Visualization of ventricular tachycardia causing reentry circuits using a pseudo-excitation map.

Abstract

To provide systems and methods for visualizing reentrant circuits in the heart via pseudo-activation maps.SOLUTION: Techniques disclosed comprise receiving sets of ECG data and respective pacing sites. Each of the sets is generated by pacing heart tissue at a respective site of the pacing sites. The techniques disclosed further comprise computing correlation gradients representative of morphological changes across sets, of the sets of the ECG data, of respective neighboring sites of the pacing sites. Based on the computed correlation gradients, a core zone associated with a reentrant circuit is identified. Then, relative to the identified core zone, one or more pseudo-activation maps are generated.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] (Cross-reference of related applications) This application claims the interests of U.S. Provisional Patent Application No. 63 / 179,054, filed on 23 April 2021, which is incorporated herein by reference in its entirety. [Background technology]

[0002] In a normal heart rhythm, electrical pulses are generated in the atria and propagate to the ventricles, causing them to contract synchronously. These contractions pump blood received from the atria through the ventricles to the lungs and other organs in the body. Ventricular tachycardia (VT), a type of arrhythmia, can be caused by electrical pulses generated within the ventricles themselves. VT disrupts the normal ventricular contraction rhythm by causing palpitations (e.g., more than 180 beats per minute) and resulting in the contraction of empty (or not yet filled with blood) ventricles. VT can be fatal if left untreated, as it can worsen to ventricular fibrillation (VF), which can lead to cardiac arrest.

[0003] VT is often caused by the presence of one or more reentrant circuits in the ventricular tissue. Reentrant circuits are sources of electrical pulses within the ventricle. To treat VT, ablation is used, a procedure in which radiofrequency energy is applied to the tissue of the reentrant circuit to interrupt the flow of electrical signals between them. For ablation to be effective, it is necessary to visualize the structure and orientation of the reentrant circuit. [Brief explanation of the drawing]

[0004] A more detailed understanding can be achieved from the following explanation, which is provided as an example along with the attached diagram. [Figure 1] This is a diagram of an exemplary device for cardiac pacing and diagnosis, on which one or more features of the present disclosure can be implemented. [Figure 2]A functional block diagram of an exemplary system for cardiac pacing and diagnosis, based on which one or more features of the present disclosure can be implemented. [Figure 3] A flowchart of an exemplary method for visualizing a reentry circuit in the heart by a pseudo-excitation map, based on which one or more features of the present disclosure can be implemented. [Figure 4] An exemplary graph of a three-dimensional surface mesh, based on which one or more features of the present disclosure can be implemented. [Figure 5] A diagram of a weighted three-dimensional surface mesh, based on which one or more features of the present disclosure can be implemented. [Figure 6] Shows an exemplary chart comparing pseudo-excitation values and excitation values according to one or more embodiments of the present disclosure. [Figure 7] Shows an exemplary visual representation of a pseudo-excitation map and an excitation map according to one or more embodiments of the present disclosure. [Figure 8] Shows an exemplary chart comparing pseudo-excitation values and excitation values according to one or more embodiments of the present disclosure. [Figure 9] Shows an exemplary visual representation of a pseudo-excitation map and an inverse excitation map according to one or more embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[0005] Myocardial infarction can lead to life-threatening VT. The infarcted area of the heart can progress to scarring through a process in which damaged myocardial tissue is replaced by fibrosis. Such a process can affect the flow of electric current, the scarred area blocks electrical conduction, and the electrical conduction in the area containing surviving myocardial fibers decreases. This non-uniform conductivity creates a so-called reentry circuit, which has been found to be a common cause of VT. In a reentry circuit, current exits from a low-conductivity region with a delay greater than the refractory period of muscle cells, causing repeated excitation of healthy muscle cells.

[0006] VT treatment typically begins with a diagnostic phase in which the origin of the reentrant circuit is identified, followed by an ablation phase in which radiofrequency heating is applied to the core of the reentrant circuit to disrupt the excitation caused by the reentrant circuit. Accurate visualization of the reentrant circuit during the diagnostic phase is crucial for effective ablation. The most direct technique for visualizing the reentrant circuit is excitation mapping. Excitation mapping involves obtaining intracardiac electrical signals called electrophoresis by placing a catheter in different locations of ventricular tissue during sustained VT. A three-dimensional (3D) map of propagation delays between electrophoresis representing the excitation pathway of VT can then be reconstructed. Unfortunately, excitation mapping can only be performed in a small number of patients who can tolerate sustained VT for the entire duration of mapping.

[0007] Pacemapping is another technique that has been shown to be a superior alternative to direct excitation mapping. Pacemapping involves stimulating the heart using a catheter at different locations in the ventricular tissue to generate excitation pathways originating from these locations. The exit site of the reentry circuit is identified at the pacing site where the excitation pathway best matches the excitation pathway previously recorded during the patient's induced VT, i.e., where the electrocardiogram (ECG) data recorded during pacing best matches the ECG data recorded during induced VT. Thus, a similarity metric can be used to correlate (compare) the morphology of QRS complexes from the ECG data recorded during pacing and the ECG data recorded during induced VT. This conventional pacemapping technique has advantages in that 1) it requires only short ECG data recordings of VT, 2) it can reconstruct and visualize the actual reentry circuit, and 3) after radiofrequency ablation, feedback on the efficiency of the applied ablation can be obtained by stimulating the tissue at the entry point of the circuit to demonstrate that no more current can enter this circuit. However, this technique is limited because it requires a baseline recording during induced VT, but VT cannot be induced in some patients.

[0008] Referenceless pace mapping is another technique for identifying the target (core) region of ablation by visualizing the re-entry circuit. Referenceless pace mapping relies solely on ECG data recorded during the pacemapping procedure and does not require a reference recording during induced VT. Since the core of the re-entry circuit has been shown to indicate abrupt changes in the excitation pathway, this technique highlights the region corresponding to the abrupt changes in the excitation pathway. This is done by comparing ECG data generated at adjacent pacing sites, rather than comparing ECG data generated at the pacing site with ECG data generated during induced VT. As shown herein, referenceless pace mapping accurately identifies the same core region as conventional pacemapping.

[0009] Referenceless pace mapping calculates correlation gradients based on morphological changes in ECG data of the pacing site network. The correlation gradient can indicate areas of rapid spatial variation in the pacing-generated ECG data as the pacing catheter moves between adjacent sites along the ventricular tissue. The correlation gradient can be calculated based on a similarity metric that measures the morphological similarity between two QRS complexes from ECG data recorded during pacing at each adjacent pacing site. Morphological changes in the paced ECG data can serve as an indicator of changes in the excitation pathway. Since core regions containing re-entry circuits exhibit rapid changes in the excitation pathway, detecting the former can pinpoint the location of core regions containing re-entry circuits.

[0010] However, while correlation gradients can highlight core regions, they do not provide a complete visualization of reentry circuits, and current systems do not utilize the information contained in the data collected by pacemapping to determine the structure and orientation of reentry circuits. Furthermore, it has been shown that the entry points of reentry circuits are associated with long (or longer) intervals between pacing and the resulting QRS complex. These intervals are called stimulus-to-QRS (sQRS) delays. sQRS delays collected during pacemapping are currently not fully utilized in the characterization of reentry circuits. For example, pacing points located deep within scar tissue have longer sQRS delays than pacing points in healthy tissue. As disclosed herein, pseudo-excitation maps are generated based on data collected during pacemapping procedures, such as correlation maps, correlation gradient maps, or sQRS delays, and possibly on complementary data collected during sinus rhythm, such as conduction velocity. Pseudo-excitation maps can be used to effectively target tissue for ablation in VT treatment.

[0011] Aspects of this disclosure describe a method for visualizing reentry circuits within the heart using a pseudo-excitation map used by a processor. The method comprises receiving a set of ECG data and each pacing site, each of which is generated by pacing cardiac tissue at each site of the pacing site. The method further comprises calculating correlation gradients representing morphological changes between sets of ECG data for each adjacent site of the pacing site. Based on the correlation gradients, core regions associated with the reentry circuit are identified, and a pseudo-excitation map is generated with respect to the identified core regions.

[0012] Aspects of this disclosure also describe a system for visualizing reentry circuits within the heart by pseudo-excitation maps. The system includes at least one processor and a memory for storing instructions. When executed by at least one processor, the instructions cause the system to receive sets of ECG data and their respective pacing sites, each of which is generated by pacing cardiac tissue at each of the pacing sites. The instructions further cause the system to calculate correlation gradients representing morphological changes between sets of ECG data for each adjacent site of the pacing sites, to identify core regions associated with reentry circuits based on the correlation gradients, and to generate pseudo-excitation maps with respect to the identified core regions.

[0013] Furthermore, aspects of the present disclosure describe a non-transient computer-readable medium comprising instructions executable by at least one processor for performing a method of visualizing reentry circuits within the heart by pseudo-excitation maps. The method comprises receiving sets of ECG data and their respective pacing sites, each of which is generated by pacing cardiac tissue at each of the pacing sites. The method further comprises calculating correlation gradients representing morphological changes between sets of ECG data for each adjacent site of the pacing sites. Based on the correlation gradients, core regions associated with reentry circuits are identified and pseudo-excitation maps are generated with respect to the identified core regions.

[0014] Figure 1 is a diagram of an exemplary apparatus 100 for cardiac pacing and diagnostics, on which one or more features of this disclosure can be implemented. Apparatus 100 may generally be referred to as a medical device. All or part of apparatus 100 may be used to collect biometric data by universal pacing operation and / or to implement cardiac pacing and diagnostic software described herein. Apparatus 100 may be configured to perform an operation to generate a pseudo-excitation map using information obtained from data collected from pacemapping. Apparatus 100 may be configured to collect pacemapping data to generate a pseudo-excitation map, or to generate a pseudo-excitation map based on pacemapping data processed by apparatus 100.

[0015] The cardiac pacing and diagnostic device 100 includes a probe 110 that a physician or medical professional 115 can navigate into a body part such as the heart 120 of a patient 125 lying on a bed (or table) 130 (see insert 139). The probe 110 can represent multiple probes according to the embodiment. For brevity, a single probe 110 is described herein. Insert 140 shows a magnified view of the distal end of the probe 110 inside the ventricle of the heart 120. As shown in insert 140, the probe 110 includes a catheter 141, a shaft 143, and a sheath 146. The catheter 141 includes one or more elements. The elements of the catheter may be electrodes 151.

[0016] The probe 110 is connected to (and / or communicates with) a console 160 configured to store data acquired by the probe 100 and run cardiac pacing and diagnostic software therein. According to one embodiment, a medical professional 115 can insert the shaft 143 through the sheath 146 while manipulating the distal end of the shaft 143 using a manipulator 148 connected to the proximal end of the probe 110. For example, the medical professional 115 can use the manipulator 148 to deflect the distal end of the shaft 143 away from the sheath 146. The catheter 141 (attached to the distal end of the shaft 143 as shown in inset 140) may be inserted through the sheath 146 in a folded state and then expanded within the heart 120.

[0017] According to one or more embodiments, the catheter 141 may be of any shape or type and may represent one or more catheters. The catheter 141 may include one or more elements used to implement the embodiments disclosed herein. The one or more elements may be any elements that can be configured to pace, ablate, and / or measure biometric data. For example, in one embodiment, the one or more elements may be electrodes (e.g., electrode 151), transducers, other elements, or a combination thereof. The catheter 141 may include multiple elements that can be connected via splines forming the shape of the catheter 141. For example, the catheter 141 may be a Picasso catheter having multiple electrodes. A Picasso catheter may have, for example, 48 or more electrodes. Other examples of the catheter 141 include a linear catheter having linearly arranged electrodes, a balloon catheter including electrodes dispersed on multiple splines forming a balloon, a lasso catheter or loop catheter having circularly arranged electrodes, or any other applicable shape. The catheter 14 may be fully or partially elastic so that it can twist, bend, or otherwise change its shape based on a received signal and / or based on the application of an external force to the catheter (for example, applied by cardiac tissue).

[0018] The catheter 141 may be configured to damage tissue areas of internal organs, such as by ablating tissue areas of the ventricles of the heart 120. The catheter 141 may further be configured to perform pacing using pulses, and then biometric data may be acquired in response to those pulses. In this regard, the catheter 141 may be placed inside the body of the patient 125 (e.g., inside the heart 120). The position of the catheter 141 may be determined by the console 160, for example, based on a transducer placed close to the body of the patient 125 and a position electrode attached to the catheter 141. The catheter 141 may also be configured to measure biometric data (e.g., electrical signals of the heart, such as sinus rhythm) obtained from inside the body of the patient 125 (e.g., the heart 120). The obtained biometric data can be associated with the determined position of the catheter, and as a result, a rendering of the patient's body part (e.g., the heart 120) can be displayed, showing the biometric data superimposed on the shape of the body. It should be noted that sinus rhythm can be any cardiac rhythm in which myocardial depolarization begins in the sinoatrial node. For example, sinus rhythm can include heartbeats with normal heart rate and rhythm (for example, the human heart rate is generally 60-100 beats per minute).

[0019] The cardiac pacing and diagnostic device 100 can be used to detect, diagnose, and treat cardiac conditions. Cardiac conditions such as cardiac arrhythmias are common and dangerous medical conditions, especially in the elderly population. In patients with normal sinus rhythm, the heart (composed of the atria, ventricles, and conduction tissues) is electrically excited and beats in a synchronous, patterned manner. In patients with cardiac arrhythmias, abnormal areas of cardiac tissue do not follow the synchronous beating cycle associated with normally conductive tissues, as is observed in patients with normal sinus rhythm. Instead, abnormal conduction occurs in the abnormal areas of cardiac tissue to adjacent tissues, disrupting the cardiac cycle and resulting in an asynchronous rhythm. Such abnormal conduction is known to occur in various areas of the heart, such as the sinoatrial (SA) node region along the conduction pathway of the atrioventricular (AV) node, or the myocardial tissue forming the walls of the ventricles and atria.

[0020] VT is a type of arrhythmia characterized by a rapid rhythm originating from one of the ventricles of the heart. It is a potentially fatal arrhythmia because it can lead to ventricular fibrillation and sudden death. VT can be a multi-wavelet reentrant cardiac arrhythmia, characterized by multiple asynchronous loops of electrical impulses that scatter around the atria and often self-propagate. In place of, or in addition to, the multi-wavelet reentrant type, cardiac arrhythmias may also have focal sources of excitation (which may be foci of interest for cardiac pacing and diagnostic devices 100), such as when an isolated region of tissue is rapidly and repeatedly excited autonomously.

[0021] As described herein, the cardiac pacing and diagnostic device 100 provides cardiologists and healthcare professionals with a method for observing how specific lesions in cardiac tissue respond to pacing. Thus, the cardiac pacing and diagnostic device 100 enables pacing procedures, particularly using the catheter 141 and console 160, and allows cardiologists and healthcare professionals to perform signal analysis that is not currently available or being performed. More specifically, the console 160, connected to and communicating with the probe 110 and catheter 141, can store and run cardiac pacing and diagnostic software. According to one embodiment, the console 160 includes at least one processor and memory, the processor executing computer instructions (related to the cardiac pacing and diagnostic software described herein), and the memory storing instructions for execution by the processor.

[0022] The console 160 can be any computing device including software and / or hardware, such as a general-purpose computer, and includes appropriate front-end and interface circuits for transmitting and receiving signals to and from the catheter 141, and for controlling other components of the device 100. The front-end and interface circuits include an input / output (I / O) communication interface that enables the console 160 to receive signals from and / or transfer signals to the catheter electrodes 151. In some embodiments, the console 160 may be further configured to receive biometric data, such as electrical activity, which enables determination of whether a given tissue region conducts electricity. According to one embodiment, part or all of the console 160 may be located, for example, in the catheter 151, in an external device, in a mobile device, in a cloud-based device, or in a standalone processor / computer.

[0023] As described above, the console 160 may include a general-purpose computer, which can be programmed with software (e.g., cardiac pacing and diagnostic software) to perform the functions of the cardiac pacing and diagnostic device 100 described herein. The software may be downloaded to the general-purpose computer in electronic form, for example, over a network, or alternatively or additionally, provided and / or stored on a non-temporary tangible medium such as magnetic memory, optical memory, or electronic memory (e.g., any suitable volatile and / or non-volatile memory such as random access memory or a hard disk drive).

[0024] In one embodiment, the display may be connected to the console 160. During the procedure, the console 160 can facilitate rendering and presenting the body part on the display to the medical professional 115 and can store the data representing the body part in memory. In some embodiments, the medical professional 115 may be able to manipulate the rendering representation of the body part using one or more input devices such as a touchpad, mouse, keyboard, or gesture recognition device. For example, the position of the catheter 141 may be changed using the input device so that the rendering is updated. In an alternative embodiment, the display may include a touchscreen that can be configured to receive input from the medical professional 115 in addition to presenting the rendered body part. Note that the display may be local or remote to the console 160 and may be installed in the same location, but may also be installed in a remote location such as another hospital or within another healthcare provider network.

[0025] The console 160 may be connected by cable to body surface electrodes, which may include adhesive skin patches attached to the patient 125. The processor of the console 160, together with a current tracking module, can determine the position coordinates of the catheter 141 within a body part of the patient 125 (e.g., the heart 120). The position coordinates may be based on impedance or electromagnetic fields measured between the body surface electrodes and electrodes 151 or other electromagnetic components of the catheter 141. Additionally, or alternatively, a position pad may be placed on the surface of the bed 130, or it may be separated from the bed 130.

[0026] The cardiac pacing and diagnostic device 100 can acquire anatomical measurements of the heart 120 using ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), or other medical imaging modalities known in the art. The cardiac pacing and diagnostic device 100 can acquire electrical measurements, such as an ECG, using a catheter 141 or other sensor that measures the electrical characteristics of the heart 120. Thus, biometric data, which may include electrical and anatomical measurements, may then be stored in a non-transient tangible medium of the console 160. The biometric data can be transmitted from the non-transient tangible medium to a server, which may be local or remote, using a network, as further described herein.

[0027] According to one or more embodiments, a catheter 141 including a position sensor can be used to determine the trajectory of a point on the surface of the heart. These trajectories can be used to infer kinetic properties such as the contractile force of the tissue. Maps showing such kinetic properties can preferably be constructed when trajectory information is sampled at a sufficient number of position points within the heart.

[0028] The cardiac pacing and diagnostic device 100 shown in Figure 1 may be modified to implement embodiments disclosed herein. Embodiments of this disclosure can be similarly applied using other system components and settings. The device 100 shown in Figure 1 may include further components such as other elements for sensing electrical activity, wired or wireless connectors, processing, memory, and display devices. The cardiac pacing and diagnostic device 100 may also be part of a surgical system configured to acquire anatomical and electrical measurements of the heart 120 and perform cardiac ablation procedures. An example of such a surgical system is the Carto® system, sold by Biosense Webster.

[0029] Figure 2 is a functional block diagram of an exemplary system 200 for cardiac pacing and diagnosis, on which one or more features of the present disclosure can be implemented. The system 200 includes a patient 201 (e.g., patient 125 in Figure 1), a local computing device 206, a remote computing system 208, a first network 210, and a second network 211. Furthermore, the system 200 includes a patient biometric sensor 221 (e.g., catheter 141 in Figure 1) communicably connected to a cardiac pacing and diagnostic device 220 (e.g., console 160 in Figure 1). The cardiac pacing and diagnostic device 220 may include a processor 222, a user input sensor 223, a memory 224, and a transmitter-receiver (i.e., transceiver) 225.

[0030] The local computing device 206 and / or remote computing system 208, together with the cardiac pacing and diagnostic device 220, may be any combination of software, firmware, and / or hardware that individually or collectively store, execute, and implement cardiac pacing and diagnostic software and its functions. Furthermore, as described herein, the local computing device 206 and / or remote computing system 208, together with the cardiac pacing and diagnostic device 220, may be an electronic computer framework comprising and / or using any number and combination of computing devices and networks utilizing various communication technologies. The local computing device 206 and / or remote computing system 208, together with the cardiac pacing and diagnostic device 220, may be scalable, expandable, and modular, having the ability to be changed to different services or to reconfigure some features independently of other features. According to one embodiment, the local computing device 206 and the remote computing system 208, together with the cardiac pacing and diagnostic device 220, may include at least a processor (e.g., processor 222 in Figure 2) and memory (e.g., memory 224 in Figure 2), wherein the processor executes computer instructions (related to cardiac pacing and diagnostic software), and the memory stores computer instructions to be executed by the processor.

[0031] The local computing device 206 of the cardiac pacing and diagnostic system 200 may communicate with the cardiac pacing and diagnostic device 220 and be configured to function as a gateway to the remote computing system 208 via a second network 211. The local computing device 206 may also be configured to display acquired patient biometric data. The local computing device 206 may be, for example, a smartphone, smartwatch, tablet, or other portable smart device configured to communicate with other devices via the network 211. Alternatively, the local computing device 206 may be a fixed or standalone device such as a fixed base station, desktop, or laptop computer that uses an executable program to communicate information between the cardiac pacing and diagnostic device 220 and the remote computing system 208, for example, via a modem, router, wireless module, and / or USB dongle. Biometric data may be communicated between the local computing device 206 and the cardiac pacing and diagnostic device 220 using short-range wireless technology standards via a short-range wireless network 210, such as a local area network (LAN) (e.g., a personal area network (PAN)). The information may also be transmitted between the cardiac pacing and diagnostic device 220 and the local computing device 206 via the network 210 using one of various short-range wireless communication protocols, such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near-field communications (NFC), Ultraband, or infrared (IR).

[0032] The remote computing system 208 may be configured to provide (visual and / or auditory) biometric data of patient 125 and present the data to a medical professional, physician, or healthcare professional. Therefore, the remote computing system 208 may be configured to receive biometric data of the monitored patient 125 via a network 211, which may be a long-range network. For example, if the local computing device 206 is a mobile phone, the network 211 may be a wireless cellular network, and information may be communicated between the local computing device 206 and the remote computing system 208 via a wireless technology standard such as one of the wireless technologies described above. The network 211 may be a wired network, a wireless network, or a combination thereof, such as 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 that facilitates communication between the local computing device 206 and the remote computing system 208. Information may be transmitted over network 211 using one of various long-range wireless communication protocols (e.g., TCP / IP, HTTP, 3G, 4G / LTE, or 5G / New Radio). Wired connections can be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection commonly known in the art. Wireless connections can be implemented using Wi-Fi, WiMAX, Bluetooth, infrared, cellular networks, satellite, or any other wireless connection technique. Furthermore, several networks may operate independently or communicating with each other to facilitate communication within network 211.In some cases, the remote computing system 208 can be implemented as a physical server on network 211. In other cases, the remote computing system 208 can be implemented as a virtual server on a public cloud computing provider (e.g., Amazon Web Services (AWS)) on network 211.

[0033] During operation, the cardiac pacing and diagnostic device 220 may use cardiac pacing and diagnostic software to acquire patient 201 biometric data (e.g., electrical signals, blood pressure, temperature, blood glucose levels, or other biometric data) acquired by the patient biometric sensor 221. In one embodiment, at least a portion of the acquired biometric data may be received from one or more other patient biometric diagnostic devices. The cardiac pacing and diagnostic software is processor-executable code necessarily rooted in the processing operations of the cardiac pacing and diagnostic device 220 and its processing hardware for providing a method for analyzing specific areas or lesions of cardiac tissue in response to pacing procedures. According to one embodiment, the cardiac pacing and diagnostic software of the cardiac pacing and diagnostic device 220 provides specific pacing and capture operations involving multi-step manipulation of electrical signals relating to cardiac tissue, which facilitates a more accurate understanding of the electrophysiology of cardiac tissue. The cardiac pacing and diagnostic device 220 can process data, including acquired biometric data and any biometric data received from one or more other patient biometric diagnostic devices, using cardiac pacing and diagnostic software. For example, when processing data in this regard, the cardiac pacing and diagnostic software may include a neural network used to learn latent representations (or data encodings) from the biometric data in an unsupervised manner. Furthermore, the cardiac pacing and diagnostic software can learn to detect specific data by training the neural network.

[0034] The cardiac pacing and diagnostic device 220 can continuously or periodically monitor, store, process, and transmit any number of diverse patient biometric data (e.g., acquired biometric data) via the network 210. Examples of patient biometric data, as described herein, include electrical signals (e.g., ECG signals and brain biometric data), blood pressure data, blood glucose data, and temperature data. Patient biometric data may be monitored and transmitted for treatment across any number of diverse diseases, such as cardiovascular diseases (e.g., arrhythmias, cardiomyopathy, and coronary artery disease) and autoimmune diseases (e.g., type 1 and type 2 diabetes).

[0035] The biosensor 221 may include, for example, one or more transducers configured to convert one or more environmental conditions into electrical signals, thereby enabling observation, acquisition, or retrieval of different types of biosensor data. For example, the patient biosensor 221 may include one or more electrodes (e.g., electrode 151 in Figure 1), one or more transducers, 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.

[0036] The processor 222 may be configured to receive, process, and manage biometric data acquired by the patient biometric sensor 221 when running cardiac pacing and diagnostic software, store the biometric data in memory 224, and / or transmit the biometric data to the entire network 210 via transceiver 225. As described in further detail herein, data from one or more other cardiac pacing and diagnostic devices 220 may also be received by the processor 222 via transceiver 225. Also as described herein, the processor 222 may be configured to selectively respond to different tapping patterns (e.g., single tap or double tap) received from a user input sensor 223 (e.g., an internal capacitive sensor), and as a result, different tasks (e.g., data acquisition, storage, or transmission) may be triggered based on the detected pattern. In some embodiments, the processor 222 may generate audible feedback in response to the detection of a gesture.

[0037] The user input sensor 223 includes, for example, a piezoelectric or capacitive sensor configured to receive user input such as a tap or touch. For example, the user input sensor 223 may be controlled to implement capacitive coupling in response to a patient 201 tapping or touching the surface of the cardiac pacing and diagnostic device 220. Gesture recognition can be implemented via any one of various capacitive types, such as resistive capacitive, surface capacitive, projected capacitive, surface ultrasonic, piezoelectric, and infrared touch. The capacitive sensor may be positioned over a small area or length of the surface so that a tap or touch of the surface activates the monitoring device.

[0038] Memory 224 may be any non-temporary tangible medium such as a magnetic memory unit, an optical memory unit, or an electronic memory unit. Memory 224 may include any suitable volatile memory and / or non-volatile memory such as random access memory or a hard disk drive. Memory 224 stores cardiac pacing and diagnostic software to be run on the processor 222.

[0039] The transceiver 225 may represent one or more transceivers, each of which may include a separate transmitter and a separate receiver. Alternatively, the transceiver 225 may include a transmitter and receiver integrated into a single device.

[0040] According to one embodiment, the cardiac pacing and diagnostic device 220 may be a device located inside the patient's body (e.g., implantable subcutaneously). In such a case, the cardiac pacing and diagnostic device 220 may be inserted into the patient 201 by any applicable method, including oral infusion, surgical insertion via vein or artery, endoscopic procedures, or laparoscopic procedures. According to one embodiment, the cardiac pacing and diagnostic device 220 may be a device located outside the patient 201. For example, as will be described in more detail herein, the cardiac pacing and diagnostic device 220 may include an attachable patch (e.g., attached to the patient's skin). According to one embodiment, the cardiac pacing and diagnostic device 220 may include both internal and external components of the patient. Although a single cardiac pacing and diagnostic device 220 is shown in system 200 of Figure 2, in one embodiment, system 200 may include multiple patient biomedical diagnostic devices. In such a case, the cardiac pacing and diagnostic device 220 may communicate with one or more other patient biomedical diagnostic devices. Furthermore, one or more other patient biometric diagnostic devices may communicate with the network 210 and other components of the cardiac pacing and diagnostic system 200.

[0041] As described above, this specification discloses a method for generating a pseudo-excitation map using data collected from a pacemapping procedure. In one embodiment, the collected data includes surface ECG data (also referred to herein as ECG data) recorded during the pacemapping procedure. The ECG data may include sets, each containing multiple leads (signals measured at each electrode pair), each set generated by stimulating cardiac tissue at each pacing site. A standard ECG records cardiac activity using 12 leads, including three standard leads (I, II, III), three unipolar limb leads (aVR, aVL, aVF), and six pectoral leads from V1 to V6. The 12-lead ECG set is a set of 12 signals, each having a specific shape, such as a peak in the P wave or a rapid variation in the QRS complex (lasting approximately 0.08 seconds). The QRS complex corresponds to ventricular depolarization following tissue stimulation at the pacing site. While this disclosure describes the collection of ECG data obtained from pacemapping procedures, it should be understood that the teachings described herein may also apply when other VT-related data are collected using other techniques.

[0042] Figure 3 is a flowchart of an exemplary method 300 for visualizing reentry circuits within the heart by a pseudo-excitation map, on which one or more features of the present disclosure can be implemented. Method 300 may be used by a processor (e.g., the processor 222 of the cardiac pacing and diagnostic device 220 of system 200 shown in Figure 2). Method 300 begins in step 310 by receiving a set of ECG data and each pacing site. Each of the received sets of ECG data may be generated by pacing cardiac tissue at each pacing site of the received pacing site (e.g., using the catheter 141 of the device 100 shown in Figure 1). The set of ECG data may include 12-lead ECG signals generated by stimulating cardiac tissue at each pacing site. Typically, the location of the pacing sites is obtained by tracking the 3D position of the catheter performing pacing at these pacing sites. In step 320, correlation gradients representing morphological changes between sets of ECG data for each adjacent pacing site may be calculated. In step 330, core regions associated with the cardiac reentry circuit may be identified based on the correlation gradient. Then, in step 340, a pseudo-excitation map is generated for the identified core regions.

[0043] Method 300 further includes generating a 3D surface mesh based on received pacing regions, where the pacing regions form nodes in a graph and pairs of pacing regions are connected by edges in the graph. Thus, correlation gradients may be calculated with respect to the edges of the 3D surface mesh based on morphological changes between two sets of ECG data for each pacing region connected by edges. In one embodiment, the 3D surface mesh includes edges, each having a correlation gradient below a threshold. The 3D surface mesh is further described with reference to Figure 4.

[0044] The generation of a pseudo-excitation map for the identified core region (in step 340) includes the calculation of pseudo-excitation values ​​for the pacing sites. Each pseudo-excitation value may represent the progression delay along a path through the edges of the 3D surface mesh between one pacing site associated with the identified core region and a part of the above pacing site. The progression delay along the path may be a cumulative delay including an estimate of the excitation delay at each edge of the 3D surface mesh along the path. In one embodiment, the excitation delay estimate includes the respective sQRS delay. In another embodiment, the excitation delay estimate includes the respective sinus rhythm conduction delay. The path may represent the shortest path through the edges of the 3D surface mesh such that the sum of the weights associated with the edges is minimized. As will be described in detail below, the weights associated with the edges may represent the sQRS delay (or sinus rhythm conduction delay) between the pacing sites connected by the edges. The generation of the pseudo-excitation map will be further described with reference to Figures 5 to 9.

[0045] Figure 4 is an exemplary graph of a three-dimensional surface mesh 400, on which one or more features of the present disclosure can be implemented. The 3D surface mesh 400 is a low-resolution representation of a surface, for example, a low-resolution representation of the surface of the left ventricle of the heart reconstructed based on pacing sites collected by a pacemapping procedure, as shown in Figure 4. Thus, the nodes of the graph 400 are defined in a coordinate system centered on the centroids of, for example, N pacing sites, referred to herein as P n or P n (x,y,z):corresponds to N pacing regions represented by n=1-N. To approximate the surface where the pacing regions are located, adjacent pacing regions (represented by graph nodes) are connected by graph edges.

[0046] One way to connect the pacing points (nodes) in Graph 400 is by using Delaunay triangulation. For example, pacing point P nThe coordinates [x, y, z] can be converted to polar coordinates [r, θ, φ]. Subsequently, a two-dimensional Delaunay triangulation can be applied to the (θ, φ) plane to establish connections between adjacent pacing sites. Using the obtained list of triangles, pacing sites P n in 3D space can be connected to form a 3D surface mesh 400. In one aspect, adjacent pacing sites may be connected within the 3D surface mesh if they are separated from each other by a distance less than a threshold value.

[0047] As described above, the excitation pathway along the ventricular tissue changes abruptly in the abnormal region, i.e., the core region of the potential reentry circuit, as can be seen from the morphological changes in the paced ECG data. The abnormal region can be identified by detecting abrupt morphological changes in the paced ECG data, i.e., by detecting the spatial variation in the QRS complex pattern of the ECG data obtained when the catheter moves between adjacent pacing sites. Thus, the spatial variation in the pattern of a pair of QRS complexes generated by stimulating each pacing site can be measured by a similarity metric (or distance metric) applied to those QRS complexes. For example, the similarity metric may be a correlation coefficient. Thus, the similarity metric between two connected pacing sites P i and P j (e.g., between pacing site P 43 and pacing site P 28 in FIG. 4) can be defined as follows:

[0048]

Equation

[0049]

number

[0050]

number

[0051] Given a similarity metric between two connected pacing regions, the gradient (spatial change) of such similarity metric, i.e., the correlation gradient, can be calculated. In one embodiment,

[0052]

number

[0053]

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[0054] One or more core regions may be identified using correlation gradient values ​​calculated for pairs of connected pacing regions (or for edges of the 3D surface mesh 400). To identify the core regions, each high correlation gradient (i.e., high G) ij A region encompassed by one or more pacing sites having ) can be detected. For example, in Figure 4, the core region 410 has a high correlation gradient value G 18,40 and G 42,43 Based on P 18 , P 40 , P 42 and P 43 It can be identified within the region encompassed by . Therefore, the identified core region 410 can be represented as CZ={18,40,42,43}.

[0055] Next, the identified core regions can be used to facilitate the generation of each pseudo-excitation map. For this purpose, the propagation delay of the paced ECG signal along the pathway on the ventricular tissue is used for one or more pacing sites that identify the core region, for example, pacing site P that identifies core region 410 in Figure 4. 18 , P 40 , P 42 and P 43 It is possible to calculate with respect to one or more of the following. To determine the propagation delay, weights can be calculated with respect to the edges connecting adjacent pacing regions in the graph (3D surface mesh 400). Edge E ab The weight w(E ab ) are two pacing regions P connected by this edge. a and P b This may also accommodate the propagation delay between the two. In one embodiment, the connected pacing region P a and P b Propagation delay w(E) related to a,b The propagation delay w(E) may be estimated by the delay between each sQRS pattern. In another embodiment, the propagation delay w(E) a,b ) may be estimated by the difference in sinus rhythm excitation time at connected pacing sites. For example, connected pacing site P a and Pb The weight w(E a,b ) can be calculated as follows:

[0056]

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[0057] Next, using the weights of each edge, the core region

[0058]

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[0059]

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[0060]

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[0061]

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[0062] Each pacing area P on the 3D surface mesh 400 j Progression delay related to this, i.e., pseudo-excitation value PAV j The following applies to each pacing site P j Shortest path SP related to j It can be calculated by adding the delay along the following:

[0063]

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[0064]

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[0065]

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[0066] Pseudo-excitability (PAV) j=1:N However, for example, the pacing area

[0067]

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[0068] In one embodiment, pseudo-excitation values

[0069]

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[0070]

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[0071] Using the pseudo-excitation map described herein, the structure and orientation of reentry circuits within an identified core region can be visualized. Therefore, it is possible to determine the minimum area to which tissue ablation should be applied. In one embodiment, the pseudo-excitation map is displayed to the cardiologist via the apparatus 100 and system 200 using an aspect of method 300, which is carried out by software executable thereon. As described above, the pseudo-excitation map allows for rapid visualization of potential reentry circuits to be ablated without referring to ECG data recorded during induced VT at the start of the procedure.

[0072] Next, we describe experiments comparing the pseudo-excitation maps disclosed herein with excitation maps. The experiments were conducted using the CARTO3® system (Biosense Webster, Inc., Irvine, USA) and the Niobe® system (Stereotaxis Inc., St. Louis, USA). Anonymized data was exported from the CARTO3® workstation and then processed using the MATLAB language (Mathworks, Natick, USA), in-house software described in version R2020a. In this experiment, electroanatomical data, including catheter position, 12-read ECG data acquired for 2.5 seconds for each pacing site, and a high-resolution vendor-generated 3D mesh of the ventricular cavity of the heart, were first loaded into the software. All other data used in the experiment (e.g., 3D surface mesh, 3D correlation gradient map, pseudo-excitation map, and excitation map) were generated offline in the MATLAB software. In this experiment, tachycardia did not persist for long enough to map excitation along the entire ventricle, and physicians only had time to focus on mapping the main elements of the reentry circuit when performing excitation mapping. Local excitation values ​​for pacing sites were provided using nearest neighbor interpolation. For a given pacing site, a local excitation value was considered defined only if the distance to the nearest excitation point was less than 20 mm. Thirty pacing sites were selected.

[0073] Tachycardia is a circuit, and the local excitation time of each pacing site i in tachycardia

[0074]

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[0080] As described above, pseudo-excitation maps were generated by using pacing points within the identified core region as starting points for generating each pseudo-excitation map. These pseudo-excitation maps were then compared to the excitation patterns or pathways of tachycardia. When the starting point was located in the exit region of the re-entry circuit, the pseudo-excitation map was compared to the excitation map. When the starting point was located in the inlet region of the re-entry circuit, the pseudo-excitation map had the opposite excitation pattern and was compared to the opposite excitation map. Thus, pacing at the inlet of the re-entry circuit and pacing at the exit of the re-entry circuit result in two opposite excitation patterns. Further information regarding tachycardia re-entry circuits can be found in U.S. Patent No. 10,891,728, which is incorporated by reference as if its entirety were fully described herein.

[0081] The pseudo-excitation maps, excitation maps, and inverse excitation maps from the experiments were integrated into surgical systems such as the CARTO3® platform for better visualization. The agreement between the pseudo-excitation maps, excitation maps, and inverse excitation maps was evaluated using various metrics, including mean, standard deviation, median, mean absolute error, and both Spearman and Lin correlation coefficients (see upper left of the graphs in Figures 6 and 8).

[0082] Figure 6 shows an exemplary chart 600 comparing pseudo-excitation values ​​and excitation values ​​according to one or more embodiments of the present disclosure. The compared pseudo-excitation values ​​and excitation values ​​are shown at the pacing site 40, i.e.

[0083]

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[0084]

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[0085] Figure 8 shows an exemplary chart 800 comparing pseudo-excitation values ​​and excitation values ​​according to one or more embodiments of the present disclosure. The compared pseudo-excitation values ​​and excitation values ​​are shown at the pacing site 42, i.e.

[0086]

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[0087]

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[0088] The flowcharts and block diagrams in the figures illustrate the structure, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing the indicated logical function. In some alternative implementations, the functions shown in a block may be performed in an order other than that shown in the figure. For example, two consecutively shown blocks may actually be executed substantially simultaneously, or they may sometimes be executed in reverse order depending on the relevant functionality. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs a particular function or operation, or they may operate or execute a combination of dedicated hardware and computer instructions.

[0089] While features and elements are described above in specific combinations, those skilled in the art will understand that each feature or element can be used individually or in combination with other features and elements. In addition, the methods described herein may be implemented in computer programs, software, or firmware incorporated into a computer-readable medium for execution on a computer or processor. The computer-readable medium as used herein should not be interpreted as being a transient signal in itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses passing through fiber optic cables), or electrical signals transmitted through moving wires.

[0090] Examples of computer-readable media include electrical signals (transmitted via wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media, though not limited to these, include registers, cache memory, 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 memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), and memory sticks. A processor can be used with software to implement a radio frequency transceiver for use in terminals, base stations, or any host computer.

[0091] The terms used herein are intended solely to describe specific embodiments and are not intended to be limiting. Where used herein, unless otherwise specified in the context, the singular forms "a," "an," and "the" also include the plural forms. The terms "comprise" and / or "comprising," as used herein, indicate the presence of a described feature, integer, process, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integers, processes, operations, elements, components, and / or groups thereof.

[0092] One or more computer systems can be configured to perform a specific operation by installing software, firmware, hardware, or a combination thereof on the system that causes the system to perform a specific operation when it is running. One or more computer programs can be configured to perform a specific operation by containing instructions that cause a data processing device to perform a specific operation when executed by that device. One common embodiment involves measuring data by pacing cardiac tissue using multiple pulses at multiple mapping points using multiple electrodes of a catheter. The method also involves generating a pseudo-excitation map using the measured data, which is used to identify re-entry circuits. Other embodiments of this embodiment include a corresponding computer system, device, and computer program recorded on one or more computer storage devices, each configured to perform the operation of the method.

[0093] The descriptions of different embodiments in this specification are for illustrative purposes only and are not intended to be exhaustive or limitful to the embodiments disclosed. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments described. The terms used herein have been selected to best describe the principles, practical applications, or technical improvements of the embodiments compared to the art available on the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0094] All patent applications, patents, and printed publications referenced herein are incorporated herein by whole reference, except for any abandonment or denial of any subject matter, and except to the extent that the incorporated material contradicts the express disclosure herein, in which case the language of this disclosure shall prevail.

[0095] [Implementation Method] (1) A method used by a processor to visualize reentrant circuits within the heart using a pseudo-excitation map, The process involves receiving sets of ECG data and their respective pacing sites, wherein each set is generated by pacing cardiac tissue at each of the pacing sites. The calculation of correlation gradients representing morphological changes between sets of ECG data for each adjacent region of the pacing region, Based on the correlation gradient, the core region related to the re-entry circuit is identified, To generate a pseudo-excitation map with respect to the identified core region, Methods that include... (2) The method according to Embodiment 1, further comprising generating a 3D surface mesh based on the received pacing portions, wherein the pacing portions form nodes of a graph, and pairs of the pacing portions are connected by edges of the graph. (3) The calculation of the correlation gradient is The method according to Embodiment 2, comprising calculating one of the correlation gradients with respect to the edges of the 3D surface mesh based on morphological changes between sets of ECG data of each pacing region connected by the edges. (4) The method according to Embodiment 3, wherein the 3D surface mesh includes edges having respective correlation gradients below a threshold. (5) The generation of the pseudo-excitation map relating to the identified core region is: The method of Embodiment 2, comprising calculating a pseudo-excitation value with respect to the pacing region, each of which the pseudo-excitation value represents a progression delay along a path, the path extending from the pacing region associated with the identified core region through the edges of the 3D surface mesh to each of the pacing regions.

[0096] (6) The method according to Embodiment 5, wherein the progression delay represents a cumulative delay including an estimate of the excitation delay of each edge of the 3D surface mesh along the path. (7) The method according to embodiment 6, wherein the estimated excitation delay includes the respective stimulus-paired QRS(sQRS) delay. (8) The method according to embodiment 6, wherein the estimated excitation delay includes the respective sinus rhythm conduction delay. (9) The method according to embodiment 5, wherein the path represents the shortest path such that the sum of the weights associated with the edges along the path is minimized. (10) The method according to Embodiment 9, wherein each of the weights represents an estimate of the excitation delay between pacing sites connected by the respective edges, the excitation delay being one of sQRS delay or sinus rhythm conduction delay.

[0097] (11) The generation of the pseudo-excitation map relating to the identified core region is: Each generates multiple pseudo-excitation maps relating to pacing sites associated with the identified core region, Combining the aforementioned multiple pseudo-excitation maps, The method according to Embodiment 1, including the method described above. (12) A system for visualizing reentry circuits within the heart using a pseudo-excitation map, At least one processor, A memory for storing instructions, wherein when an instruction is executed by the at least one processor, the system... The process involves receiving sets of ECG data and their respective pacing sites, wherein each set is generated by pacing cardiac tissue at each of the pacing sites. The calculation of correlation gradients representing morphological changes between sets of ECG data for each adjacent region of the pacing region, Based on the correlation gradient, the core region related to the re-entry circuit is identified, To generate a pseudo-excitation map with respect to the identified core region, To execute, memory and A system that includes these features. (13) The instruction shall be directed to the system The system according to embodiment 12, further comprising generating a 3D surface mesh based on the received pacing portions, wherein the pacing portions form nodes in a graph, and pairs of the pacing portions are connected by edges in the graph. (14) The calculation of the correlation gradient is The system according to Embodiment 12, comprising calculating one of the correlation gradients with respect to the edges of the 3D surface mesh based on morphological changes between sets of ECG data of each pacing region connected by the edges. (15) The system according to embodiment 14, wherein the 3D surface mesh includes edges having respective correlation gradients below a threshold.

[0098] (16) The generation of the pseudo-excitation map relating to the identified core region is: The system according to Embodiment 12, comprising calculating a pseudo-excitation value with respect to the pacing region, each of which represents a progression delay along a path, the path extending from the pacing region associated with the identified core region through the edges of the 3D surface mesh to each of the pacing regions. (17) The system according to Embodiment 16, wherein the progression delay represents a cumulative delay including an estimate of the excitation delay of each edge of the 3D surface mesh along the path. (18) The system according to embodiment 17, wherein the estimated excitation delay includes the respective sQRS delays. (19) The system according to embodiment 17, wherein the estimated excitation delay includes the respective sinus rhythm conduction delay. (20) The system according to embodiment 16, wherein the path represents the shortest path such that the sum of the weights associated with the edges along the path is minimized.

[0099] (21) The system according to Embodiment 20, wherein each of the weights represents an estimate of the excitation delay between pacing sites connected by the respective edges, the excitation delay being one of sQRS delay or sinus rhythm conduction delay. (22) A non-temporary computer-readable medium comprising instructions executable by at least one processor for performing a method of visualizing reentrant circuits within the heart by a pseudo-excitation map, wherein the method is The process involves receiving sets of ECG data and their respective pacing sites, wherein each set is generated by pacing cardiac tissue at each of the pacing sites. The calculation of correlation gradients representing morphological changes between sets of ECG data for each adjacent region of the pacing region, Based on the correlation gradient, the core region related to the re-entry circuit is identified, To generate a pseudo-excitation map with respect to the identified core region, Non-temporary computer-readable media, including [specific examples of such media].

Claims

1. A system for visualizing reentry circuits within the heart using a pseudo-excitation map, At least one processor, A memory for storing instructions, wherein when an instruction is executed by the at least one processor, the system... A plurality of sets of ECG data, each of which is generated by pacing cardiac tissue at each of a plurality of pacing sites within the heart, and receiving a plurality of sets of ECG data. The process involves calculating a correlation gradient representing the morphological changes between sets of ECG data corresponding to adjacent pacing sites among the plurality of pacing sites, Based on the correlation gradient, the core region related to the re-entry circuit is identified, To generate a pseudo-excitation map with respect to the identified core region, To execute, memory and Equipped with, The aforementioned instruction is, The method further includes generating a 3D surface mesh based on the received plurality of pacing regions, wherein each pacing region forms a node in a graph, and pairs of adjacent pacing regions are connected by edges in the graph. The generation of the pseudo-excitation map relating to the identified core region is as follows: This involves calculating a pseudo-excitation value for each of the plurality of pacing regions, each of which represents a progression delay along a path, the path extending from a pacing region associated with the identified core region through the edge of the 3D surface mesh to each of the plurality of pacing regions, The aforementioned path represents the shortest path such that the sum of the weights associated with the edges along the path is minimized. Each of the aforementioned weights represents an estimate of the excitation delay between adjacent pacing sites connected by their respective edges, and the excitation delay is one of either the sQRS delay or the sinus rhythm conduction delay. The pseudo-excitation value is the sum of the weights along the shortest path in the system.

2. The calculation of the correlation gradient is as follows: The system according to claim 1, comprising calculating the correlation gradient based on morphological changes between sets of ECG data of each pacing region connected by the edge.

3. The system according to claim 2, wherein the 3D surface mesh includes edges having respective correlation gradients below a threshold.

4. A non-temporary computer-readable medium comprising instructions executable by at least one processor for performing a method of visualizing reentry circuits within the heart by a pseudo-excitation map, wherein the method is: A plurality of sets of ECG data, each of which is generated by pacing cardiac tissue at each of a plurality of pacing sites within the heart, and receiving a plurality of sets of ECG data. The process involves calculating a correlation gradient representing the morphological changes between sets of ECG data corresponding to adjacent pacing sites among the plurality of pacing sites, Based on the correlation gradient, the core region related to the re-entry circuit is identified, To generate a pseudo-excitation map with respect to the identified core region, Includes, The aforementioned method, The method further includes generating a 3D surface mesh based on the received plurality of pacing regions, wherein each pacing region forms a node in a graph, and pairs of adjacent pacing regions are connected by edges in the graph. The generation of the pseudo-excitation map relating to the identified core region is as follows: This involves calculating a pseudo-excitation value for each of the plurality of pacing regions, each of which represents a progression delay along a path, the path extending from a pacing region associated with the identified core region through the edge of the 3D surface mesh to each of the plurality of pacing regions, The aforementioned path represents the shortest path such that the sum of the weights associated with the edges along the path is minimized. Each of the aforementioned weights represents an estimate of the excitation delay between adjacent pacing sites connected by their respective edges, and the excitation delay is one of either the sQRS delay or the sinus rhythm conduction delay. The pseudo-excitation value is the sum of the weights along the shortest path, in a non-temporary, computer-readable medium.

5. A method in which a processor executes a program to visualize the reentrant circuits within the heart using a pseudo-excitation map, A plurality of sets of ECG data, each of which is generated by pacing cardiac tissue at each of a plurality of pacing sites within the heart, and receiving a plurality of sets of ECG data. The process involves calculating a correlation gradient representing the morphological changes between sets of ECG data corresponding to adjacent pacing sites among the plurality of pacing sites, Based on the correlation gradient, the core region related to the re-entry circuit is identified, To generate a pseudo-excitation map with respect to the identified core region, Includes, The method further includes generating a 3D surface mesh based on the received plurality of pacing regions, wherein each pacing region forms a node in a graph, and pairs of adjacent pacing regions are connected by edges in the graph. The generation of the pseudo-excitation map relating to the identified core region is as follows: This involves calculating a pseudo-excitation value for each of the plurality of pacing regions, each of which represents a progression delay along a path, the path extending from a pacing region associated with the identified core region through the edge of the 3D surface mesh to each of the plurality of pacing regions, The aforementioned path represents the shortest path such that the sum of the weights associated with the edges along the path is minimized. Each of the aforementioned weights represents an estimate of the excitation delay between adjacent pacing sites connected by their respective edges, and the excitation delay is one of either the sQRS delay or the sinus rhythm conduction delay. The method wherein the pseudo-excitation value is the sum of the weights along the shortest path.

6. The calculation of the correlation gradient is as follows: The method according to claim 5, comprising calculating the correlation gradient based on morphological changes between sets of ECG data of each pacing region connected by the edge.

7. The method according to claim 6, wherein the 3D surface mesh includes edges having respective correlation gradients below a threshold.

8. The generation of the pseudo-excitation map relating to the identified core region is as follows: Each generates multiple pseudo-excitation maps relating to pacing sites associated with the identified core region, Combining the aforementioned multiple pseudo-excitation maps, The method according to claim 5, including the method described in claim 5.

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