on the smaller of the specified backward confidence value and the specified forward confidence value. In yet another embodiment, the system calculates the backward-forward correlation between the channel activation waveform in the backward window and the channel activation waveform in the forward window for each selected channel. The system also determines the backward-forward confidence value for each window size, for example, using equation (3). Thereafter, the system selects a specified backward-forward confidence value from a set of backward-forward confidence values ​​for various window sizes. For example, the specified backward-forward confidence value is the highest value in the set of backward-forward confidence values. In one embodiment, the segment confidence value is determined based on the specified backward confidence value, the specified forward confidence value, and the specified backward-forward confidence value. In one embodiment, the segment confidence value is determined based on the minimum of a specified backward confidence value, a specified forward confidence value, and a specified backward-forward confidence value. In some embodiments, the segment confidence value is also determined based on the difference between a first selected window size (i.e., the backward local period length) and a second selected window size (i.e., the forward local period length).

[0131] In some embodiments, the electrophysiological system may use a jitter interval to slightly shift the window to reduce the computational cost of the relatively large window increment. Figure 4C This is another example flowchart depicting an illustrative method 400C for processing electrophysiological information according to some embodiments of the present disclosure. Aspects of embodiments of method 400C may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 depicted in the image Figure 2 The processing unit 200, computational processing unit, and / or graphics processing unit depicted herein execute the method. One or more steps of method 400C are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 400C. The electrophysiological system receives an activation waveform comprising multiple channel activation waveforms, each channel waveform corresponding to multiple selected channels (405C). The system receives a set of window parameters (407C) including, for example, a window size range (e.g., 120 ms to 250 ms), a window size increment (e.g., 10 ms), a jitter range (e.g., -5 ms to 5 ms), and a jitter interval (e.g., 1 ms). In one embodiment, the jitter interval is less than the window size increment.

[0132] The system is configured to traverse window sizes within the range, and for each window size (410C), the system selects the position of the central window (415C). Next, for each of the selected channels (420C), each channel is associated with a channel activation waveform. The electrophysiological system calculates a channel backward correlation set (422C) based on the channel activation waveform in the central window and the channel activation waveform in the backward jitter window set; and calculates a channel forward correlation set (424C) based on the channel activation waveform in the central window and the channel activation waveform in the forward jitter window set. In some cases, the selected channels include all channels of the mapping catheter. In some cases, the selected channels include channels that meet specific criteria. In some embodiments, the channel activation waveform is associated with a segment of the channel's cardiac electrical signal, for example, a signal segment associated with a heartbeat, a predetermined sample size, or a predetermined duration.

[0133] In some cases, each of the channel backward correlation sets is the correlation between the channel activation waveform in the central window and the channel activation waveform in the backward shifted window, with jitter adjustment within a jitter range (e.g., -5ms to 5ms). In one example, assuming a central window of size 200ms at position 400ms and a backward shifted window at position 200ms, the backward jitter window set includes windows at positions 195ms, 196ms, 197ms, 198ms, 199ms, 200ms, 201ms, 202ms, 203ms, 204ms, and 205ms. Figure 5F An illustrative example of a set of channel backward correlations with a jitter range of -5ms to 5ms and a jitter interval of 1ms is shown. In some cases, each channel forward correlation in the set of channel forward correlations is the correlation between the channel activation waveform in the central window and the channel activation waveform in the forward window, with a jitter adjustment amount within the jitter range (e.g., -5ms to 5ms). In one example, assuming a central window with a window size of 200ms at position 400ms and a forward window at position 600ms, the set of forward jitter windows includes windows at positions of 595ms, 596ms, 597ms, 598ms, 599ms, 600ms, 601ms, 602ms, 603ms, 604ms, and 605ms. Figure 5H An illustrative example of a channel forward correlation set with a jitter range of -5ms to 5ms and a jitter interval of 1ms is shown.

[0134] In some embodiments, for a given window size, the electrophysiological system selects a specified channel backward correlation from a set of channel backward correlations that has a channel backward jitter value. Figure 5FIn the example shown, data point 535 is a specified channel backward correlation (e.g., 0.972) with a channel backward jitter value (e.g., 1 ms). In some embodiments, the electrophysiological system selects a specified channel forward correlation with a channel forward jitter value from a set of channel forward correlations for a given window size. Figure 5H In the example shown, data point 545 is a specified channel forward correlation (e.g., 0.97) with a channel forward jitter value (e.g., 0 ms).

[0135] For each window size, the system also determines a backward confidence value (430C) based on channel backward correlation. In one embodiment, for each selected channel, the channel backward correlation includes the set of channel backward correlations of the channel activation waveform in the central window and the channel activation waveform in the set of backward dithering windows. In one embodiment, for each selected channel, the channel backward correlation includes a specified channel backward correlation. For each window size, the system also determines a forward confidence value (435C) based on channel forward correlation. In one embodiment, the channel forward correlation includes the set of channel forward correlations of the channel activation waveform in the central window and the channel activation waveform in the set of forward dithering windows. In one embodiment, for each selected channel, the channel forward correlation includes a specified channel forward correlation.

[0136] In some embodiments, the electrophysiological system compares a set of backward confidence values, each for a window size, to select a specified backward confidence value corresponding to a first selected window size, and calculates a first selected jitter value (440C). In one embodiment, the specified backward confidence value is the highest backward confidence value across window sizes. In one embodiment, the first selected window size corresponds to the highest backward confidence value across window sizes. In some cases, after determining the first selected window size, the system calculates the first selected jitter value based on the amplitude of the activated waveform in the backward-shifted window and the channel backward jitter value across the window size. In some embodiments, the electrophysiological system also compares a set of forward confidence values, each for a window size, to select a specified forward confidence value corresponding to a second selected window size, and calculates a second selected jitter value (445C). In some cases, the specified forward confidence value is the highest forward confidence value in the set. In one embodiment, the first selected window size corresponds to the highest forward confidence value across window sizes. In some cases, after determining the second selected window size, the system calculates the second selected jitter value based on the amplitude of the active waveform in the forward window and the channel forward jitter value across the window size.

[0137] Next, the system can determine the local cycle length (450C) based on a first selected window size and a second selected window size. In one embodiment, the local cycle length is the average of the first selected window size and the second selected window size. For example, the local cycle length is the average of a 231ms window size and a 220ms window size. In another embodiment, the system determines the local cycle length based on the selected window size adjusted by the jitter value. In some embodiments, the system determines the local duty cycle (460C) based on the active waveform and the local cycle length. In one embodiment, for each sampling point of the central window of the local cycle length, the system selects the maximum amplitude of the channel active waveforms of multiple selected channels. In some embodiments, the system can calculate the average of these selected amplitudes of the window as the local duty cycle. In one embodiment, the system generates a maximum waveform based on the multiple channel active waveforms in the selected central window, wherein each data point of the maximum waveform has the maximum value of the multiple channel active waveforms of the multiple selected channels at the corresponding data point. Figure 5I An illustrative example of the maximum waveform is depicted. In one embodiment, the system determines the duty cycle based on the maximum waveform. In another case, the duty cycle is determined as the average of the data points of the maximum waveform.

[0138] Figure 4D This is yet another example flowchart depicting an illustrative method 400D for processing electrophysiological information according to some embodiments of the present disclosure. Aspects of embodiments of method 400D may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2 The processing unit 200 depicted herein executes the method. One or more steps of method 400D are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 400D. The electrophysiological system receives an activation waveform comprising multiple channel activation waveforms, each corresponding to one of a plurality of selected channels (405D).

[0139] In some cases, the selected channels include all channels of the mapping catheter. In other cases, the selected channels include channels that meet specific criteria. In some embodiments, the activation waveform is associated with a segment of cardiac electrical signal, such as a signal segment associated with a heartbeat, a predetermined sample size, or a predetermined duration. The system receives a set of window parameters (407D) that includes, for example, a window size range (e.g., 120 ms to 250 ms), a window size increment (e.g., 10 ms), a jitter range (e.g., -5 ms to 5 ms), and a jitter interval (e.g., 1 ms). In one embodiment, the jitter interval is smaller than the window size increment. The system is configured to traverse the window size within this range (e.g., 120 ms, 130 ms, etc.).

[0140] For each window size (410D), the system selects the position of the central window (415D). For each of the multiple selected channels (420D), each channel is associated with the channel activation waveform, and the electrophysiological system calculates the channel backward correlation set (422D) of the channel activation waveform in the central window with the channel activation waveform in the backward jitter window set; and calculates the channel forward correlation set (424D) of the channel activation waveform in the central window with the channel activation waveform in the forward jitter window set. In some cases, each of the channel backward correlation sets is the correlation of the channel activation waveform in the central window with the channel activation waveform in the backward window, with jitter adjustment within the jitter range (e.g., -5ms to 5ms). In one example, the correlation of the channel backward correlation set for a specific window size N, a specific channel Ch, and a specific jitter J can be calculated using the following equation (4):

[0141]

[0142] Where CiB(Ch,J) is the correlation value, Ch is the specific channel, J is the specific jitter value, N is the window size, s is the sample point, Central(s) is the amplitude of the activation waveform of the central window at sample point s, and Backward(s) is the amplitude of the activation waveform of the backward jitter (Jittered J) window at sample point s.

[0143] In some cases, each of the channel forward correlation sets is the correlation between the channel activation waveform in the central window and the channel activation waveform in the forward window, with jitter adjustment within the jitter range (e.g., -5ms to 5ms). In one example, the correlation of the channel forward correlation set for a specific window size N, a specific channel Ch, and a specific jitter J can be calculated using the following equation (5):

[0144]

[0145] Where CiF(Ch,J) is the correlation value, Ch is the specific channel, J is the specific jitter value, N is the window size, s is the sample point, Central(s) is the amplitude of the activation waveform of the central window at sample point s, and Forward(s) is the amplitude of the activation waveform of the forward jitter (Jittered J) window at sample point s.

[0146] In some embodiments, for each channel, the electrophysiological system compares a set of channel backward correlations to select a specified channel backward correlation and a channel backward jitter value (426D) corresponding to that specified channel backward correlation. In some cases, the specified channel backward correlation is the highest correlation value in the set of channel backward correlations. Figure 5F In the example shown, the correlation at data point 535 is a specified channel backward correlation (e.g., 0.972) with a corresponding channel backward jitter value (e.g., 1 ms). In some embodiments, for each channel, the system may compare a set of channel forward correlations to select a specified channel forward correlation and a channel forward jitter value (428D) corresponding to the specified channel forward correlation. Figure 5H In the example shown, the correlation at data point 545 is the specified channel forward correlation (e.g., 0.97) with the corresponding channel forward jitter value (e.g., 0ms).

[0147] For each window size, the system also determines a backward confidence value (430D) based on a specified channel backward correlation. In one embodiment, the specified channel backward correlation includes the specified channel backward correlation for each selected channel. In some cases, the system determines the activation weight W of the central window for all selected channels. C In some cases, the system determines the activation weight W for the shifted window across all selected channels. B In some cases, the system determines the activation weight W for the forward window of all selected channels. F In some embodiments, the activation weight across the selected channel is an indication of the signal amplitude within the corresponding window. In some cases, the activation weight across the selected channel indicates whether activation occurs within the corresponding window.

[0148] In some cases, activation weights are determined based on the maximum value of the activation waveform. In some cases, activation weights are determined based on a nonlinear function applied to the maximum value of the activation waveform. In some cases, activation weights are determined based on a linear function applied to the maximum value of the activation waveform. In some cases, activation weights are determined based on a bivariate function applied to the maximum value of the activation waveform. In some cases, activation weights are determined based on an error function applied to the maximum value of the activation waveform. In one example, the backward confidence value Cf for window size s... BThe following equation (6) can be used to calculate:

[0149]

[0150] Where CfB(s) is the backward confidence value, WB(Ch) is the activation weight for the backward shift window of channel Ch, CB(Ch) is the specified channel backward correlation value at channel Ch, Ch is the channel, NCh is the number of selected channels, and W... C It is the activation weight of the central window across all selected channels, and W B This is the activation weight of the shifted window across all selected channels. In some cases, the activation weights are specific to a particular window and a specific channel (e.g., W). C (Ch) is determined based on the highest amplitude of the activated waveform within a specific window for a specific channel. In some cases, activation weights (e.g., W) are assigned to specific windows and specific channels. C (Ch) is the highest amplitude of the active waveform in a specific window for a specific channel.

[0151] For each window size, the system can also determine a forward confidence value (435D) based on a specified channel forward correlation. In one embodiment, the specified channel forward correlation includes a specified channel forward correlation for each channel. In one example, the forward confidence value Cf for window size s F The following equation (7) can be used to calculate:

[0152]

[0153] Where CfF(s) is the forward confidence value, WF(Ch) is the activation weight for the forward window of channel Ch, CF(Ch) is the specified channel forward correlation value at channel Ch, Ch is the channel, NCh is the number of selected channels, and W... C It is the activation weight of the central window across all selected channels, and W F It is the activation weight of the forward window across all selected channels.

[0154] In some embodiments, the electrophysiological system compares a set of backward confidence values, each for a window size, to select a specified backward confidence value (440D) corresponding to a first selected window size. In some embodiments, the electrophysiological system also compares a set of forward confidence values, each for a window size, to select a specified forward confidence value (442D) corresponding to a second selected window size. In one embodiment, the specified confidence value is the highest confidence value in the set. In some embodiments, the system determines a backward jitter value (444D) based on a channel backward jitter value, one channel backward jitter value per channel. In one example, the backward jitter value J...B The following equation (8) can be used to calculate:

[0155]

[0156] Where JB is the back jitter value, WB(Ch) is the activation weight for the backward window of channel Ch, JB(Ch) is the channel back jitter value for channel Ch, Ch is the channel, and NCh is the number of selected channels. In some cases, the activation weight for a specific window and a specific channel (e.g., WB(Ch)) is the highest amplitude of the activated waveform in the specific window for a specific channel.

[0157] In some embodiments, the system determines the forward jitter value (446D) ​​based on the channel forward jitter value, one channel forward jitter value per channel. In one example, the forward jitter value J F The following equation (9) can be used to calculate:

[0158]

[0159] Where JF is the backward jitter value, WF(Ch) is the activation weight for the forward window of channel Ch, JF(Ch) is the channel backward jitter value for channel Ch, Ch is the channel, and NCh is the number of selected channels. In some cases, the activation weight for a specific window and a specific channel (e.g., WF(Ch)) is the highest amplitude of the activated waveform in the specific window for a specific channel.

[0160] Next, the system can determine the local period length (450D) based on the first and second selected window sizes and the backward and forward jitter values. In one embodiment, the local period length is the average of the first selected window size adjusted by the backward jitter value and the second selected window size adjusted by the forward jitter value. For example, with a first selected window size of 230ms and a backward jitter value of 1ms, and a second selected window size of 220ms and a forward jitter value of 0ms, the local period length is 226ms. In some embodiments, the system determines the local duty cycle (460D) based on the active waveform and the local period length. In one embodiment, for each sampling point of the central window of the local period length, the system selects the maximum amplitude of the channel active waveforms of multiple selected channels. In some embodiments, the system can calculate the average of these selected amplitudes of the window as the local duty cycle. In one embodiment, the system generates a maximum waveform based on the multiple channel active waveforms in the selected central window, wherein each data point of the maximum waveform has the maximum value of the multiple channel active waveforms among the multiple selected channels at the corresponding data point. Figure 5IAn example of the maximum activation waveform across selected channels (e.g., 64 channels) for a local period length (e.g., 226 ms) is shown.

[0161] In one embodiment, the system determines the duty cycle based on the maximum waveform. In one case, the duty cycle is determined as the average of the maximum waveform data points. In some embodiments, the electrophysiological system determines a segment confidence value (470D). In one embodiment, the segment confidence value is determined based on a specified backward confidence value and a specified forward confidence value. In one embodiment, the segment confidence value is determined based on the smaller of the specified backward confidence value and the specified forward confidence value. In another embodiment, the system calculates backward-forward correlations, treating each backward-forward correlation as the correlation between the channel activation waveform in the backward window and the channel activation waveform in the forward window for each selected channel. Backward-forward correlations can be determined using embodiments similar to those used to determine forward and backward correlations. The system also determines backward-forward confidence values ​​based on backward-forward correlations.

[0162] In one example, the backward-forward confidence value CfBF for window size s can be calculated using the following equation (10):

[0163]

[0164] Among them, Cf BF (s) is the back-forward confidence value, W BF (Ch) is the activation weight for the backward and forward shift windows of channel Ch, C BF (Ch) is the specified channel backward-forward correlation value at channel Ch, where Ch is the number of channels, NCh is the number of channels selected, and W... B It is the activation weight of the shifted window across all selected channels, and W F It is the activation weight of the forward window across all selected channels. In some cases, it is specific to a particular window and a specific channel (e.g., W). C The activation weights (Ch) are determined based on the highest amplitude of the activated waveform within a specific window for a specific channel. In some cases, the activation weights are determined for specific windows (e.g., shifting the window backward and forward) and specific channels (e.g., W). BF The activation weight (Ch) is the highest amplitude of the activated waveform in a specific window for a specific channel.

[0165] The electrophysiological system can select a specified backward-forward confidence value from a set of backward-forward confidence values ​​for various window sizes. For example, the specified backward-forward confidence value is the highest value in the set of backward-forward confidence values. In one embodiment, the segment confidence value is determined based on the specified backward confidence value, the specified forward confidence value, and the specified backward-forward confidence value. In another embodiment, the segment confidence value is determined based on the minimum of the specified backward confidence value, the specified forward confidence value, and the specified backward-forward confidence value. In some embodiments, the segment confidence value is also determined at least in part based on the difference between a first selected window size (i.e., the backward local cycle length) and a second selected window size (i.e., the forward local cycle length).

[0166] In some cases, the difference between the first and second selected window sizes is used as input to a nonlinear function used to determine the weighting factor. In some designs, the weighting factor is a value between 0 and 1. In one example, the weighting factor is set to a relatively large value (e.g., 1) when the difference between the first and second selected window sizes is relatively small (e.g., 0). In another example, the weighting factor is set to a relatively small value (e.g., 0.2) when the difference between the first and second selected window sizes is relatively large (e.g., 30 ms). In some cases, the segment confidence value is determined based on the weighting factor, a specified backward confidence value, a specified forward confidence value, and a specified backward-forward confidence value. In some embodiments, the electrophysiological system includes downweighting techniques to remove signals lacking consistency. In some cases, downweighting (e.g., random downweighting) is a form of outlier rejection. In some cases, the system reduces the weight of the confidence of individual beats or channels whose signals are inconsistent with the local distribution. In some implementations, random downweighting is used to remove false positive highlight areas from spurious beats whose duty cycle or period length does not match the surrounding area.

[0167] It has been demonstrated that clear, consistent tissue with discrete cycle length patterns exists in certain regions of the atrium during atrial fibrillation (AF). In embodiments of this disclosure, aggregating local cycle length measurements into a histogram (e.g., a 1D local cycle length histogram) allows users to visually and / or study these patterns by having regions of interest on a cardiac mapping. In some implementations, only activated waveform segments associated with heartbeats whose confidence level is above a user-defined threshold are included in the histogram.

[0168] Figure 6 This is a flowchart depicting an illustrative method 600 for processing electrophysiological information to generate a histogram according to some embodiments of the present disclosure. Aspects of embodiments of method 600 may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2 The processing unit 200 depicted herein executes the method. One or more steps of method 600 are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 600. First, the electrophysiological system receives an activation waveform (610), which includes a set of activation waveform data of multiple signal segments collected at multiple locations. In some cases, the multiple locations include a portion or all of the heart chambers. In some cases, the multiple locations are selected based on, for example, user input (e.g., input via a user interface such as a graphical user interface), system input (e.g., system configuration), or software input (e.g., input via an application programming interface, web services, etc.). In some cases, the multiple locations are selected within a predetermined radius of a probe (e.g., a flowing probe) location. In some designs, the flowing probe can move around within the heart chambers, and the position of the flowing probe changes accordingly. In some cases, the flowing probe position is indicated by input (e.g., user input, system input, or software input, etc.). The system uses any of the embodiments described herein to determine multiple local period lengths corresponding to multiple signal segments (620). In some embodiments, the system may also use any of the embodiments described herein to determine multiple local duty cycles corresponding to multiple signal segments (623). In some embodiments, the system further uses any of the embodiments described herein to determine multiple segment confidence values, each segment confidence value corresponding to one of the multiple local period lengths (627). In some embodiments, each of the multiple segment confidence values ​​is a confidence value for a signal segment.

[0169] Next, the system can generate a local period length histogram (630) based on multiple local period lengths. In some embodiments, the local period length histogram is a one-dimensional histogram. In some embodiments, the bars of the local period length histogram are in milliseconds. In some cases, the local period length histogram is based on local period lengths with confidence values ​​greater than a predetermined threshold. In some embodiments, the system can generate a local duty cycle histogram (633) based on multiple local duty cycles. In some embodiments, the local duty cycle histogram is a one-dimensional histogram. In some embodiments, the bars of the local duty cycle histogram are between 0 and 1. In some cases, the local duty cycle histogram is based on local duty cycles whose confidence values ​​are greater than a predetermined threshold. In some embodiments, the system can generate a confidence value histogram (637) based on multiple segment confidence values. In some embodiments, the confidence value histogram is a one-dimensional histogram. In some embodiments, the bars of the confidence value histogram are between 0 and 1.

[0170] In addition, the system can generate a representation of the local period length histogram (640). Figure 7A This is an illustrative example of a local period length histogram. The system can also generate a representation of a local duty cycle histogram (643). Figure 7B This is an illustrative example of a local duty cycle histogram. In some cases, the system generates a representation of a confidence value histogram (647). Figure 7C This is an illustrative example of a confidence value histogram. In some embodiments, the system may receive input (650) regarding the region of interest, such as the region of interest for the local period length, the region of interest for the local duty cycle, and / or the region of interest for the confidence value. In some cases, input regarding the region of interest may be received from a user, for example, via a graphical user interface. In some cases, input regarding the region of interest may be received from configuration settings and / or profile settings. In some cases, input regarding the region of interest may be received from a software interface (e.g., an application programming interface or a web service, etc.).

[0171] In some embodiments, the system may display and / or overlay a region of interest (655) on a histogram representation, which includes a representation of a local cycle length histogram, a representation of a local duty cycle histogram, and / or a representation of a confidence value histogram. In some cases, the histogram representation is displayed with cardiac mapping. Figure 7D An illustrative example of a representation of a local cycle length histogram 700D with a cardiac mapping 710D is depicted. The local cycle length histogram representation 700D includes a region of interest 701D. As shown, the cardiac mapping 710D has an indication of local cycle length values ​​on the mapping. In one example, the cardiac mapping 710D indicates each value / amplitude of the local duty cycle by color or grayscale at the detection location. When the electrophysiological system receives input for the region of interest 701D, the system can update the cardiac mapping 710D to highlight the electrogram with local cycle lengths within the region of interest 701D. Spatial dispersion of the highlighted areas in the cardiac mapping can aid in clinical diagnosis.

[0172] Understanding how local cycle length and duty cycle data spatially cluster within an anatomical context helps in locating atrial fibrillation (AF) drivers within cardiac chambers. Various graphical representations of electrogram characteristics (e.g., local cycle length, local duty cycle) can be generated, including interactive graphical representations. Figure 8A This is a flowchart depicting an illustrative method 800A for processing electrophysiological information to generate a representation of electrogram characteristics according to some embodiments of the present disclosure. Aspects of embodiments of method 800A may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2The processing unit 200 depicted herein executes the method. One or more steps of method 800A are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 800A. First, the electrophysiological system receives an activation waveform and a set of cardiac electrical signals (810A), for example, the set of cardiac electrical signals includes a set of activation waveform data of multiple signal segments collected at multiple locations. In some cases, the multiple locations include a portion or all of the heart chambers. In some cases, the multiple locations are selected based on, for example, user input (e.g., input via a user interface such as a graphical user interface), system input (e.g., system configuration), or software input (e.g., input via an application programming interface, web services, etc.). In some cases, the multiple locations are selected within a predetermined radius of a probe (e.g., a flowing probe) location. In some designs, the flowing probe can move around within the heart chambers, and the position of the flowing probe changes accordingly. In some cases, the flowing probe position is indicated by input (e.g., user input, system input, or software input, etc.).

[0173] The system determines a set of electrogram characteristics (820A). In some cases, the set of electrogram characteristics includes multiple local period lengths corresponding to multiple signal segments. The multiple local duty cycles can be determined using any of the embodiments described herein. In some cases, the set of electrogram characteristics includes multiple local duty cycles corresponding to multiple signal portions. The multiple local duty cycles can be determined using any of the embodiments described herein. In some cases, the set of electrogram characteristics includes multiple segment confidence values, each segment confidence value corresponding to one of the multiple local period lengths. The multiple segment confidence values ​​can be determined using any of the embodiments described herein. In some embodiments, each of the multiple segment confidence values ​​is a confidence value for a signal segment.

[0174] Next, the system generates a representation (830A) of a set of electrophoretic characteristics (e.g., local period length, local duty cycle, confidence value, etc.). In some embodiments, the representation is a graphical representation. In some embodiments, the representation is an interactive graphical representation, for example, taking input from the user and adjusting or changing the representation based on the input. In one example, the representation is a graphical representation of one or more histograms. Figures 7A-7D Examples are shown, for instance, to illustrate the spatial patterns and consistency of the various characteristics. In one embodiment, the representation is a cardiac mapping, which has one or more electrogram characteristics indicated on the mapping and the values / amplitudes of the characteristics represented in grayscale or color. Figure 9AAn illustrative example of a cardiac mapping with electrogrammatic characteristic indications is depicted, wherein a 3D cardiac mapping 901A includes a local cycle length indication, and a 3D cardiac mapping 902A includes a local duty cycle indication. In one case, one or more electrogrammatic characteristics (e.g., local cycle length, local duty cycle, etc.) shown in the graphical representation are characteristics whose confidence values ​​are higher than a predetermined threshold.

[0175] In another embodiment, the representation is a 3D cardiac mapping map with one or more histograms shown sideways. In one case, the system receives input from a user or a software interface regarding the region of interest in the histogram and updates the 3D cardiac mapping map with the corresponding features. (Return to reference) Figure 7D An illustrative example is depicted of a local cycle length histogram 700D with a cardiac mapping 710D. When the electrophysiological system receives input for a region of interest 701D, the system can update the cardiac mapping 710D to highlight the electrorecord within the region of interest 701D that has a local cycle length.

[0176] In some embodiments, the electrophysiological system allows the user to move a flow probe on a cardiac mapping to highlight certain areas of the heart chambers, and the graphical representation of the electrogram characteristics updates accordingly to the changes in the highlighted areas. Figure 9B An illustrative example of a graphical representation 900B is depicted, which has a flowable probe 912B that can be moved around on a cardiac mapping 910B. The flowable probe 912B is associated with a highlighted region 914B having a predetermined radius from the location of the flowable probe, also referred to as a flashlight region. A graphical representation 920B of the electrogram characteristics is updated corresponding to changes in the highlighted region 914B. In some cases, the highlighted region 914B is a circle of a predetermined radius of the flowable probe 912B. In the example shown, the graphical representation 920B of the electrogram characteristics and the exploded plot 922B display multiple histograms, including a confidence histogram, a local period length histogram, and a local duty cycle histogram.

[0177] In some cases, the graphical representation is a scatter plot. In one example, the x-axis of the scatter plot is the local period length, and the y-axis is the local duty cycle. Figure 9C An illustrative example of a scatter plot 900C is depicted. In some designs, the system allows the user to select a region of interest with a range of local period lengths and local duty cycles. In some cases, the scatter plot can be correlated with the histogram of electrical recording characteristics. Figure 1 It appears. Figure 9CIn the diagram, the local period length histogram 910C is shown together with the scatter plot 900C, and the region of interest 920C is selected by the range of local period length 912C and the range of local duty cycle 914C. In one embodiment, the points in the scatter plot are labeled with different colors (e.g., red) or different shades of gray for points within the radius of the flow probe.

[0178] In some embodiments, the graphical representation is a scatter plot showing one or more 3D cardiac mapping maps. Figure 9D An illustrative example of such a scatter plot 900D is depicted, having a scatter plot 910D and one or more cardiac mapping plots 920D. In the example shown, the scatter plot 910D and... Figure 9C The graphic representation shown is identical to 900C. One or more 3D cardiac mapping maps 920D include a cardiac mapping map 922D displaying a local cycle length indication and a cardiac mapping map 924D displaying a local duty cycle indication. In one example, the electrophysiological system receives input about the region of interest from a user or software interface and updates the 3D cardiac mapping map based on the input. Figure 9D In the example shown, when the region of interest 914D changes, the corresponding highlighted regions of interest 923D and 925D (i.e., the corresponding electrogrammatic features within the regions of interest) change. For example, when the range of local cycle length changes, the highlighted region 923D in the cardiac mapping 922D changes. As another example, when the range of local duty cycle changes, the highlighted region 925D in the cardiac mapping 924D changes. In one embodiment, one or more electrogrammatic features (e.g., local cycle length, local duty cycle, etc.) shown in the graphical representation are features with confidence values ​​above a predetermined threshold. In one embodiment, the graphical representation uses confidence values ​​to create a mask such that electrogrammatic features with confidence values ​​below the predetermined threshold are identifiable, for example, displayed in gray, while electrogrammatic features with confidence values ​​above the predetermined threshold are displayed in color.

[0179] In some embodiments, the system may receive input (840A) of one or more representation parameters, such as the position of the flow probe, the radius of the highlighted region, the region of interest for the local period length, the region of interest for the local duty cycle, and / or the region of interest for the confidence value. In some cases, the parameter input may be received from a user, for example, via a graphical user interface. In some cases, the parameter input may be received from configuration settings and / or profile settings. In some cases, the parameter input may be received from a software interface (e.g., an application programming interface, or a web service, etc.). In some embodiments, the system may adjust the representation (845A) based on the input, for example, by updating the representation using only signals with a local duty cycle in the region of interest.

[0180] In some embodiments, electrogram characteristics can be used to refine cardiac mapping. In one example, the cardiac mapping is superimposed with an activation waveform. Figure 8B This is a flowchart depicting an illustrative method 800B for refining cardiac mapping using representations of electrogram characteristics according to some embodiments of the present disclosure. Aspects of embodiments of method 800B may be derived, for example, from an electrophysiological system or processing unit (e.g., Figure 1 The processing unit 120 and / or depicted in the figure Figure 2 The processing unit 200 depicted herein performs the method. One or more steps of method 800B are optional and / or can be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein can be added to method 800B. First, the electrophysiological system receives an activation waveform (810B) comprising a collection of activation waveform data of multiple signal portions collected at multiple locations. In some cases, the multiple locations comprise a portion or all of a cardiac chamber. In some cases, the multiple locations are selected based on, for example, user input (e.g., input via a user interface such as a graphical user interface), system input (e.g., system configuration), or software input (e.g., input via an application programming interface, web services, etc.). In some cases, the multiple locations are selected within a predetermined radius of a probe (e.g., a flowing probe) location. In some designs, the flowing probe can move throughout the cardiac chamber, and its position changes accordingly. In some cases, the flowing probe position is indicated by input (e.g., user input, system input, or software input, etc.).

[0181] The system determines an electrogram feature set (820B) corresponding to multiple signal segments. In some cases, the electrogram feature set includes multiple local cycle lengths corresponding to the multiple signal segments. The multiple local duty cycles can be determined using any of the embodiments described herein. In some cases, the electrogram feature set includes multiple segment confidence values, each segment confidence value corresponding to one of the multiple local cycle lengths. The multiple segment confidence values ​​can be determined using any of the embodiments described herein.

[0182] Next, the system generates a representation (830B) of the set of electrogram characteristics. In some embodiments, this representation is a graphical representation. In one example, the representation is a graphical representation of a histogram, for example, to illustrate the spatial pattern and consistency of the corresponding electrogram characteristics. In another example, the representation is a scatter plot to illustrate the distribution of data points. In one case, the x-axis of the scatter plot is the local period length, and the y-axis of the scatter plot is the local duty cycle. In yet another example, the representation is one or more histograms illustrating the scatter plot. Figure 9E An illustrative example of a graphical representation 900E depicting a set of electrogram characteristics is provided. In the example shown, representation 900E includes a local period length histogram 910E and a scatter plot 920E. In one example, the histogram shows two or more peaks of a corresponding electrogram characteristic, where one of the two or more peaks is of interest, or referred to as the target characteristic. In one case, the target characteristic is associated with a reference conduit. Figure 9E As shown, histogram 910E has two peaks, 912E and 914E, where peak 914E is associated with the target cycle length. In one embodiment, the target characteristic is received from, for example, a different part of an electrophysiological system (e.g., a reference catheter cycle length), another electrophysiological system, or the user.

[0183] The electrophysiological system can also generate cardiac mapping (835B) with superimposed activation waveform data. Figure 9F An illustrative example of a cardiac mapping 900F with an overlay of an activation waveform indicator is depicted. In some cases, the representation of electrogram characteristics is displayed side-by-side with the cardiac mapping. For example, Figure 9E The representation of 900E and superposition has Figure 9F The activation waveforms of the cardiac mapping 900F are displayed side-by-side. The electrophysiological system can receive inputs (840B) of parameters associated with a set of electrogram characteristics, such as regions of interest for local cycle lengths, regions of interest for local duty cycles, and / or regions of interest for confidence values. In one embodiment, the system can receive input of a target characteristic and determine the region of interest based on that input. In some cases, parameter inputs can be received from a user, for example, via a graphical user interface. In some cases, parameter inputs can be received from configuration settings and / or profile settings. In some cases, parameter inputs can be received from a software interface (e.g., an application programming interface, a web service, etc.).

[0184] The electrophysiological system can update the cardiac mapping based on input (845B). In one example, the system can update a cardiac mapping that includes a set of electrogram data based on input. For example, the updated cardiac mapping can be generated using only cardiac electrical signals from selected electrograms within a local cycle length range. Furthermore, the system can use a new set of electrogram data to generate a reprocessed cardiac mapping. Figure 9G Depicting based on Figure 9F An illustrative example of a reprocessed cardiac mapping map (900G) depicted in the image. Comparison. Figure 9F and Figure 9G The selected region 910G shows a different activation waveform indication than the selected region 910F, where Figure 9G It can better identify the spatial patterns of electrical propagation, which are indicated by the activation waveform.

[0185] Various modifications and additions may be made to the exemplary embodiments discussed without departing from the scope of the invention. For example, although the embodiments described above relate to specific features, the scope of the invention also includes embodiments having different combinations of features and embodiments that do not include all the described features. Therefore, the scope of the invention is intended to cover all such alternatives, modifications, and variations, and all equivalents thereof, that fall within the scope of the claims.

Claims

1. A method for processing cardiac information, comprising: Receive activation waveforms, the activation waveforms comprising a set of activation waveform data of multiple signal segments collected at multiple locations; Receives a set of window parameters, including a range of window sizes; For each of the plurality of signal segments A set of confidence values ​​is determined by iterating through each of the multiple window sizes within the stated window size range, where each confidence value corresponds to one of the multiple window sizes, such that for each of the multiple window sizes... Select the position of the central window, which has the dimensions of each window; Calculate a set of correlations, where each correlation in the set is the correlation between the activation waveform in the central window and the activation waveform in a shifted window, the shifted window being a sample window shifted from the central window and having the size of each window; as well as A confidence value is determined from the set of confidence values ​​based on the set of correlations; as well as Compare the confidence values ​​in the set of confidence values ​​to select a specified confidence value and a selected window size corresponding to the specified confidence value; One of a plurality of local period lengths is determined based on the selected window size for each of the plurality of signal segments; as well as Generate a graphical representation of the lengths of the multiple local cycles.

2. The method according to claim 1, wherein, The graphical representation includes a graphical representation of the multiple local cycle lengths superimposed on the cardiac mapping.

3. The method according to claim 1, further comprising: Receive input of parameters representing the multiple local period lengths; as well as The representation of the multiple local period lengths is adjusted based on the input.

4. The method according to claim 1, wherein, The multiple locations are selected based on the input.

5. The method according to claim 4, wherein, The input indicates the probe position in the heart chamber, and wherein the plurality of positions are within a predetermined radius from the probe position.

6. The method according to claim 1, wherein, The correlation set includes a backward correlation set and a forward correlation set, wherein each backward correlation in the backward correlation set is the correlation between the central window and the backward-moved window, wherein the backward-moved window is the central window that has been shifted backward, and each forward correlation in the forward correlation set is the correlation between the central window and the forward-moved window, wherein the forward-moved window is the central window that has been shifted forward.

7. The method according to any one of claims 1-6, further comprising: For each of the plurality of signal segments One of a plurality of local duty cycles is determined based on the activation waveform of a selected central window with a selected window size, wherein the selected central window corresponds to the specified confidence value; and The generation of the graphical representation includes generating graphical representations of the plurality of local period lengths and the plurality of local duty cycles. The graphical representation includes a scatter plot with a first axis and a second axis, wherein the first axis corresponds to the plurality of local period lengths and the second axis corresponds to the plurality of local duty cycles.

8. The method according to claim 7, in, The graphical representation includes the graphical representation of the plurality of local duty cycles superimposed on the cardiac mapping.

9. The method according to claim 8, further comprising: Receive input of parameters representing the plurality of local duty cycles; as well as The representation of the multiple local duty cycles is adjusted based on the input.

10. The method according to claim 1, further comprising: For each of the plurality of signal segments One of the multiple segment confidence values ​​is determined based on the set of confidence values.

11. The method of claim 10, further comprising: Generate a representation of the confidence values ​​for the multiple segments. The graphic representation is at least one of a histogram and a scatter plot of multiple local duty cycles superimposed on a cardiac mapping map.

12. A system for processing cardiac information, the system comprising: The processing unit is configured as follows: Receive activation waveforms, the activation waveforms comprising a set of activation waveform data of multiple signal segments collected at multiple locations; Receives a set of window parameters, including a range of window sizes; For each of the plurality of signal segments A set of confidence values ​​is determined by iterating through each of the multiple window sizes within the stated window size range, where each confidence value corresponds to one of the multiple window sizes, such that... For each of the plurality of window sizes, Select the position of the central window, which has the dimensions of each window; Calculate a set of correlations, where each correlation in the set is the correlation between the activation waveform in the central window and the activation waveform in a shifted window, the shifted window being a sample window shifted from the central window and having the size of each window; as well as A confidence value is determined from the set of confidence values ​​based on the set of correlations; as well as Compare the confidence values ​​in the set of confidence values ​​to select a specified confidence value and a selected window size corresponding to the specified confidence value; as well as One of a plurality of local period lengths is determined based on the selected window size for each of the plurality of signal segments; as well as Generate a graphical representation of the lengths of the multiple local cycles.

13. The system according to claim 12, further comprising: For each of the plurality of signal segments, the processing unit is configured to determine one of a plurality of local duty cycles based on the activation waveform of a selected central window having a selected window size, wherein the selected central window corresponds to a specified confidence value; and The graphical representation includes graphical representations of the plurality of local period lengths and the plurality of local duty cycles. The graphical representation includes a scatter plot with a first axis and a second axis, wherein the first axis corresponds to the plurality of local period lengths and the second axis corresponds to the plurality of local duty cycles.

14. The system according to claim 12 or 13, wherein the processing unit is further configured to: Receives input of parameters representing the multiple local period lengths; and The representation of the multiple local period lengths is adjusted based on the input.

15. The system of claim 14, wherein the processing unit is further configured to receive input of the probe position in the cardiac chamber, and wherein, The plurality of locations are within a predetermined radius from the probe location.

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