Intracardiac monopolar far field cancellation using multiple electrode catheters and methods for creating ECG depth and radial lenses

Abstract

Embodiments of methods and systems for determining electrocardiogram (ECG) depth of electrical activity. Receiving electrical activity from a plurality of electrodes of the catheter; and identifying, within the electrical activity, an electrical signal originating from a depth within the cardiac tissue. In some embodiments, spatial electrode signal analysis of the electrical activity may be performed on each electrode of the plurality of electrodes. In some embodiments, linear and / or non-linear combinations of signal components within the electrical activity may be calculated. In some embodiments, a neural network may be provided with the electrical signal and an estimated distance to the most recent activation may be determined for at least one electrode of the plurality of electrodes. A visualization of the identified electrical signals at a particular depth within the cardiac tissue may be provided for display.

Description

[0001] Related Applications

[0002] This application claims the benefit of and priority to a continuation of U.S. Application No. 18 / 072,793, filed December 1, 2022, entitled “Intracardiac Unipolar Far Field Cancelation Using Multiple ElectrodeCatheters,” which claims priority to U.S. Provisional Application No. 63 / 288,966, filed December 13, 2021; and also claims priority to U.S. Provisional Application No. 63 / 523,976, filed June 29, 2023, entitled “Intracardiac Unipolar Far Field Cancelation Using Multiple ElectrodeCatheters and Methods for Creating an ECG Depth and Radial Lens.” The entirety of each of the above applications is incorporated herein by reference. Technical Field

[0003] The systems and methods herein relate generally to the field of cardiac electrophysiology, and more particularly to electroanatomical mapping systems for isolating intracardiac ECG (IECG) signals at various depths within myocardial tissue. Background Art

[0004] Generally speaking, conventional cardiac mapping is a computer processing operation that produces a three-dimensional map of the heart. Medical professionals can use the three-dimensional map to determine the precise source location of arrhythmias or when performing medical procedures, such as cardiac ablation to treat atrial fibrillation (aFib).

[0005] By way of example, in conventional cardiac mapping, a three-dimensional map of the heart is created when a medical professional (e.g., a doctor) guides a catheter through a patient's blood vessels until the catheter is within the heart. The catheter senses electrical activity, and a computer processing operation analyzes the electrical activity and generates a three-dimensional map of the heart. A problem with conventional cardiac mapping is that far-field signals mask or interfere with local field signals within the electrical activity. In addition, conventional computer processing operations are not suitable for addressing far-field signals. For example, the computer processing operations assume that all far-field signals are the same on all electrodes of the catheter. What is needed is a system and method for extracting and analyzing electrical activity from a catheter while reducing or eliminating interference from the far field.

[0006] Furthermore, intracardiac ECGs collected during cardiac ablation procedures are two-dimensional and reflect electrical activity / voltage only on the surface of intracardiac tissue. A device is needed to identify and visualize ECG signals that reflect electrical conduction below the surface of myocardial tissue (e.g., 0.5mm-10mm depth within cardiac tissue) in three dimensions. Summary of the invention

[0007] Exemplary embodiments, methods, and systems are provided. Methods and systems for performing these methods may be implemented by a mapping engine executed by one or more processors. The method includes receiving electrical activity from a plurality of electrodes of a catheter; and identifying electrical signals originating from a depth within cardiac tissue within the electrical activity. The method may include performing spatial electrode signal analysis of the electrical activity for each of the plurality of electrodes, and performing a linear combination (and in some embodiments, a nonlinear combination) of signal components within the electrical activity. In some embodiments, identifying electrical signals originating from a depth within cardiac tissue may include receiving real-time ECG readings from the plurality of electrodes as input to a neural network, and determining by the neural network an estimated distance to the nearest activation for at least one of the plurality of electrodes. In some embodiments, identifying electrical signals originating from a depth within cardiac tissue may include, for each of the plurality of electrodes, applying a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position. In some embodiments, the method may include visualizing the identified electrical signals at a specified depth within the cardiac tissue on a display. According to one or more embodiments, the exemplary method described above may be implemented as an apparatus, a system, and / or a computer program product.

[0008] In some aspects, the present disclosure relates to a method. The method includes receiving electrical activity from multiple electrodes of a catheter by a mapping engine executed by one or more processors; and identifying electrical signals within the electrical activity that originate from a depth within cardiac tissue. In some specific implementations, the method includes identifying electrical signals within the electrical activity that originate from the endocardial surface of the cardiac tissue. In some specific implementations, the method includes performing spatial electrode signal analysis of the electrical activity on each of the multiple electrodes by the mapping engine; and identifying electrical signals originating from a depth within the cardiac tissue includes calculating a linear combination of signal components within the electrical activity. In another specific implementation, the method includes calculating a linear combination of signal components within the electrical activity by multiplying each signal component within the electrical activity by a corresponding weight determined via a trained model. In another further specific implementation, the method includes identifying electrical signals originating from a depth within the cardiac tissue by calculating a nonlinear combination of the signal components within the electrical activity.

[0009] In some embodiments, the method includes identifying an electrical signal originating from depth within cardiac tissue by providing the electrical activity from the plurality of electrodes as input to a neural network, and determining, via the neural network, an estimated distance from a corresponding electrode to the nearest activation for at least one of the plurality of electrodes.

[0010] In some implementations, the method includes identifying electrical signals originating from a depth within cardiac tissue by applying a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position for each of the plurality of electrodes. In some implementations, the received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes. In some implementations, identifying electrical signals originating from a depth within cardiac tissue includes executing a machine learning model trained on simulated ECG data. In some implementations, the method includes generating, by the mapping engine, a visual display of the identified electrical signals at a specified depth within the cardiac tissue.

[0011] In another aspect, the present disclosure relates to a system comprising: a memory storing software for a mapping engine; and one or more processors configured to execute the mapping engine to: receive electrical activity from multiple electrodes of a catheter, and identify electrical signals within the electrical activity originating from a depth within cardiac tissue.

[0012] In some embodiments, the one or more processors are further configured to identify electrical signals within the electrical activity that originate from the endocardial surface of the cardiac tissue. In some embodiments, the one or more processors are further configured to perform spatial electrode signal analysis of the electrical activity for each of the multiple electrodes; and identifying electrical signals originating from depths within the cardiac tissue includes calculating linear combinations of signal components within the electrical activity. In another embodiment, calculating linear combinations of signal components within the electrical activity includes multiplying each signal component within the electrical activity by a corresponding weight determined via a trained model. In another further embodiment, identifying electrical signals originating from depths within the cardiac tissue includes calculating nonlinear combinations of the signal components within the electrical activity.

[0013] In some embodiments, the one or more processors are further configured to provide the electrical activity from the plurality of electrodes as an input to a neural network, and determine, via the neural network, an estimated distance from the corresponding electrode to the nearest activation for at least one of the plurality of electrodes. In some embodiments, the one or more processors are further configured to apply a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position for each of the plurality of electrodes. In some embodiments, the received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes. In some embodiments, identifying an electrical signal originating from a depth within cardiac tissue includes executing a machine learning model trained on simulated ECG data.

[0014] In another aspect, the present disclosure relates to a method. The method includes receiving electrical activity from a plurality of electrodes (26) of a catheter (14) by a mapping engine (101) executed by one or more processors (61); and identifying within the electrical activity an electrical signal originating from a depth (820) within cardiac tissue (12). In some implementations, the method includes identifying within the electrical activity an electrical signal originating from an endocardial surface of the cardiac tissue (12). In some implementations, the method includes performing, by the mapping engine (101), a spatial electrode signal analysis of the electrical activity on each electrode (26) of the plurality of electrodes (26); and identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes calculating a linear combination of signal components within the electrical activity. In another implementation, calculating the linear combination of signal components within the electrical activity includes multiplying each signal component within the electrical activity by a corresponding weight determined via a trained model. In another further implementation, identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes calculating a non-linear combination of components of the signal within the electrical activity.

[0015] In some implementations, identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes providing the electrical activity from the plurality of electrodes (26) as an input to a neural network, and determining, via the neural network, for at least one of the plurality of electrodes (26), an estimated distance from the corresponding electrode (26) to the nearest activation. In some implementations, identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes, for each electrode (26) of the plurality of electrodes (26), applying a mathematical model and known electrode positions of the plurality of electrodes (26) to calculate an expected signal at each known electrode position.

[0016] In some implementations, the received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes (26). In some implementations, identifying electrical signals originating from a depth within the cardiac tissue includes executing a machine learning model trained on simulated ECG data. In some implementations, the method includes generating, by the mapping engine (101), a visual display (20) of the identified electrical signals at a specified depth within the cardiac tissue (12).

[0017] In another aspect, the present disclosure relates to a system comprising: a memory (62) storing software for a mapping engine (101); and one or more processors (61) configured to execute the mapping engine (101) to: receive electrical activity from a plurality of electrodes (26) of a catheter (14), and identify within the electrical activity an electrical signal originating from a depth (802) within cardiac tissue (12).

[0018] In some embodiments, the one or more processors (61) are further configured to identify within the electrical activity an electrical signal originating from the endocardial surface of the cardiac tissue (12). In some embodiments, the one or more processors (61) are further configured to perform spatial electrode signal analysis of the electrical activity for each electrode (26) of the plurality of electrodes (26); and identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes calculating a linear combination of signal components within the electrical activity. In another embodiment, calculating the linear combination of signal components within the electrical activity includes multiplying each signal component within the electrical activity by a corresponding weight determined via a trained model. In another further embodiment, identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes calculating a nonlinear combination of the signal components within the electrical activity.

[0019] In some embodiments, the one or more processors (61) are further configured to provide the electrical activity from the plurality of electrodes (26) as an input to a neural network, and determine, via the neural network, for at least one electrode in the plurality of electrodes (26), an estimated distance from the corresponding electrode (26) to the nearest activation. In some embodiments, the one or more processors (61) are further configured to apply, for each electrode (26) in the plurality of electrodes (26), a mathematical model and known electrode positions of the plurality of electrodes (26) to calculate an expected signal at each known electrode position.

[0020] In some implementations, the received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes (26). In some implementations, identifying electrical signals originating from a depth within the cardiac tissue includes executing a machine learning model trained on simulated ECG data. In some implementations, the one or more processors (61) are further configured to generate a visual display (20) of the identified electrical signals at a specified depth within the cardiac tissue (12). BRIEF DESCRIPTION OF THE DRAWINGS

[0021] A more detailed understanding may be obtained from the following detailed description provided by way of example in conjunction with the accompanying drawings, in which like reference numerals indicate like elements, and in which:

[0022] Figure 1 Diagrams showing example systems that may implement one or more features of the disclosed subject matter according to one or more embodiments;

[0023] Figure 2 A diagram showing a system according to one or more embodiments is shown;

[0024] Figure 3A A method according to one or more embodiments is shown;

[0025] Figure 3B shows a graph according to one or more embodiments;

[0026] Figure 4 A method according to one or more embodiments is shown;

[0027] Figure 5 depicts an exemplary catheter according to one or more embodiments;

[0028] Figure 6 An exemplary catheter according to one or more embodiments is shown;

[0029] Figure 7 Described are methods according to one or more embodiments;

[0030] Figure 8 Depicting an exemplary catheter of an example system configured to identify electrical activation signals at different distances from an electrode location in accordance with one or more embodiments;

[0031] Fig. 9 An example method for isolating electrical signals at a distance (e.g., depth) within cardiac tissue using ECG information collected from a multi-electrode mapping catheter is shown in accordance with one or more embodiments;

[0032] Fig.10An alternative example method for isolating electrical signals at a distance (e.g., depth) within cardiac tissue using ECG information collected from a multi-electrode mapping catheter is shown in accordance with one or more embodiments; and

[0033] Fig.11 Another alternative example method for isolating electrical signals at a distance (eg, depth) within cardiac tissue using ECG information collected from a multi-electrode mapping catheter in accordance with one or more embodiments is shown. DETAILED DESCRIPTION

[0034] The systems and methods herein generally relate to signal processing. According to one or more embodiments, the systems and methods herein provide intracardiac unipolar far-field reduction or elimination using multiple electrode catheters. As an example, the systems and methods herein are described with respect to a mapping engine.

[0035] The mapping engine can be implemented as a processor executable code or software, which must be rooted in the process operations performed by the medical device equipment and in the processing hardware of the medical device equipment. For ease of explanation, the mapping engine is described herein with respect to mapping the heart; however, any anatomical structure, body part, organ or part thereof may be a target for mapping by the mapping engine described herein. According to one or more embodiments, the mapping engine eliminates the conventional cardiac mapping assumption that all far-field signals are the same on all electrodes. Compared to conventional cardiac mapping, the mapping engine intelligently implements the new assumption that far-field signals are similar but not identical. Then, the mapping engine weights the electrodes in an inversely proportional manner to identify a common signal component across the electrodes. The common signal component is used by the mapping engine to accurately reduce or eliminate far-field interference. The reduction or elimination of far-field interference by the mapping engine produces a cleaner near-field signal for improved cardiac mapping. Additionally or alternatively, in some embodiments, using information generated by weighting the electrodes inversely proportional to distance, the mapping engine can identify signals emitted at specific distances from the electrodes, including at depths within myocardial tissue.

[0036] One or more advantages, technical effects, and / or benefits of the mapping engine may include filtering out far-field interference and estimating pure local activity from unipolar signals (e.g., particularly in very complex mapping situations), which replace the need for bipolar signals, which are known for their shortcomings (such as electrode distance and orientation distortion). Thus, by specifically utilizing and transforming unipolar signals, the mapping engine doubles the number of electrodes analyzed to obtain or increase spatial behavioral resolution. Additionally, in the case of late activation, the mapping engine may also remove persistent aFib near-far field signals to enhance aFib target detection.

[0037] Additional advantages, technical effects and / or benefits include enabling an electroanatomical mapping system (interchangeably referred to herein as a "cardiac mapping" system) to identify and / or visualize in three dimensions electrical activity within myocardial tissue below the inner surface of the heart (e.g., a depth of 0.5 mm-10 mm within the cardiac tissue). Furthermore, by identifying electrical activity based on depth and location within the myocardial tissue, corresponding ECG information can be presented to a user of the electroanatomical mapping system in a variety of ways, including but not limited to constructed layers or "shell" surfaces of an electrical map (also referred to as a "map") at different depths of an electroanatomical model and / or enabling a physician or system operator to view ECG waveforms at selectable depths within the tissue. The novel capabilities enabled by the methods and systems described herein that allow identification, isolation and / or visualization of electrical activity within myocardial tissue can be described as creating an ECG depth and radial lens.

[0038] Figure 1 1 is a diagram of an example system (e.g., a medical device equipped and / or catheter-based electrophysiological mapping and ablation) shown as system 10, in which one or more features of the subject matter of this article can be implemented according to one or more embodiments. All or part of system 10 can be used to collect information (e.g., biometric data and / or training data sets) and / or to implement mapping engine 101, as described herein. As shown, system 10 includes recorder 11, heart 12, catheter 14, model or anatomical map 20, electrogram 21, spline 22, patient 23, physician 24 (or medical professional or clinician), location pad 25, electrode 26, display device 27, distal tip 28, sensor 29, coil 32, patient interface unit (PIU) 30, electrode skin patch 38, ablation energy generator 50 and workstation 55 (including at least one processor 61 and at least one memory 62 in which mapping engine 101 is stored). It should be noted that each element and / or item of system 10 represents one or more of the element and / or item. Figure 1 The example of the system 10 shown can be modified to implement the embodiments disclosed herein. The embodiments disclosed in the present invention can be similarly applied using other system components and settings. In addition, the system 10 can include additional components, such as elements for sensing electrical activity and / or physiological signals, wired or wireless connectors, processing and display devices, etc.

[0039] The system 10 includes a plurality of catheters 14 that are inserted percutaneously through the vascular system of a patient 23 into a chamber or vascular structure of a heart 12 by a physician 24. Typically, a delivery sheath catheter (which is an example of a catheter 14) is inserted into the left atrium or right atrium near a desired location in the heart 12. Multiple catheters 14 may then be inserted into the delivery sheath catheter in order to reach the desired location. The plurality of catheters 14 may include a catheter dedicated to sensing intracardiac electrogram (IEGM) signals, a catheter dedicated to ablation, and / or a catheter dedicated to both sensing and ablation. An example catheter 14 configured for sensing IEGM is shown herein. The physician 24 brings the distal tip 28 of the catheter 14 into contact with the heart wall for sensing a target site in the heart 12. For ablation, the physician 24 would similarly bring the distal end of the ablation catheter to the target site for ablation.

[0040] The catheter 14 is an exemplary catheter that includes one (preferably multiple) electrodes 26 that are optionally distributed on a plurality of splines 22 at a distal tip 28 and configured to sense IEGM signals. In addition, the catheter 14 may also include a sensor 29 that is embedded in or near the distal tip 28 for tracking the position and orientation of the distal tip 28. Optionally and preferably, the position sensor 29 is a magnetic-based position sensor that includes three magnetic coils for sensing three-dimensional (3D) position and orientation.

[0041] The sensor 29 (e.g., a position-based or magnetic sensor) can operate with the position pad 25, which includes a plurality of magnetic coils 32 configured to generate a magnetic field in a predefined working space. The real-time position of the distal tip 28 of the catheter 14 can be tracked based on the magnetic field generated by the position pad 25 and sensed by the sensor 29. Details of magnetic-based position sensing technology are described in U.S. Patents 5,5391,199, 5,443,489, 5,558,091, 6,172,499, 6,239,724, 6,332,089, 6,484,118, 6,618,612, 6,690,963, 6,788,967, and 6,892,091.

[0042] The system 10 includes one or more electrode patches 38 positioned in contact with the skin of the patient 23 to establish a position reference for impedance-based tracking of the location pad 25 and the electrodes 26. For impedance-based tracking, current is directed toward the electrodes 26 and sensed at the patches 38 (e.g., electrode skin patches) so that the position of each electrode can be triangulated via the patches 38. Details of impedance-based position tracking techniques are described in U.S. Patents 7,536,218, 7,756,576, 7,848,787, 7,869,865, and 8,456,182, which are incorporated herein by reference.

[0043] Recorder 11 displays electrograms 21 captured with electrodes 18 (eg, surface electrocardiogram (ECG) electrodes) and IEGMs captured with electrodes 26 of catheter 14. Recorder 11 may include pacing capabilities for pacing the cardiac rhythm and / or may be electrically connected to a separate pacemaker.

[0044] The system 10 may include an ablation energy generator 50 adapted to conduct ablation energy to one or more of the electrodes 26 at the distal tip 28 of the catheter 14 configured for ablation. The energy generated by the ablation energy generator 50 may include, but is not limited to, radio frequency (RF) energy or pulsed field ablation (PFA) energy (including unipolar or bipolar high voltage DC pulses that can be used to achieve irreversible electroporation (IRE)), or a combination thereof. The PIU 30 is an interface configured to establish electrical connectivity between the catheter, electrophysiology equipment, a power source, and a workstation 55 for controlling the operation of the system 10. The electrophysiology equipment of the system 10 may include, for example, a plurality of catheters 14, a location pad 25, a surface ECG electrode 18, an electrode patch 38, an ablation energy generator 50, and a recorder 11. Optionally and preferably, the PIU 30 further includes processing capabilities for achieving real-time calculation of the position of the catheter and for performing ECG calculations.

[0045] The workstation 55 includes a memory 62, a processor unit 61 having the memory 62 or a storage device having appropriate operating software loaded therein, and user interface capabilities, as further described herein. The workstation 55 may provide multiple functions, optionally including: (1) three-dimensional (3D) modeling of the endocardial anatomy and rendering the model or anatomical map 20 for display on the display device 27; (2) displaying on the display device 27 an activation sequence (or other data) compiled from recorded electrograms 21 as representative visual markers or images superimposed on the rendered anatomical map 20; (3) displaying the real-time position and orientation of multiple catheters within the heart chamber; and (5) displaying on the display device 27 a site of interest (such as where ablation energy has been applied). A commercial product embodying elements of the system 10 may be The 3 system was purchased from Biosense Webster, Inc., 31A Technology Drive, Irvine, CA 92618.

[0046] The system 10 can be used to detect, diagnose and / or treat cardiac conditions (e.g., using the mapping engine 101). Cardiac conditions such as arrhythmias continue to be common and dangerous medical conditions, especially in the elderly. For example, the system 10 can be a surgical system (e.g., sold by Biosense Webster). The present invention is part of a surgical system that is configured to obtain biometric data (e.g., anatomical measurements and electrical measurements of a patient's organ such as the heart 12, and as described herein) and to perform cardiac ablation procedures. More specifically, treatment of cardiac conditions such as arrhythmias often requires obtaining detailed mapping of cardiac tissue, chambers, veins, arteries, and / or electrical pathways. For example, a prerequisite for successfully performing catheter ablation (as described herein) is that the cause of the arrhythmia is accurately located in a chamber of the heart 12. Such localization can be accomplished via an electrophysiological study during which electrical potentials are spatially resolved using a mapping catheter (e.g., catheter 14) introduced into a chamber of the heart 12. The electrophysiological study (so-called electroanatomical mapping) thus provides 3D mapping data that can be displayed on the display device 27. In many cases, the mapping function and the therapeutic function (e.g., ablation) are provided by a single catheter or a group of catheters, such that the mapping catheter also simultaneously operates as a therapeutic (e.g., ablation) catheter. According to one or more embodiments, the mapping engine 101 may be directly stored and executed by the catheter 14. According to one or more embodiments, the mapping engine 101 performs an electrocardiogram and / or intracardiac electrogram (ECG / ICEG). ECG / ICEG is a process of decomposing / analyzing / recording the electrical activity of the heart 12 over a period of time using physiological signals from multiple electrodes 18 and 26 (e.g., with ICEG, at least one of the electrodes 26 is within the heart 12). That is, in order to support the system 100 in detecting, diagnosing and / or treating cardiac conditions, one or more catheters 14 may be navigated into the heart 12 of the patient 23 by the physician 24. In turn, the multiple electrodes 18 and 26 detect and provide signals (also referred to as physiological signals), which the mapping engine 101 uses to identify small electrical changes caused by the electrophysiological pattern of depolarization of the myocardium during each heartbeat. For example, an ECG / ICEG may be performed over a period of time (such as ten (10) seconds). In this way, the overall magnitude and direction of the electrical depolarization at the heart 12 is captured at each moment throughout the cardiac cycle. ECG / ICEG can be recorded during a diagnostic or therapeutic procedure. The duration of the procedure can vary from tens of minutes to several hours. During each therapeutic procedure, there are typically dozens of ablation sessions, for example, each ablation session lasting from a few seconds to up to about 1 minute.

[0047] According to one or more embodiments, the mapping engine 101 may include and execute one or more algorithms, such as algorithms that implement mathematical multi-signal decomposition operations or mathematical dimensionality reduction techniques. The mapping engine 101 reduces the need for wavefront algorithms because the mapping engine 101 provides accurate near-field components, activation times (e.g., using minimum derivatives), and voltage levels (e.g., maximum-minimum). Examples of one or more algorithms include (but are not limited to) singular value decomposition (SVD), principal component analysis (PCA), and / or other matrix decompositions or factorizations.

[0048] According to one or more additional embodiments, the mapping engine 101 may also utilize information generated by a mathematical dimensionality reduction algorithm (such as an SVD or PCA algorithm) to identify electrical signals originating from an estimated depth within the cardiac tissue. When the mapping engine 101 generates orthogonal signals generated by SVD or PCA, each orthogonal signal component may correspond to electrical activity originating from a specific distance or depth relative to a specific electrode. A linear combination of the signal components is applied, where the weight assigned to each orthogonal signal determines its contribution to the final composite signal. Optionally, a nonlinear combination of the signal components may also be performed to take into account signal degradation or attenuation that depends on the distance from the electrode. The weights applied to each signal can be adjusted in a variety of ways, including by employing deep learning or other artificial intelligence (AI) or machine learning (ML) algorithms. By considering the spatial information contained in the orthogonal signals and manipulating their contributions through mathematical combinations (linear or nonlinear), the system can distinguish and extract electrical signals originating from various distances around a specific electrode (including at depths within the myocardial tissue).

[0049] Alternatively, in some embodiments, a deep learning or machine learning model may be employed independently without using a mathematical dimensional algorithm. In such embodiments, the ECG signal from the electrode of the mapping catheter may be input to a trained model, which in turn directly processes the ECG signal to identify patterns or features associated with a specific distance or depth from the electrode, consistent with its training based on electrical mapping and corresponding information about electrical propagation within the depth of the tissue.

[0050] According to one or more alternative embodiments, the mapping engine 101 may implement a neural network configured to identify intracardiac ECG signals representing electrical activity at different depths within the myocardial tissue. A trained machine learning model (specifically a neural network) can be used to estimate the distance to the nearest activation within the myocardial tissue based on the ECG readings. The model can be trained using simulation data that includes various scenarios, such as different catheter positions, transmembrane potential positions, and myocardial tissue activation states (e.g., healthy, scarred, fibrotic, etc.). When enabled as part of a cardiac mapping system 10 (such as part of the mapping engine 101), the model can receive input ECG signals from electrodes of a multi-electrode catheter to generate an output in the form of an identified electrical signal at a specified distance or depth within the myocardial tissue.

[0051] In yet another alternative embodiment, the mapping engine 101 may implement models, field projection calculations, and optimization and inverse problem solutions to identify ECG signals representing electrical activity at different depths within the myocardial tissue. In this embodiment, the electrical activity of the cardiac tissue is represented as a collection of sources (such as dipoles) distributed on the surface of the heart chambers and in the depth of the tissue. Each such dipole has an activation time and all dipoles work synchronously to generate activation wave propagation. This will have a field projection at the location of the electrode, which will be compared with the measurement from the electrode. A mathematical minimization problem can be solved to estimate the distance from the electrode to the nearest activation, which finds the time and distance at which a grid of dipoles that maximally resamples the measurement is activated. In order to estimate the electrical activity at a specific location within the myocardial tissue, a field projection is calculated based on the location of the electrode on the mapping catheter.

[0052] Therefore, in some embodiments, the mapping engine 101 may include hardware, software, or a combination of hardware and software for implementing a neural network, a classifier, a machine learning engine, or a similar model. For example, the mapping engine 101 may include software executed by one or more processors of a device or system, such as a central processing unit, a graphics processing unit, a tensor processing unit, a vector processing unit, or other types of processors or coprocessors. In many specific implementations, the mapping engine 101 may be executed by multiple computing devices (e.g., as a cluster of appliances, a cloud of virtual computing devices, a server farm, etc.). In many such specific implementations, the functions of the mapping engine 101 may be distributed across multiple processors and / or computing devices. For example, in some specific implementations, a first estimate of electrical activity at a first location may be performed by a first processor and / or a first physical or virtual computing device; and a second estimate of electrical activity at the same first location (e.g., at different times) or a different second location (e.g., at the same time or at different times) may be performed by a second processor and / or a second physical or virtual computing device. This can achieve scalability and low-latency generation of results for even very complex estimates.

[0053] In a patient with normal sinus rhythm (NSR) (e.g., patient 23), the heart (e.g., heart 12) including the atria, ventricles, and excitatory conduction tissue is electrically stimulated to beat in a synchronized patterned manner. Note that this electrical stimulation can be detected as intracardiac electrocardiogram (IC ECG) data, etc.

[0054] According to one or more embodiments, in a patient (e.g., patient 23) suffering from an arrhythmia (e.g., aFib), an abnormal region of cardiac tissue does not follow the synchronous beating cycle associated with normal conducting tissue, in contrast to a patient suffering from NSR. Instead, the abnormal region of cardiac tissue abnormally conducts to adjacent tissue, disrupting the cardiac cycle into an asynchronous rhythm. It is noted that this asynchronous rhythm may also be detected as IC ECG data. Such abnormal conduction has previously been known to occur in various regions of the heart 12, such as in the sinoatrial (SA) node region, along the conduction pathway of the atrioventricular (AV) node, or in the myocardial tissue that forms the walls of the ventricular and atrial heart chambers. There are other conditions (such as fibrillation) in which the pattern of abnormal conducting tissue results in a reentrant pathway that causes the chambers to beat in a regular pattern that may be a multiple of the sinus rhythm.

[0055] By way of example, to support the system 10 in detecting, diagnosing and / or treating a cardiac condition, the catheter 14 may be navigated by a physician 24 into the heart 12 of a patient 23 lying on a bed. For example, the physician 24 may insert the shaft through the sheath while manipulating the distal end of the shaft using a manipulator near the proximal end of the catheter 14 and / or deflecting from the sheath. According to one or more embodiments, the catheter 14 may be mounted at the distal end of the shaft. The catheter 14 may be inserted through the sheath in a collapsed state and then may be deployed within the heart 12.

[0056] Generally, a catheter 14 containing an electrical sensor (e.g., sensor 29) at or near its distal tip (e.g., at least one electrode 26) can be advanced to a point in the heart 12, contacting the tissue with the sensor, and acquiring data at that point to measure electrical activity at that point in the heart 12. One disadvantage of using a type of catheter containing only a single distal tip electrode to map a heart chamber is the time required to accumulate data point by point at the necessary number of points required for a detailed map of the chamber as a whole. Therefore, multi-electrode catheters (e.g., catheter 14) have been developed to simultaneously measure electrical activity at multiple points in a heart chamber.

[0057] The catheter 14, which may include at least one electrode 26 and a catheter needle coupled to the body thereof, may be configured to obtain biometric data, such as electrical signals of an organ in the body (e.g., the heart 12), and / or ablate a tissue region thereof (e.g., a cardiac chamber of the heart 12). Note that the electrode 26 represents any similar element, such as a tracking coil, a piezoelectric transducer, an electrode, or a combination of elements configured to ablate a tissue region or obtain biometric data. According to one or more embodiments, the catheter 14 may include one or more positioning sensors for determining trajectory information. The trajectory information may be used to infer motion characteristics, such as the contractility of the tissue.

[0058] Biometric data (e.g., patient biometrics, patient data, or patient biometric data) may include one or more of local activation time (LAT), electrical activity, topology, bipolar mapping, reference activity, ventricular activity, dominant frequency, impedance, and the like. The LAT may be a time point corresponding to a threshold activity of local activation calculated based on a normalized initial starting point. The electrical activity may be any applicable electrical signal that may be measured based on one or more thresholds and may be sensed and / or enhanced based on a signal-to-noise ratio and / or other filters. The topology may correspond to a physical structure of a body part or a portion of a body part, and may correspond to changes in the physical structure relative to different portions of a body part or relative to different body parts. The dominant frequency may be a frequency or frequency range that is prevalent at a portion of a body part, and may be different in different portions of the same body part. For example, the dominant frequency of the PV of a heart may be different from the dominant frequency of the right atrium of the same heart. Impedance may be a resistance measurement at a given area of ​​a body part.

[0059] Examples of biometric data include, but are not limited to, patient identification data, IC ECG data, bipolar intracardiac reference signals, anatomical and electrical measurements, trajectory information, body surface (BS) ECG data, historical data, brain biometrics, blood pressure data, ultrasound signals, radio signals, audio signals, two-dimensional or three-dimensional (3D) image data, blood glucose data, and temperature data or other electrical activity and / or physiological signals. Biometric data can generally be used to monitor, diagnose, and treat any number of various diseases, such as cardiovascular diseases (e.g., arrhythmias, cardiomyopathy, and coronary artery disease) and autoimmune diseases (e.g., type I and type II diabetes). It should be noted that BS ECG data may include data and signals collected from electrodes on the surface of the patient, IC ECG data may include data and signals collected from electrodes inside the patient, and ablation data may include data and signals collected from tissue that has been ablated. In addition, BS ECG data, IC ECG data, and ablation data, along with catheter electrode positioning data, may be derived from one or more procedure records.

[0060] For example, catheter 14 may use electrodes 26 to enable intravascular ultrasound and / or MRI catheterization to image (e.g., obtain and process biometric data) heart 12. Catheter 14 is shown in an enlarged view inside a heart chamber of heart 12. It should be understood that any shape including one or more electrodes 26 may be used to implement the embodiments disclosed herein.

[0061] Examples of catheter 14 include, but are not limited to, a linear catheter with multiple electrodes, a balloon catheter including electrodes dispersed on multiple ridges that shape the balloon, a lasso, a catheter with electrodes in a grid shape, or a circular catheter with multiple electrodes, a high-density catheter, or any other applicable shape or complexity. The linear catheter may be fully or partially elastic so that it can twist, bend, and / or otherwise change its shape based on received signals and / or based on the application of external forces (e.g., cardiac tissue) on the linear catheter. The balloon catheter may be designed so that when deployed into the patient's body, its electrodes can remain in close contact against the endocardial surface. For example, the balloon catheter can be inserted into an inner cavity such as a pulmonary vein (PV). The balloon catheter can be inserted into the PV in a deflated state so that the balloon catheter does not occupy its maximum volume when inserted into the PV. The balloon catheter can be expanded inside the PV so that those electrodes on the balloon catheter are in contact with the entire circular segment of the PV. Such contact with the entire circular segment of the PV or any other inner cavity can achieve effective imaging and / or ablation. Other examples of catheter 14 include Catheter and Constellation Catheter. Also see Figures 5 and 6 An example of a catheter 14 is described, as will be described in more detail below.

[0062] According to other examples, body patches and / or body surface electrodes (e.g., one or more electrode patches 38) may also be positioned on or near the body of the patient 23. The catheter 14 having one or more electrodes 26 may be positioned within the body (e.g., within the heart 12), and the positioning of the catheter 14 may be determined by the system 100 based on signals transmitted and received between the one or more electrodes 26 of the catheter 14 and the body patches and / or body surface electrodes. In addition, the electrodes 26 may sense biometric data from within the body of the patient 23 (such as within the heart 12) (e.g., the electrodes 26 sense the electrical potential of the tissue in real time). The biometric data may be associated with the determined positioning of the catheter 14, so that a rendering of a body part of the patient (e.g., the heart 12) may be displayed, and the biometric data may be displayed overlaid on the shape of the body part.

[0063] By way of another example, the catheter 14 and other items of the system 10 may be connected to a workstation 55. The workstation 55 may include any computing device that employs an ML / AI algorithm (which may be included in the mapping engine 101). According to an exemplary embodiment, the workstation 55 includes one or more processors 61 (any computing hardware) and a memory 62 (any non-transitory tangible medium), wherein the one or more processors 61 execute computer instructions relative to the mapping engine 101, and the memory 62 stores these instructions for execution by the one or more processors 61. For example, the workstation 55 may be configured to receive and process biometric data and determine whether a given tissue region is conductive. In some embodiments, the workstation 55 may be further programmed by the mapping engine 101 (in software) to perform the functions of the ablation procedure guidance method. For example, the ablation procedure guidance method may include receiving an input (e.g., including one or more images and conduction velocity vector estimates), generating a digital twin of an anatomical structure using the image and conduction velocity vector estimates, and presenting the digital twin to provide accurate ablation guidance of the anatomical structure and provide electrophysiological information of the anatomical structure.

[0064] According to one or more embodiments, the mapping engine 101 can be external to the workstation 55 and can be located, for example, in the catheter 14, in an external device, in a mobile device, in a cloud-based device, or can be a separate processor. In this regard, the mapping engine 101 can be transmitted / downloaded in electronic form over a network.

[0065] In one example, the workstation 55 can be any computing device (such as a general purpose computer) as described herein including software (e.g., mapping engine 101) and / or hardware (e.g., processor 61 and memory 62) with suitable front-end and interface circuitry for transmitting signals to and receiving signals from the catheter 14, as well as for controlling other components of the system 10. For example, the front-end and interface circuitry include an input / output (I / O) communication interface that enables the workstation 55 to receive signals from and / or transmit signals to at least one electrode 26. The workstation 55 may include real-time noise reduction circuitry, typically configured as a field programmable gate array (FPGA), followed by an analog-to-digital (A / D) ECG or electrocardiograph or electromyogram (EMG) signal conversion integrated circuit. The workstation 55 may pass signals from the A / D ECG or EMG circuitry to another processor and / or may be programmed to perform one or more functions disclosed herein.

[0066] The display device 27 is connected to the workstation 55, which can be any electronic device for visual presentation of biometric data. According to an exemplary embodiment, during a procedure, the workstation 55 can facilitate presenting a body part rendering to the physician 24 on the display device 27, and storing data representing the body part rendering in the memory 62. For example, a map depicting motion characteristics can be rendered / constructed based on trajectory information sampled at a sufficient number of points in the heart 12. As an example, the display device 27 can include a touch screen that can be configured to accept input from the physician 24 in addition to presenting the body part rendering.

[0067] In some embodiments, the physician 24 can use one or more input devices (such as a touchpad, a mouse, a keyboard, a gesture recognition device, etc.) to manipulate the elements and / or body part rendering of the system 10. For example, the input device can be used to change the positioning of the catheter 14 so that the rendering is updated. It should be noted that the display device 27 can be located in the same location or a remote location, such as a separate hospital or a separate healthcare provider network. According to one or more embodiments, the system 10 can also use ultrasound, computed tomography (CT), MRI or other medical imaging techniques using catheters 14 or other medical equipment to obtain biometric data. For example, the system 10 can use one or more catheters 14 or other sensors to obtain ECG data and / or anatomical and electrical measurements (e.g., biometric data) of the heart 12. More specifically, the workstation 55 can be connected to the BS electrode via a cable, and the BS electrode includes an adhesive skin patch attached to the patient 23. The BS electrode obtains / generates biometric data in the form of BS ECG data. For example, the processor 61 determines the positioning coordinates of the catheter 14 in the body part (e.g., heart 12) of the patient 23. These location coordinates are based on the impedance or electromagnetic field measured between the body surface electrodes and the electrodes 26 or other electromagnetic components of the catheter 14. Additionally or alternatively, a location pad that generates a magnetic field for navigation may be located on the surface of the bed (or table) and may be detachable from the bed. The biometric data may be transmitted to the workstation 55 and stored in the memory 62. Alternatively or in addition, the biometric data is transmitted to a server, which may be local or remote, using a network as further described herein.

[0068] According to one or more embodiments, the catheter 14 may be configured to ablate a tissue region of a cardiac chamber of the heart 12. For example, the catheter 14 is shown in an enlarged view within a cardiac chamber of the heart 12. In addition, ablation electrodes such as at least one electrode 26 are configured to provide energy to a tissue region of an organ (e.g., the heart 12) within the body. The energy may be thermal energy and may cause damage to the tissue region starting from the surface of the tissue region and extending into the thickness of the tissue region. Biometric data relative to an ablation procedure (e.g., ablated tissue, ablation location, etc.) may be considered as ablation data.

[0069] According to one example, with respect to obtaining biometric data, a multi-electrode catheter (e.g., catheter 14) is advanced into a chamber of the heart 12. Anterior-posterior fluorograms (AP) and lateral fluorograms may be obtained to establish the positioning and orientation of each electrode. An ECG may be recorded from each of the electrodes 26 in contact with the surface of the heart relative to a time reference (such as the start of a P wave in sinus rhythm from a BS ECG and / or a signal from an electrode 26 of a catheter 14 placed in the coronary sinus). As further disclosed herein, the system may distinguish between those electrodes that record electrical activity and those electrodes that do not record electrical activity due to not being in close proximity to the endocardial wall. After recording the initial ECG, the catheter is repositioned and the fluorogram and ECG may be recorded again. An electrical mapping map (e.g., via cardiac mapping) may then be constructed according to iterations of the above process.

[0070] Cardiac mapping can be accomplished using one or more techniques. Generally speaking, mapping of cardiac regions (such as cardiac areas, tissues, veins, arteries, and / or electrical pathways of the heart 12) can enable identification of problem areas (such as scar tissue), sources of arrhythmias (e.g., electrical rotors), healthy areas, and the like. The cardiac region is mapped so that a visual rendering of the mapped cardiac region is provided using a display, as further disclosed herein. In addition, cardiac mapping (which is an example of cardiac imaging) may include mapping based on one or more modalities, such as, but not limited to, LAT, local activation velocity, electrical activity, topology, bipolar maps, dominant frequency, or impedance. A catheter (e.g., catheter 14) inserted into the patient's body can be used to capture data corresponding to multiple modalities (e.g., biometric data), and the data can be provided for rendering simultaneously or at different times based on corresponding settings and / or preferences of the physician 24.

[0071] As an example of a first technique, cardiac mapping may be achieved by sensing electrical properties of cardiac tissue (e.g., LAT) according to precise locations within the heart 12. Corresponding data (e.g., biometric data) may be acquired by one or more catheters (e.g., catheter 14) that are advanced into the heart 12 and have electrical sensors and position sensors (e.g., electrodes 26) in their distal tips. As a specific example, position and electrical activity may initially be measured at about 10 to about 20 points on the inner surface of the heart 12. These data points may generally be sufficient to generate a preliminary reconstruction or map of satisfactory quality of the heart surface. The preliminary map may be combined with data taken from additional points to produce a more comprehensive map of cardiac electrical activity. In a clinical setting, it is not uncommon to accumulate data at 100 or more sites (e.g., several thousand) to generate a detailed and comprehensive map of cardiac chamber electrical activity. The generated detailed map may then serve as a basis for deciding on a course of therapeutic action, such as tissue ablation as described herein, to alter the propagation of cardiac electrical activity and restore normal heart rhythm.

[0072] In addition, cardiac mapping is generated based on the detection of an intracardiac potential field (e.g., an example of an IC ECG data and / or a bipolar intracardiac reference signal). A non-contact technique for simultaneously acquiring a large amount of cardiac electrical information can be implemented. For example, a catheter type having a distal end portion may be provided with a series of sensor electrodes distributed on its surface and connected to an insulated electrical conductor for connection to a signal sensing and processing device. The size and shape of the end portion may be such that the electrode is substantially spaced apart from the wall of the heart chamber. The intracardiac potential field may be detected during a single heartbeat. According to one example, the sensor electrodes may be distributed on a series of circumferences located in planes spaced apart from each other. These planes may be perpendicular to the long axis of the end portion of the catheter. At least two additional electrodes may be provided adjacently at the end of the long axis of the end. As a more specific example, the catheter may include four circumferences, wherein eight electrodes are equiangularly spaced apart on each circumference. Therefore, in this specific implementation, the catheter may include at least 34 electrodes (32 circumferential electrodes and 2 end electrodes). As another more specific example, the catheter may include other multi-spline catheters, such as five soft flexible branches, eight radial splines, or a parallel spline spatula type (eg, any of which may have a total of 42 electrodes).

[0073] As an example of electrical mapping or cardiac mapping, an electrophysiological cardiac mapping system and technique based on a non-contact and non-expanding multi-electrode catheter (e.g., catheter 14) can be implemented. ECG can be obtained using one or more catheters 14 having multiple electrodes (e.g., such as between 42 and 122 electrodes). According to this specific implementation, knowledge of the relative geometry of the probe and the endocardium can be obtained by an independent imaging modality such as transesophageal echocardiography. After independent imaging, the non-contact electrodes can be used to measure the cardiac surface potential and construct a map therefrom (e.g., in some cases, using a bipolar intracardiac reference signal). The technique may include the following steps (after the independent imaging step): (a) measuring the potential using multiple electrodes disposed on a probe positioned in the heart 12; (b) determining the geometric relationship between the probe surface and the endocardial surface and / or other references; (c) generating a coefficient matrix representing the geometric relationship between the probe surface and the endocardial surface; and (d) determining the endocardial potential based on the electrode potential and the coefficient matrix.

[0074] According to another example of electrical mapping or cardiac mapping, techniques and apparatus for mapping the electrical potential distribution of a cardiac chamber may be implemented. An intracardiac multi-electrode mapping catheter assembly is inserted into the heart 12. The mapping catheter (e.g., catheter 14) assembly preferably includes a multi-electrode array or a companion reference catheter having one or more integral reference electrodes (e.g., one or more electrodes 26).

[0075] According to one or more embodiments, the electrodes can be deployed in a substantially spherical array that can be spatially referenced to a point on the endocardial surface by a reference electrode or by a reference catheter in contact with the endocardial surface. A preferred electrode array catheter can carry a plurality of individual electrode sites (e.g., at least 24). In addition, the example technique can be implemented by knowing the location of each of the electrode sites on the array and knowing the geometry of the heart. These locations are preferably determined by the technique of impedance plethysmography.

[0076] In view of electrical mapping or cardiac mapping and according to another example, the catheter 14 is a cardiac mapping catheter assembly, which includes an electrode array defining a plurality of electrode sites. The cardiac mapping catheter assembly also includes an inner cavity to receive a reference catheter having a distal tip electrode assembly, which is used to detect the heart wall. The cardiac mapping catheter assembly includes a braid of insulated wires (e.g., having 24 to 64 wires in the braid), and each wire can be used to form an electrode site. The cardiac mapping catheter assembly is easily positioned in the heart 12 for obtaining electrical activity information from a first set of non-contact electrode sites and / or a second set of contact electrode sites.

[0077] In addition, according to another example, the catheter 14 that can implement mapping of electrophysiological activity in the heart includes a distal tip suitable for delivering stimulation pulses for cardiac pacing or an ablation electrode for ablating tissue contacted with the tip. The catheter 14 may also include at least one pair of orthogonal electrodes to generate a difference signal indicating local cardiac electrical activity adjacent to the orthogonal electrodes.

[0078] As described herein, the system 10 is used to detect, diagnose and / or treat cardiac conditions. In an example operation, a process for measuring electrophysiological data in a heart chamber is implemented by the system 10. The process includes, in part, positioning a set of active electrodes and passive electrodes into the heart 12, supplying current to the active electrodes, thereby generating an electric field in the heart chamber, and measuring the electric field at the passive electrode sites. The passive electrodes are contained in an array positioned on an inflatable balloon of a balloon catheter. In a preferred embodiment, the array is said to have from 60 to 64 electrodes.

[0079] As another example operation, cardiac mapping is implemented by the system 10 using one or more ultrasound transducers. The ultrasound transducer may be inserted into the patient's heart 12, and multiple ultrasound slices (e.g., two-dimensional or 3D slices) may be collected at various locations and orientations within the heart 12. The location and orientation of a given ultrasound transducer are known, and the collected ultrasound slices are stored so that they can be displayed at a later time. One or more ultrasound slices corresponding to the positioning of a catheter 14 (e.g., a treatment catheter) are displayed after a period of time, and the catheter 14 is overlaid on the one or more ultrasound slices.

[0080] In view of system 10, it is noted that arrhythmias, including atrial arrhythmias, may be of the multi-wavelet reentry type, characterized by multiple asynchronous loops of electrical pulses dispersed around the atrial chambers and generally self-propagating (e.g., another example of IC ECG data). Alternatively, or in addition to the multi-wavelet reentry type, arrhythmias may also have a focal origin, such as when an identified tissue region within the atrium beats autonomously in a rapid, repetitive manner (e.g., another example of IC ECG data). Ventricular tachycardia (V-tach or VT) is a tachycardia or rapid heart rhythm originating from one of the ventricles. This is a potentially life-threatening arrhythmia as it can lead to ventricular fibrillation and sudden death.

[0081] For example, aFib occurs when normal electrical impulses generated by the sinoatrial node (e.g., another example of IC ECG data) are overwhelmed by chaotic electrical impulses originating from the atrial veins and PVs that cause irregular pulses to be transmitted to the ventricles. Irregular heartbeats occur and may last from minutes to weeks, or even years. aFib is typically a chronic condition that is usually caused by a stroke, with a slightly increased risk of death. The first-line treatment for aFib is medication that slows the heart rate or restores the heart rhythm to normal. In addition, people with aFib are usually given anticoagulants to prevent them from having a risk of stroke. The use of such anticoagulants is accompanied by its own risk of internal bleeding. For some patients, drug therapy is not enough, and their aFib is considered drug-refractory, that is, it cannot be cured with standard drug interventions. Synchronized cardioversion can also be used to convert aFib to a normal heart rhythm. Alternatively, aFib patients are treated by catheter ablation.

[0082] Catheter ablation-based therapy can include mapping the electrical properties of cardiac tissue (particularly the endocardium and cardiac volume), and selectively ablating cardiac tissue by applying energy. Electrical mapping or cardiac mapping (e.g., as implemented by any of the electrophysiology cardiac mapping systems and techniques described herein) includes creating a map of electrical potentials propagating along the cardiac tissue (e.g., a voltage map) or a map of arrival times to various tissue locations (e.g., a LAT map). Electrical mapping or cardiac mapping (e.g., a cardiac map) can be used to detect local cardiac tissue dysfunction. Ablation, such as ablation based on cardiac mapping, can stop or alter the propagation of unwanted electrical signals from one part of the heart 12 to another.

[0083] The ablation process damages unwanted electrical pathways by forming non-conductive lesions. A variety of energy delivery modalities for forming lesions have been disclosed, and include the use of microwaves, lasers, and more commonly radiofrequency energy to form conduction blocks along the walls of cardiac tissue. Another example of an energy delivery technique includes irreversible electroporation (IRE), which provides a high electric field that damages cell membranes. In a two-step procedure (e.g., mapping followed by ablation), electrical activity at various points within the heart 12 is typically sensed and measured by advancing a catheter 14 containing one or more electrical sensors (or electrodes 26) into the heart 12 and obtaining / acquiring data at multiple points (e.g., generally such as biometric data, or specifically such as ECG data). The ECG data is then used to select an endocardial target area where ablation is to be performed.

[0084] Cardiac ablation and other cardiac electrophysiology procedures are becoming increasingly complex as clinicians treat increasingly challenging conditions such as atrial fibrillation and ventricular tachycardia. Treatment of complex arrhythmias may rely solely on the use of a 3D mapping system to reconstruct the anatomy of the heart chamber of interest. In this regard, the mapping engine 101 employed by the system 10 herein manipulates and evaluates general biometric data or specific ECG data to produce improved tissue data that enables more accurate diagnosis, images, scans, and / or maps for treating abnormal heartbeats or arrhythmias. For example, cardiologists rely on software such as the 3D mapping engine 101 produced by Biosense Webster, Inc. (Diamond Bar, Calif.) to provide a more accurate diagnosis, image, scan, and / or map of the abnormal heartbeat or arrhythmia. The 3D mapping system's Complex Fractionated Atrial Electrogram (CFAE) module generates and analyzes ECG data. The mapping engine 101 of the system 10 enhances the software to generate and analyze improved biometric data, which further provides multiple pieces of information about the electrophysiological properties of the heart 12 (including scar tissue) that represent the cardiac substrate (anatomy and function) of aFib.

[0085] Thus, system 10 implements a 3D mapping system such as 3D mapping system to locate potential arrhythmogenic substrates of cardiomyopathy in terms of abnormal ECG detection. The substrates associated with these cardiac diseases are associated with the presence of fragmented and prolonged ECG in the endocardial layer and / or epicardial layer of the ventricular chambers (right and left). For example, areas of low or medium voltage may exhibit ECG fragmentation and prolonged activity. In addition, during sinus rhythm, areas of low or medium voltage may correspond to key isthmuses identified during sustained and organized ventricular arrhythmias (e.g., applicable to non-tolerant ventricular tachycardias, as well as in the atria). In general, abnormal tissue is characterized by a low voltage ECG. However, initial clinical experience in endocardial-epicardial mapping indicates that low voltage areas are not always present in such patients as the only arrhythmogenic mechanism. In fact, areas of low or medium voltage may exhibit ECG fragmentation and prolonged activity during sinus rhythm, which corresponds to key isthmuses identified during sustained and organized ventricular arrhythmias, e.g., applicable only to non-tolerant ventricular tachycardias. Furthermore, in many cases, ECG fragmentation and prolonged activity are observed in areas showing normal or near-normal voltage amplitudes (>1mV-1.5mV). Although the latter areas can be assessed based on voltage amplitude, they cannot be considered normal based on the intracardiac signal and therefore represent a true arrhythmogenic substrate. 3D mapping enables localization of the arrhythmogenic substrate on the endocardial and / or epicardial layers of the right / left ventricle, which can vary in distribution depending on the extension of the primary disease.

[0086] As another example operation, cardiac mapping can be implemented by the system 10 using one or more multi-electrode catheters (e.g., catheter 14). Multi-electrode catheters are used to stimulate and map electrical activity in the heart 12 and to ablate sites of abnormal electrical activity. When used, the multi-electrode catheter is inserted into a major vein or artery (e.g., femoral vein) and then guided into the chamber of the heart 12 of interest. A typical ablation procedure involves inserting a catheter 14 having at least one electrode 26 at its distal end into a cardiac chamber. A reference electrode is provided that is glued to the patient's skin, or a second catheter is provided that is positioned in or near the heart or selected from one or other electrodes 26 of the catheter 14. A radio frequency (RF) current is applied to the tip electrode 26 of the ablation catheter 14, and the current flows to the reference electrode through the medium (e.g., blood and tissue) surrounding the tip electrode. The distribution of the current depends on the amount of contact between the electrode surface and the tissue compared to the blood, which has a higher conductivity than the tissue. Due to the electrical resistance of the tissue, heating of the tissue occurs. The tissue is heated sufficiently to cause cell destruction in the cardiac tissue, resulting in the formation of non-conductive ablation lesions within the cardiac tissue. During this process, heating of the tip electrode 26 also occurs due to conduction from the heated tissue to the electrode itself. If the electrode temperature becomes high enough (perhaps above 60° C.), a thin transparent coating of dehydrated blood protein may form on the surface of the electrode 26. If the temperature continues to rise, the dehydrated layer may become thicker and thicker, causing blood coagulation on the electrode surface. Because the dehydrated biomaterial has a higher electrical resistance than the endocardial tissue, the impedance to the flow of electrical energy into the tissue also increases. If the impedance increases sufficiently, an impedance rise occurs, and the catheter 14 must be removed from the body and the tip electrode 26 cleaned.

[0087] Now go to Figure 2 , shows a diagram of a system 200 in which one or more features of the disclosed subject matter can be implemented, according to one or more exemplary embodiments. With respect to a patient 202 (e.g., Figure 1 23), the system 200 includes a device 204, a local computing device 206, a remote computing system 208, a first network 210, and a second network 211. In addition, the device 204 includes a biometric sensor 221 (e.g., Figure 1 ), a processor 222, a user input (UI) sensor 223, a memory 224, and a transceiver 225. Note that for ease of explanation and brevity, Figure 1 The mapping engine 101 in Figure 2 According to one embodiment, the device 204 is Figure 1An example of a system 100 of , wherein the device 204 may include both components that are internal to the patient 202 and components that are external to the patient 202. According to another embodiment, the device 204 is a device external to the patient 202 that includes an attachable patch (e.g., attached to the patient's skin). According to another embodiment, the device 204 may be internal to the body of the patient 202 (e.g., implanted subcutaneously), wherein the device 204 may be inserted into the patient 202 via any suitable means, including oral injection, surgical insertion via a vein or artery, endoscopic procedures, or laparoscopic procedures. According to one embodiment, although in Figure 2 A single device 204 is shown in FIG. 2 , but the example system may include multiple devices.

[0088] Thus, the apparatus 204, the local computing device 206, and / or the remote computing system 208 may be programmed to execute computer instructions relative to the mapping engine 101. For example, the memory 223 stores these instructions for execution by the processor 222 so that the apparatus 204 can receive and process biometric data via the biometric sensor 201. Thus, the processor 222 and the memory 223 represent the processor and memory of the local computing apparatus 206 and / or the remote computing system 208.

[0089] The device 204, local computing device 206 and / or remote computing system 208 can be any combination of software and / or hardware that, alone or in combination, stores, executes and implements the mapping engine 101 and its functions. In addition, the device 204, local computing device 206 and / or remote computing system 208 can be an electronic computer framework that includes and / or employs any number and combination of computing devices and networks that utilize various communication technologies, as described herein. The device 204, local computing device 206 and / or remote computing system 208 is easily scalable, extensible and modular, with the ability to change to different services or reconfigure some features independently of other features.

[0090] Networks 210 and 211 may be wired networks, wireless networks, or include one or more wired and wireless networks. According to one embodiment, network 210 is an example of a short-range network (e.g., a local area network (LAN) or a personal area network (PAN)). Any of a variety of short-range wireless communication protocols (such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near field communication (NFC), ultra-band, Zigbee, or infrared (IR)) may be used to send information between device 204 and local computing device 206 via network 210. In addition, network 211 is an example of one or more of the following: an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or a series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between local computing device 206 and remote computing system 208. Any of a variety of long-range wireless communication protocols (e.g., TCP / IP, HTTP, 3G, 4G / LTE, or 5G / new radio) may be used to send information via network 211. Note that for either of networks 210 and 211, wired connections may be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection and wireless connections may be implemented using Wi-Fi, WiMAX, as well as Bluetooth, infrared, cellular networks, satellite, or any other wireless connection method.

[0091] In operation, device 204 may continuously or periodically obtain, monitor, store, process biometric data associated with patient 202, and transmit the biometric data via network 210. In addition, device 204, local computing device 206, and / or remote computing system 208 communicate via networks 210 and 211 (e.g., local computing device 206 may be configured as a gateway between device 204 and remote computing system 208). For example, device 204 may be Figure 1 The system 100 is configured to communicate with a local computing device 206 via a network 210. The local computing device 206 may be, for example, a fixed / standalone device, a base station, a desktop / laptop computer, a smart phone, a smart watch, a tablet computer, or other device configured to communicate with other devices via networks 211 and 210. A public cloud computing provider (e.g., Amazon Web Services ) implemented as a physical server on or connected to the network 211 or a network 210 ) can be configured to communicate with the local computing device 206 via the network 211. Thus, biometric data associated with the patient 202 can be transmitted throughout the system 200.

[0092] The elements of the device 204 are now described. The biometric sensor 221 may include, for example, one or more transducers configured to convert one or more environmental conditions into electrical signals so that different types of biometric data can be observed / obtained / acquired. For example, the biometric sensor 221 includes one or more of the following: electrodes (e.g., Figure 1 electrodes 18 and 26), a temperature sensor (e.g., a thermocouple), a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer, and a microphone.

[0093] When executing the mapping engine 101, the processor 222 may be configured to receive, process, and manage biometric data acquired by the biometric sensor 221, and transmit the biometric data to the memory 224 via the transceiver 225 for storage and / or across the network 210. Biometric data from one or more other devices 204 may also be received by the processor 222 via the transceiver 225. As described in more detail below, the processor 222 may be configured to selectively respond to different tap patterns (e.g., single or double taps) received from the UI sensor 223, so that different tasks of the patch (e.g., acquisition, storage, or transmission of data) may be activated based on the detected pattern. In some embodiments, the processor 222 may generate audible feedback about the detected gesture.

[0094] UI sensor 223 includes, for example, a piezoelectric sensor or a capacitive sensor configured to receive user input such as a tap or touch. For example, in response to patient 202 tapping or contacting the surface of device 204, UI sensor 223 can be controlled to achieve capacitive coupling. Gesture recognition can be achieved via any of a variety of capacitance types, such as resistive capacitance, surface capacitance, projected capacitance, surface acoustic wave, piezoelectric, and infrared touch. Capacitive sensors can be set at a small area or over the length of a surface so that a tap or touch of the surface activates the monitoring device.

[0095] The memory 224 is any non-temporary tangible medium, such as magnetic memory, optical memory, or electronic memory (e.g., any suitable volatile memory and / or non-volatile memory, such as random access memory or a hard drive). The memory 224 stores computer instructions for execution by the processor 222. The transceiver 225 may include a separate transmitter and a separate receiver. Alternatively, the transceiver 225 may include a transmitter and a receiver integrated into a single device. In operation, the device 204 utilizes the mapping engine 101 to observe / obtain biometric data of the patient 202 via the biometric sensor 221, stores the biometric data in the memory, and shares the biometric data across the system 200 via the transceiver 225. The mapping engine 101 may then utilize models, neural networks, machine learning, and / or artificial intelligence to provide intracardiac unipolar far-field reduction or elimination using multiple electrode catheters.

[0096] Now go to Figure 3A , showing a method according to one or more exemplary embodiments (e.g., by Figure 1 and / or Figure 2 Method 300 is performed by the mapping engine 101 of the present invention. Method 300 is an example of using a multiple electrode catheter (e.g., catheter 14) to provide intracardiac unipolar far-field reduction or elimination. In one example, method 300 performs atrial local activation detection as well as ventricular detection within the QRS (regardless of late local activation and annotation). It is noted that the use of a multi-electrode catheter provides a spatial advantage, wherein the mapping engine 101 analyzes each electrode 18 and 26 and each corresponding intracardiac voltage signal based on the surrounding spatial electrode information. In this regard, the mapping engine 101 reduces or eliminates far-field information from all electrodes while significantly enhancing pure local unipolar signals. In addition to accurate activation annotation, the mapping engine 101 also provides scar detection based on local activation amplitude because the mapping engine 101 outperforms the known bipolar sensitivity to spatial electrode orientation and distance disadvantages.

[0097] The method 300 begins at box 310, where the mapping engine 101 performs a spatial electrode signal analysis on each of a plurality of electrodes. The spatial electrode signal analysis considers far-field characteristics associated with spatial position to provide electrode weighting information and determines a common signal component, which can be an estimate of the far-field information. At least one specific electrode is selected for the spatial electrode signal analysis. For each specific electrode, all other electrode information is considered and related to the specific electrode. According to one or more embodiments, the spatial electrode signal analysis is repeated, where each electrode is a specific electrode. According to one or more embodiments, for each multi-electrode catheter stable site sampling time window, all electrodes are analyzed one by one. In addition, for each specific electrode, all other electrode information is considered, as further described herein.

[0098] According to one or more embodiments of spatial electrode signal analysis, electrodes surrounding a specific electrode are weighted in a manner inversely proportional to the distance from the specific electrode. For example, the catheter 14 includes three (3) electrodes in a row separated by a distance such as two (2) millimeters. The mapping engine 101 analyzes the first electrode and may assign a weight function to it. Example weight functions include 1-0.25*D, where D is the distance of another electrode from the first electrode. Then, the mapping engine 101 generates a weight vector, and as the distance of a particular electrode from a reference electrode increases, the weight decreases (for example, the farther, the smaller the weight). According to one or more embodiments, electrodes beyond a certain distance may be excluded (note that the distance is catheter-related and in some cases is 5 millimeters). The mapping engine 101 then uses one or more algorithms (for example, mathematical multi-signal decomposition operations) to analyze the electrode weighting information. Examples of one or more algorithms include (but are not limited to) singular value decomposition (SVD), principal component analysis (PCA), and / or other matrix decompositions or factor decompositions. SVD can be a factorization of a real or complex matrix (e.g., an electrode weight matrix) to generalize the eigendecomposition of a square normal matrix using an orthogonal eigenbase. By analyzing the electrode weighting information, the mapping engine 101 estimates the common signal components hidden in all signals. The common signal components are estimated by the mapping engine 101 by taking the maximum decomposed component / signal. Other examples of common signal components include estimates of the maximum common signal component and far-field information.

[0099] At block 320, the mapping engine 101 scales the common signal component (identified by the mapping engine 101 and / or the spatial electrode signal analysis). According to one or more embodiments, the mapping engine 101 automatically scales the common signal component by applying the inverse decomposition formula using only the common signal component. Then, the far-field signal is estimated by using the scaled common signal.

[0100] According to one or more embodiments, the mapping engine 101 scales the common signal components to best fit the electrode signals being analyzed. Figure 3B Graph 325 of , demonstrating scaling by scalar multiplication. Graph 325 includes a sample index x-axis and a millivoltage y-axis. The graph shows an initial signal 326, a common signal 327, a scaled common signal 328 (i.e., far field), and a residual local signal 329 (i.e., near field). In addition, a residual signal is determined. That is, once scaled and fitted, the common signal component is subtracted to leave only the residual signal. The residual signal estimates pure local information. Local information may include, but is not limited to, local field signals within the electrical activity detected by the electrodes.

[0101] At box 340, the mapping engine 101 analyzes the pure local information to identify / generate / provide local unipolar activation. Local unipolar activation may include complex cases of dual activation patterns. According to one or more embodiments, for LAT mapping, the mapping engine 101 utilizes the minimum pure local information derivative to detect activation moments within the cardiac cycle. According to one or more embodiments, for voltage (V) mapping, the mapping engine 101 takes local activation peak to peak within the cardiac cycle. For both mappings, the mapping engine 101 averages the final local results over several cardiac cycles and adds spatial smoothing by combining all different spatial local results. At box 350, the mapping engine 101 displays the results. According to one or more embodiments, the mapping engine provides local unipolar activation (e.g., all local activation patterns for each electrode) above a constant zero background of the user interface of the display 165. The visual presentation of the display 165 of the local activation pattern may include a large duration for consistency analysis.

[0102] Now go to Figure 4 , showing a method according to one or more exemplary embodiments (e.g., by Figure 1 and / or Figure 2 Method 400 is performed by the mapping engine 101 of the embodiment of the present invention. Method 400 is an example of estimating far-field information, such as relative to Figure 3A The method 400 begins at block 410, where the mapping engine 101 receives electrode signals from the plurality of electrodes 18 and 26. According to one or more embodiments, the mapping engine 101 receives electrode signals from the electrodes 26 of the catheter 14.

[0103] At block 420, the mapping engine 101 determines a reference electrode from among the plurality of electrodes 18 and 26. The reference electrode may be selected from any electrode 26 of the catheter. Furthermore, more than one reference electrode may be determined such that the method 400 is performed for each reference electrode.

[0104] At box 430, the mapping engine 101 determines the weights based on an electrode weight matrix based on the reference electrode. The electrode weight matrix can be based on the structure of the catheter 14. It should be noted that the electrode weight matrix stores and manages the weights of the electrodes surrounding a specific electrode, which is inversely proportional to the distance from the specific electrode. Examples of high-density mapping catheters may include, but are not limited to, OCTARAY 100 from BiosenseWebster. TM Mapping catheter, NAV ECO High Density Mapping Catheter and / or OPTRELL TM High-density mapping catheter. Figures 5 and 6 An example catheter 14 is shown.

[0105] Figure 5A catheter 500 is shown according to one or more embodiments. The catheter 500 can be a catheter having a plurality of electrodes 534, such as at least twenty (20) electrodes. As shown, the plurality of electrodes can include exactly forty-eight (48) electrodes located on or dispersed across a plurality of ridges 537. Using such a number of electrodes 534 distributed over a wide area by the ridges 537 allows a large amount of electrical activity to be captured over a large area at once. According to one or more embodiments, the plurality of ridges 537 are moved through the sheath in a collapsed state, and once in Figure 1 The plurality of ridges may be expanded by entering the patient 23. Electrical activity at any focal point in the heart 12 may generally be measured by advancing the catheter 500, contacting cardiac tissue with the catheter 500, and acquiring data related to the electrical activity at that point. One or more of the electrodes 534 may be selected as reference electrodes.

[0106] For example, electrode 560 is designated as a reference electrode. In addition, as the distance from electrode 560 increases, the weight decreases. In this way, because electrode 562 is closer to electrode 560 than electrode 564, electrode 562 will have a higher weight than electrode 564. In addition, because electrode 566 is farther away from electrode 560 than electrode 562, electrode 566 will have a lower weight than electrode 562. It should be noted that the distance between each electrode in electrode 534 can be measured relative to three-dimensional space (i.e., using x, y, z coordinates). Then, electrodes 564 and 566 may or may not have the same weight.

[0107] Figure 6 A catheter 500 is shown according to one or more embodiments. The catheter 600 is a catheter having a plurality of electrodes 605 that provide one or more physiological signals. For example, the number of electrodes 605 can be at least three (3) arranged in any combination. Figure 6 As shown, a set of three electrodes 612, 614, and 612 and a set of two electrodes 622 and 624 are located on the same spline 620. According to one or more embodiments, the catheter 600 includes at least five (5) arms (e.g., Figure 6 as shown), and in some cases, eight (8) or more arms, and the electrodes may include twenty (20) or forty-eight (48) monopolar electrodes positioned in pairs or coupled 1 mm or 2 mm apart from each other (e.g., higher density improves performance).

[0108] Returning to method 400 , at box 440 , the mapping engine 101 determines the dot product of the weights based on the electrode weight matrix and the electrode signals.

[0109] At box 450, the mapping engine 101 uses one or more algorithms to process the dot product to generate an orthogonal signal. Generally speaking, the one or more algorithms of the mapping engine 100 include a mathematical multi-signal decomposition operation. Examples of one or more algorithms include (but are not limited to) singular value decomposition (SVD), principal component analysis (PCA), and / or other matrix decompositions or factorizations. SCV can be a factorization of a real or complex matrix (e.g., an electrode weight matrix) to generalize the eigendecomposition of a square normal matrix using an orthogonal eigenbasis.

[0110] At box 460, the mapping engine 101 selects the maximum signal among the orthogonal signals. At box 455, the mapping engine 101 performs a correlation analysis of the maximum signal with the signal of the reference electrode to provide a projection value. At box 470, the mapping engine 101 takes a tensor product of the maximum signal with the projection value to provide a far field estimate (e.g., a common signal component of electrical activity estimated by the spatial electrode signal analysis).

[0111] Now go to Figure 7 , showing a method according to one or more exemplary embodiments (e.g., by Figure 1 and / or Figure 2 Method 700 is performed by the mapping engine 101. Method 700 is an example of a cardiac cycle specific electrode signal analysis, such as relative to Figure 3A As described in block 310 of .

[0112] At box 708, the mapping engine 101 receives a first input. The first input includes a global PxP weight matrix. At box 709, a second input is received. The second input includes a specific electrode index K.

[0113] At box 710, the mapping engine 101 performs a first peek operation. The first peek operation includes peeking at the matrix row vector K. At box 715, the mapping engine 101 converts the global PxP weight matrix into a diagonal matrix. According to one or more embodiments, the mapping engine 101 outputs a specific PxP weight matrix for electrode K, where Wkk=1.

[0114] At box 718, the mapping engine 101 receives a third input. The third input includes N time samples and P electrode information in an NxP matrix. At box 720, the mapping engine 101 performs matrix multiplication. The matrix multiplication includes multiplication between the NxP matrix and the diagonal matrix to output a weighted information NxP matrix. At box 725, the mapping engine 101 performs an orthogonal decomposition of the weighted information NxP matrix to generate an orthogonal component NxP matrix.

[0115] At block 730, the mapping engine 101 performs a second peek operation. The second peek operation looks at the orthogonal component NxP matrix to find the largest component. The largest component is the common component.

[0116] At box 735, the mapping engine 101 performs an orthogonal synthesis of the common components. The mapping engine 101 can utilize the orthogonal component NxP matrix from the orthogonal decomposition of box 725. The mapping engine 101 provides the common components based on the synthesized NxP matrix. At box 740, the mapping engine 101 selects column K. At box 745, the mapping engine 101 provides an output. The output includes an estimated far-field signal of electrode K.

[0117] At box 755, the mapping engine 101 also selects column K to determine electrode K initial information. At box 760, the mapping engine 101 uses the output in box 745 to perform far-field subtraction on the initial information to determine residual information. At box 765, the mapping engine 101 provides output. The output includes estimated local information for each electrode.

[0118] Figure 8 An exemplary catheter of an example system configured to identify electrical activation signals at different distances from an electrode location according to one or more embodiments is depicted. As shown, the example mapping catheter 500 is inserted into the left ventricle of the heart 12 of the patient 23 (shown as a transverse cross-section of the myocardial tissue through the left ventricle 804), such as may be performed during a cardiac ablation procedure to address ventricular tachycardia. According to certain embodiments further described below, the mapping engine 101 of the cardiac mapping system 10 may be configured to identify and / or visualize electrical activity below the surface of the myocardial tissue (e.g., a depth of 0.5mm-10mm within the cardiac tissue) in three dimensions relative to an electrode (e.g., electrode 810 or electrode 814) in contact with the endocardium of the left ventricle 804.

[0119] For example, instead of detecting only electrical activity on the inner surface of the myocardial tissue of the left ventricle 804, the mapping engine 101 can be utilized to determine electrical activity at one or more depths at the surface and / or below the surface. In the depicted example, the tissue depth 820 is 1 mm, 2 mm, 3 mm, and 4 mm below the inner surface of the myocardial tissue of the left ventricle 804; however, it should be understood that application of the methods herein can be applicable to any tissue depth on any surface of the heart associated with targeted arrhythmias and treatment procedures (e.g., the inner surface of another one or more chambers of the heart and the depth within the myocardial tissue of the one or more chambers). As discussed above, electrical activity can be detected or identified as near-field components, activation times (e.g., using minimum derivatives), and / or voltage levels (e.g., maximum-minimum). Advantageously, the present invention with respect to Figures 9 to 11 The described example method enables identification of distinct electrical signals at each specified depth (eg, depth 820 ) within myocardial tissue.

[0120] Fig. 9An example method for isolating electrical signals at a distance (eg, depth) within cardiac tissue using ECG information collected from a multi-electrode mapping catheter is shown, as shown in the example Figure 8 As depicted in . At box 902, a deep learning or machine learning model is trained to calculate weights for identifying ECG signals at different distances or depths from a specific electrode. The data collection process for training such a model can be performed in various ways. In one example, data is collected through preclinical studies. The collected data may include samples of electrical activity (e.g., near-field components, activation times (e.g., using minimum derivatives) and / or voltage levels (e.g., maximum-minimum)) on the surface of myocardial tissue and at multiple locations within the myocardial tissue. For example, the collected data may include electrical activity for each of multiple points (e.g., point clouds, voxel clouds, or other three-dimensional structures) at multiple times (e.g., before, during, and / or after activation). In some specific implementations, the collected data may be collected via electroanatomical mapping (EAM). For example, in some specific implementations, the study may involve the use of an animal or human model that includes a myocardial infarction (MI) scar or channel therein. In some specific implementations, EAM may be performed to capture electrical signals and map the model. Subsequently, during the post-study dissection, the depth of the scar or channel may be measured to obtain basic true depth information. In some specific implementations, the training data may include simulated ECG signals (e.g., signals that look as if they come from a diagnostic catheter and are similar to previously measured signals). Generating such ECG signals may be accomplished by changing real data, mixing real data (e.g., mixing measured signals from multiple studies or patients), generating statistically representative data (e.g., by determining the mean of the input data set and changing the data within one or more standard deviations), etc. The input data may be in any suitable format, such as an array of voltage values. Such voltage values ​​are typically given in mV and may be in a range between 0mV-5mV in some specific implementations, or may be in a wider range (e.g., 0mV-10mV, 0mV-20mV, etc.). The training data may also include simulations of conductivity at different parts of tissue, ventricular anatomical structures, and locations. For example, training data simulating scars may have lower conductivity compared to healthy tissue having higher conductivity.

[0121] Additionally or alternatively, electrical waves originating from different tissue depths within the myocardial tissue may be simulated, thereby generating an analysis signal with a known depth of the electrical source. For example, cardiac anatomy and conductivity may be simulated, including variations in different structures of cardiac tissue with and without scars or with scars of varying depths and sizes. The ECG signal to be measured in the case of using a catheter to map the electrical activity of a region may be simulated (with variations around different scars, structural sizes, transmurality, partial scars, etc.). In some implementations, the anatomical structure may also vary.

[0122] Various deep learning or machine learning model architectures can be trained on the collected data, including but not limited to convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers, depending on the specific characteristics collected. The training data can be fed into the selected model and trained using an appropriate optimization algorithm (such as stochastic gradient descent (SGD) or Adam optimization). Through the training process, the model learns to associate the input ECG signal with its corresponding depth or distance, thereby effectively learning the weights of the linear combination of the components of the signal. In some specific implementations, the input data can be divided into training data and test data, allowing the accuracy and / or sensitivity of the model to be tested and confirmed. In some specific implementations, the input data can be enhanced to increase promotion, for example, by generating additional data (e.g., pseudo data or virtual data generated from the input data and similar to the input data, such as via scaling, rotation, transposition, etc., and sometimes referred to as shadow data, promotion data, or similar terms). Once the model has been successfully trained and evaluated, it can be deployed in an implementation of the mapping engine 101 to identify signals at various depths of the tissue.

[0123] At the next box 904, spatial electrode signal analysis can be performed on each of the multiple electrodes of the mapping catheter in contact with the cardiac tissue. Spatial electrode signal analysis can be performed according to the method 300 or method 400 described above, including a multi-signal decomposition operation (such as but not limited to SVD or PCA) to produce multiple orthogonal signal components. Specifically, in some specific implementations, local unipolar signals can be identified and far-field signals can be suppressed to map the electrical activity of cells at the inner surface of the ventricle. The ECG signal measured at a specific point is the sum of different electrical signals received in that point, some of which are far-field (generated by more distant cardiac tissue), other signals are local (generated by inner surface cardiac tissue), and there are other signals in between (i.e., nearby, but not on the surface). When the far-field signal is removed, the local and nearby signals are retained.

[0124] At block 906, mathematical combinations of the different components are performed to identify ECG signals at different distances or depths from a specific electrode within the plurality of electrodes. The mathematical combination includes linear combinations, and in some embodiments, nonlinear combinations to account for degradation or attenuation of the signal depending on the distance of the signal from the subject electrode. For example, in some implementations, the signals may be added by weighting coefficients (e.g., aX+bY+cZ...). In other implementations, the signals may be added based on the depth of the tissue (e.g., X(z x )+Y(z y ^2)…, where z nis the depth below the surface to the signal source). In other specific implementations, the signals may be summed based on the distance from the electrode (e.g., the x,y Cartesian distance between the signal source and the electrode). Various weighting functions and / or exponential functions may be utilized. The weights or weighting coefficients may be adjusted in a variety of ways, including by employing a deep learning or other artificial intelligence (AI) or machine learning (ML) algorithm trained in this manner and using the data described above with reference to box 902. By considering the spatial information contained in the orthogonal signals (e.g., x,y coordinates; x,y,z coordinates; z depth measurement, etc.) and manipulating their contributions through mathematical combination, the mapping engine 101 can distinguish and extract (i.e., identify) electrical signals originating from various distances around a specific electrode (including at depths within myocardial tissue).

[0125] Finally, at box 908, the ECG signal is visualized on a display at multiple distances or depths from the electrode. The visualization may take the form of a conventional electrogram 21, where the electrogram shown on the display may be selectable based on multiple distances (e.g., tissue depth into the myocardial tissue (e.g., 1mm, 2mm, 3mm, 4mm...10mm)). Additionally or alternatively, the visualization may take the form of a 3D electroanatomy, where the map includes multiple "shells," where each shell corresponds to a specific tissue depth into the myocardial tissue (e.g., 1mm, 2mm, 3mm, 4mm...10mm or greater). Each shell may be constructed in the same manner as used by electroanatomy systems to depict signal propagation along the tissue surface in a 3D cardiac map, such as in the 3D Electroanatomy System from Biosense Webster. 3 system. In some implementations, the visualization may provide a point cloud, voxel cloud, or other three-dimensional representation. In some implementations, the visualization may show a smooth surface at a selectable depth within the tissue (e.g., showing a 2 mm depth map). The user or physician may select different depths. In some implementations, the visualization may be animated to periodically show different depths (e.g., sequentially over time or iteratively stepping through each depth at a single time point).

[0126] Fig.10 An alternative example method for isolating electrical signals at a distance (eg, depth) within cardiac tissue using ECG information collected from a multi-electrode mapping catheter is shown, as shown in FIG. Figure 8. Beginning at box 1002, simulation data is generated for various conditions. More specifically, a simulation data set can be generated that includes various conditions, including but not limited to various catheter positions, transmembrane potential positions, and myocardial tissue activation states (e.g., healthy, scarred, fibrotic). The data can be in any suitable format, such as an array of potentials at a given time point, an array of voxels with coordinates and voltage values ​​(e.g., {x, y, z, v}), a depth map, or any other such format. The simulation also preferably considers a transition point from an inactive state to an interactive state of the transmembrane potential (e.g., at a determined threshold voltage, such as 0.5 mV, 1 mV, 0.5 V, or any other appropriate voltage threshold).

[0127] As a next block 1004, a neural network is trained using simulated data, wherein the network takes as input ECG readings from a multi-electrode catheter (or simulated readings from a myocardial tissue model in various activation states) and outputs an estimated distance to the nearest activation within the myocardial tissue. The training process involves adjusting the weights of the network using a cost function, specifically the difference between the network output at each time and the distance of the activation from the electrode. In some implementations, the input or simulated data may be divided into a first training data set and a second test data set, as discussed above. The weights may be modified to increase the accuracy and / or sensitivity of the trained model to general data.

[0128] At box 1006, the mapping engine 101 may implement a trained model to analyze ECG readings from a multi-electrode catheter and output an estimated distance to the nearest activation for each electrode in real time. The nearest activation may be on the inner surface of the myocardial tissue, or may be in deeper tissue, and may be identified by the depth or distance to the activation. In some implementations, this may be repeated for multiple depths (e.g., the mapping engine 101 may implement a trained model to analyze ECG readings from a multi-electrode catheter and output an estimated distance to the nearest activation at a first depth (such as 1 mm) for each electrode, then repeat this step for a depth of 2 mm, then for a depth of 3 mm, etc.). In other implementations, the process need not be iterative, but rather a trained model may provide a list of depths as well as a voltage signal for each depth.

[0129] Finally, at block 1008, the electrical signal at multiple distances or depths from the electrode is visualized on a display. Example visualizations may take the form of the above with respect to Fig. 9 The forms of those described in box 908 are as follows.

[0130] Fig.11 Still another alternative example method for isolating electrical signals at a distance (eg, depth) within cardiac tissue using ECG information collected from a multi-electrode mapping catheter is shown, as shown in the example Figure 8At box 1102, the mapping engine 101 can implement a mathematical model that defines a collection of sources (e.g., dipoles) distributed on a surface and within cardiac tissue, wherein the model preferably includes a representation of the spatial distribution of the dipole sources, activation times, and other relevant parameters.

[0131] Next, at block 1104, for each electrode, the mathematical model and the known electrode positions are applied to calculate the expected signal (ie, field projection) at each electrode position. This calculation may involve determining the potential at each electrode based on the dipole activation and its position.

[0132] Then, at block 1106, optimization and inverse problem solution are applied to determine the dipole activation that fits (i.e., best matches) the input ECG readings to the computed field projections. In some embodiments, the optimization and inverse problem solution may include an optimization algorithm (such as gradient descent, Levenberg-Marquardt, or a genetic algorithm) to iteratively adjust the dipole activation and minimize the objective function.

[0133] Finally, at block 1108, the electrical signal at multiple distances or depths from the electrode is visualized on a display. Example visualizations may take the form of the above with respect to Fig. 9 The forms of those described in box 908 are as follows.

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the possible specific implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, fragment or part of an instruction, which includes one or more executable instructions for implementing a specified logical function. In some alternative specific implementations, the function indicated in the box may not occur in the order indicated in the accompanying drawings. For example, depending on the function involved, the two boxes shown in succession may actually be executed substantially simultaneously, or the box may sometimes be executed in the opposite order. It should also be noted that each box in the block diagram and / or flowchart illustration and the combination of boxes in the block diagram and / or flowchart illustration can be implemented by a dedicated hardware-based system that performs a specified function or action or by a combination of dedicated hardware and computer instructions. Although the features and elements are specifically described above, it will be known to those of ordinary skill in the art that each feature or element can be used alone or in any combination with other features and elements. In addition, the methods described herein can be implemented in a computer program, software or firmware incorporated in a computer-readable medium for execution by a computer or processor. As used herein, computer-readable media should not be understood as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0135] Examples of computer-readable media include electronic signals (transmitted via a wired or wireless connection) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, registers, cache memories, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, optical media such as compact disks (CDs) and digital versatile disks (DVDs), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), and memory sticks. A processor associated with software can be used to implement a radio frequency transceiver used in a terminal, a base station, or any host computer.

[0136] The terms used herein are only for the purpose of describing specific embodiments and are not intended to be limited. As used herein, unless the context clearly indicates otherwise, the singular forms "one", "a kind of" and "the / said" include plural references. It should also be understood that the terms "include" and / or "comprise" specify the presence of the features, integers, steps, operations, elements and / or parts when used in this specification, but do not exclude the presence or addition of one or more other features, integers, steps, operations, element parts and / or their groups. The description of various embodiments herein is presented for the purpose of illustration, but is not intended to be exhaustive or limited to the disclosed embodiments. Without departing from the scope and essence of the embodiments, many modifications and variations will be apparent to those of ordinary skill in the art. The terms used herein are selected to best explain the principles of the embodiments, the practical application or technical improvements relative to the technology present on the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method comprising: receiving, by a mapping engine executed by one or more processors, electrical activity from a plurality of electrodes of the catheter; as well as An electrical signal originating from a depth within the cardiac tissue is identified within the electrical activity.

2. The method of claim 1, further comprising identifying within the electrical activity electrical signals originating from an endocardial surface of the cardiac tissue.

3. The method according to claim 1 further comprises performing, by the mapping engine, a spatial electrode signal analysis of the electrical activity on each of the plurality of electrodes; and Wherein identifying the electrical signal originating from a depth within the cardiac tissue comprises computing a linear combination of signal components within the electrical activity.

4. The method according to claim 3, wherein: Computing linear combinations of signal components within the electrical activity further includes multiplying each signal component within the electrical activity by a corresponding weight determined via the trained model.

5. The method according to claim 3, wherein: Identifying the electrical signal originating from a depth within the cardiac tissue further includes calculating a non-linear combination of the signal components within the electrical activity.

6. The method according to claim 1, wherein: Identifying electrical signals originating from a depth within cardiac tissue includes providing the electrical activity from the plurality of electrodes as input to a neural network, and determining, via the neural network, for at least one of the plurality of electrodes, an estimated distance from the corresponding electrode to the nearest activation.

7. The method according to claim 1, wherein: Identifying the electrical signal originating from a depth within the cardiac tissue includes, for each of the plurality of electrodes, applying a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position.

8. The method according to claim 1, wherein: The received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes.

9. The method according to claim 1, wherein: Identifying electrical signals originating from depth within cardiac tissue includes executing a machine learning model trained on simulated ECG data.

10. The method of claim 1, further comprising generating, by the mapping engine, a visual display of the identified electrical signals at a specified depth within the cardiac tissue.

11. A system comprising: A memory storing software of the mapping engine; and one or more processors configured to execute the mapping engine to: receiving electrical activity from a plurality of electrodes of the catheter, and An electrical signal originating from a depth within the cardiac tissue is identified within the electrical activity.

12. The system according to claim 11, wherein: The one or more processors are further configured to identify, within the electrical activity, electrical signals originating from an endocardial surface of the cardiac tissue.

13. The system according to claim 11, wherein: The one or more processors are further configured to perform a spatial electrode signal analysis of the electrical activity on each electrode of the plurality of electrodes; and Wherein identifying the electrical signal originating from a depth within the cardiac tissue comprises computing a linear combination of signal components within the electrical activity.

14. The system according to claim 13, wherein: Computing linear combinations of signal components within the electrical activity further includes multiplying each signal component within the electrical activity by a corresponding weight determined via the trained model.

15. The system of claim 13, wherein: Identifying the electrical signal originating from a depth within the cardiac tissue further includes calculating a non-linear combination of the signal components within the electrical activity.

16. The system according to claim 11, wherein: The one or more processors are further configured to provide the electrical activity from the plurality of electrodes as input to a neural network and determine, via the neural network, for at least one electrode of the plurality of electrodes, an estimated distance from the corresponding electrode to the nearest activation.

17. The system of claim 11, wherein: The one or more processors are further configured to apply, for each of the plurality of electrodes, a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position.

18. The system of claim 11, wherein: The received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes.

19. The system of claim 11, wherein: The processor is further configured to execute a machine learning model trained on simulated ECG data.

20. The system of claim 11, wherein: The one or more processors are further configured to generate a visual display of the identified electrical signals at a specified depth within the cardiac tissue.

21. A method comprising: Receiving electrical activity from a plurality of electrodes (26) of a catheter (14) via a mapping engine (101) executed by one or more processors (61); as well as An electrical signal originating from a depth (820) within the cardiac tissue (12) is identified within the electrical activity.

22. The method of claim 21, further comprising identifying within the electrical activity electrical signals originating from an endocardial surface of the heart tissue (12).

23. The method according to claim 21 further comprises performing, by the mapping engine (101), a spatial electrode signal analysis of the electrical activity on each electrode (26) of the plurality of electrodes (26); and Wherein identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes calculating a linear combination of signal components within the electrical activity.

24. The method according to claim 23, wherein: Computing linear combinations of signal components within the electrical activity further includes multiplying each signal component within the electrical activity by a corresponding weight determined via the trained model.

25. The method according to claim 23, wherein: Identifying an electrical signal originating from a depth (820) within cardiac tissue (12) further includes calculating a non-linear combination of the signal components within the electrical activity.

26. The method of claim 21, wherein: Identifying an electrical signal originating from a depth (820) within cardiac tissue (12) includes providing the electrical activity from the plurality of electrodes (26) as input to a neural network and determining, via the neural network, for at least one of the plurality of electrodes (26), an estimated distance from a corresponding electrode (26) to the nearest activation.

27. The method of claim 21, wherein: Identifying an electrical signal originating from a depth (820) within the cardiac tissue (12) includes, for each electrode (26) of the plurality of electrodes (26), applying a mathematical model and known electrode positions of the plurality of electrodes (26) to calculate an expected signal at each known electrode position.

28. The method of claim 21, wherein: The received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes (26).

29. The method according to claim 21, wherein: Identifying electrical signals originating from depth within cardiac tissue includes executing a machine learning model trained on simulated electrocardiogram (ECG) data.

30. The method of claim 21, further comprising generating, by the mapping engine (101), a visual display (20) of the identified electrical signals at a specified depth within the cardiac tissue (12).

31. A system comprising: A memory (62) storing software of the mapping engine (101); and One or more processors (61) configured to execute the mapping engine (101) to: receiving electrical activity from a plurality of electrodes (26) of the catheter (14), and An electrical signal originating from a depth (802) within the cardiac tissue (12) is identified within the electrical activity.

32. The system of claim 31, wherein: The one or more processors (61) are further configured to identify electrical signals within the electrical activity originating from an endocardial surface of the heart tissue (12).

33. The system of claim 31, wherein: The one or more processors (61) are further configured to perform a spatial electrode signal analysis of the electrical activity on each electrode (26) of the plurality of electrodes (26); and Wherein identifying the electrical signal originating from a depth (820) within the cardiac tissue (12) includes calculating a linear combination of signal components within the electrical activity.

34. The system of claim 33, wherein: Computing linear combinations of signal components within the electrical activity further includes multiplying each signal component within the electrical activity by a corresponding weight determined via the trained model.

35. The system of claim 33, wherein: Identifying an electrical signal originating from a depth (820) within cardiac tissue (12) further includes calculating a non-linear combination of the signal components within the electrical activity.

36. The system of claim 31, wherein: The one or more processors (61) are further configured to provide the electrical activity from the plurality of electrodes (26) as input to a neural network and to determine, via the neural network, for at least one electrode of the plurality of electrodes (26), an estimated distance from a corresponding electrode (26) to the nearest activation.

37. The system of claim 31, wherein: The one or more processors (61) are further configured to apply, for each electrode (26) of the plurality of electrodes (26), a mathematical model and known electrode positions of the plurality of electrodes (26) to calculate an expected signal at each known electrode position.

38. The system of claim 31, wherein: The received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes (26).

39. The system of claim 31, wherein: The processor is further configured to execute a machine learning model trained on simulated electrocardiogram (ECG) data.

40. The system of claim 31, wherein: The one or more processors (61) are further configured to generate a visual display (20) of the identified electrical signals at a specified depth within the cardiac tissue (12).

41. A method comprising: receiving, by a mapping engine executed by one or more processors, electrical activity from a plurality of electrodes of the catheter; as well as An electrical signal originating from a depth within the cardiac tissue is identified within the electrical activity.

42. The method of claim 41 further comprising identifying electrical signals within the electrical activity originating from an endocardial surface of the cardiac tissue.

43. The method of claim 41 or 42, further comprising performing, by the mapping engine, a spatial electrode signal analysis of the electrical activity on each of the plurality of electrodes; and Wherein identifying the electrical signal originating from a depth within the cardiac tissue comprises computing a linear combination of signal components within the electrical activity.

44. The method of claim 43, wherein: Computing linear combinations of signal components within the electrical activity further includes multiplying each signal component within the electrical activity by a corresponding weight determined via the trained model.

45. The method of claim 43, wherein: Identifying the electrical signal originating from a depth within the cardiac tissue further includes calculating a non-linear combination of the signal components within the electrical activity.

46. ​​A method according to any one of claims 41 to 45, wherein: Identifying electrical signals originating from a depth within cardiac tissue includes providing the electrical activity from the plurality of electrodes as input to a neural network, and determining, via the neural network, for at least one of the plurality of electrodes, an estimated distance from the corresponding electrode to the nearest activation.

47. A method according to any one of claims 41 to 46, wherein: Identifying the electrical signal originating from a depth within the cardiac tissue includes, for each of the plurality of electrodes, applying a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position.

48. A method according to any one of claims 41 to 47, wherein: The received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes.

49. The method according to any one of claims 41 to 48, wherein: Identifying electrical signals originating from depth within cardiac tissue includes executing a machine learning model trained on simulated electrocardiogram (ECG) data.

50. The method of any one of claims 41 to 49, further comprising generating, by the mapping engine, a visual display of the identified electrical signals at a specified depth within the cardiac tissue.

51. A system comprising: A memory storing software of the mapping engine; and one or more processors configured to execute the mapping engine to: receiving electrical activity from a plurality of electrodes of the catheter, and An electrical signal originating from a depth within the cardiac tissue is identified within the electrical activity.

52. The system of claim 51, wherein: The one or more processors are further configured to identify, within the electrical activity, electrical signals originating from an endocardial surface of the cardiac tissue.

53. A system according to claim 51 or 52, wherein: The one or more processors are further configured to perform a spatial electrode signal analysis of the electrical activity on each electrode of the plurality of electrodes; and Wherein identifying the electrical signal originating from a depth within the cardiac tissue comprises computing a linear combination of signal components within the electrical activity.

54. The system of claim 53, wherein: Computing linear combinations of signal components within the electrical activity further includes multiplying each signal component within the electrical activity by a corresponding weight determined via the trained model.

55. The system of claim 53, wherein: Identifying the electrical signal originating from a depth within the cardiac tissue further includes calculating a non-linear combination of the signal components within the electrical activity.

56. A system according to any one of claims 51 to 55, wherein: The one or more processors are further configured to provide the electrical activity from the plurality of electrodes as input to a neural network and determine, via the neural network, for at least one electrode of the plurality of electrodes, an estimated distance from the corresponding electrode to the nearest activation.

57. A system according to any one of claims 51 to 56, wherein: The one or more processors are further configured to apply, for each of the plurality of electrodes, a mathematical model and known electrode positions of the plurality of electrodes to calculate an expected signal at each known electrode position.

58. A system according to any one of claims 51 to 57, wherein: The received electrical activity includes real-time electrocardiogram (ECG) readings from the plurality of electrodes.

59. A system according to any one of claims 51 to 58, wherein: The processor is further configured to execute a machine learning model trained on simulated electrocardiogram (ECG) data.

60. The system of any one of claims 51 to 59, wherein: The one or more processors are further configured to generate a visual display of the identified electrical signals at a specified depth within the cardiac tissue.

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