Cardiac diagnostic system

Abstract

Systems, devices, and methods for performing cardiac diagnostic procedures are provided herein. A system for locating a segment of a cardiac conduction pathway in a patient includes a plurality of sensors disposed on a surface of a body of the patient; a data collection system to collect sensor data from the plurality of sensors; a data processing system to calculate CEA data from the sensor data; and a display system for presenting a three-dimensional model of positioning of the segment of the cardiac conduction pathway based on the calculated CEA data.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 511,783, filed July 3, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein generally relate to methods, systems, and apparatuses for: (1) imaging organ conduction pathways such as cardiac conduction pathways; (2) directing a therapeutic device to such organ conduction pathways; and (3) determining whether the therapeutic device has used an organ conduction system pathway. Background Technology

[0004] Normal cardiac function depends on the delivery of contractile-triggered electrical impulses to cardiomyocytes according to a well-defined spatiotemporal pattern known as "sinus rhythm." This pattern is maintained by cardiac conduction pathways (sometimes referred to as the "conduction system" in this text), such as... Figure 1 As shown. The electrical signal propagating through the cardiac conduction pathway begins in the sinoatrial (“SA”) node and then travels around the right atrium to the left atrium and atrioventricular (“AV”) node. In the AV node, the signal pauses before propagating through the His bundle, then through the left bundle branch (“LBB”) and right bundle branch (“RBB”)—both located in the septum between the ventricles—and then through the branching Purkinje fibers before extending across the ventricles. Figure 2 As shown, this electrical activity in the heart produces a detectable electric field, such as that detected via electrodes on the skin surface in the form of a spectrum called an electrocardiogram (“ECG”). The ECG shows how the electric field strength fluctuates during each heartbeat. When detected, for example, as the difference between the voltages detected on electrodes in the left and right arms, the electric field generated in a single heartbeat displays a characteristic spectrum of “waves” labeled / named by the letters P, Q, R, S, and T. Each wave spans a segment of the ECG spectrum with a peak. It is common for both the segment and the peak to be referred to by the same letter. The P segment, or “P wave,” corresponds to the propagation of electrical activity across the surface of the atria. The QRS complex contains the Q wave, R wave, and S wave and corresponds to the propagation of electrical activity across the surface of the ventricles. The T wave corresponds to ventricular repolarization.

[0005] The wave mentioned above refers to segments of the cardiac cycle called "segments," which begin at the end of one wave and end at the beginning of another. For example, the PR segment begins at the end of the P wave and ends before the beginning of the Q wave. Figure 2It is sometimes useful to divide the cardiac cycle into segments called "intervals." Some intervals begin at the peak of one wave and end at the peak of another. For example, the RR interval begins at the peak of the R wave of the first heartbeat and ends at the peak of the R wave of the subsequent heartbeat. Some intervals begin at the beginning of one wave and end at the beginning of the next wave. For example, the PR interval begins at the beginning of the P wave and ends at the beginning of the Q wave.

[0006] Diseases can disrupt cardiac conduction pathways and thus disrupt sinus rhythm, leading to decreased cardiac output and morbidity and mortality associated with reduced oxygen delivery to the body. For example, such disruptions in conduction pathways can cause irregular heartbeats (“arrhythmias”), which, if left untreated, can reduce cardiac output and lead to serious conditions such as heart failure. Arrhythmias (such as tachycardia) affect heart rate. The most common types of tachycardia (e.g., a heart rate exceeding 100 bpm) are atrial fibrillation (“AF”) and ventricular tachycardia (“VT”). Another type of arrhythmia, bradycardia (e.g., a heart rate below 50 bpm), may be caused by sinoatrial node dysfunction or AV block.

[0007] To alleviate cardiac arrhythmias, many patients receive implantable devices that pace the heart's electrical system to induce a sinus rhythm. For example, an implantable cardioverter defibrillator (“ICD”) can be used to prevent tachycardia, while a pacemaker (“PPM”) can be used to prevent bradycardia (and sometimes tachycardia). The pacing leads (implantable leads or wires containing one or more electrodes) of these devices are wired to at least one of three locations in the heart to deliver precisely timed electrical pulses. These locations are typically the right atrium (“RA”), the right ventricle (“RV”), and sometimes the left ventricle (“LV”). The electrical pulses “pac” the heart's electrical system and restore the heart to a normal sinus rhythm.

[0008] PPM and ICD pacing leads are typically delivered percutaneously to the heart via the neck. Clinicians use fluoroscopy to guide the pacing lead through the patient's vein to the target. Typically, most ventricular leads are placed in conventional target locations, such as the inferior septum, ventricular apex, and / or free ventricular wall, because these placements are generally easy to achieve. That is, clinicians can quickly navigate to these targets using routine scanning techniques (e.g., fluoroscopy, echocardiography, MRI / CT images, etc.). However, long-term follow-up studies have shown that these routine placements may lead to ventricular dyssynchrony, myocardial perfusion defects, heart failure, and / or other arrhythmias.

[0009] Alternatively, “conduction system pacing,” also known as “physiological pacing,” utilizes the working portion of the heart’s conduction pathway to restore essentially normal electrical propagation and thus return the heart to a more normal rhythm. Compared to pacing at other locations on the heart, conduction system pacing improves ventricular synchronization and increases cardiac output. Ideal targets for conduction system pacing (“CSP”) are the His bundle, LBB, and / or RBB.

[0010] Unfortunately, reaching these pacing lead targets can be challenging. Specifically, endocardial placement is difficult because the diaphragm and bundle cannot be detected directly and / or in a timely manner using conventional methods such as fluoroscopy or echocardiography (the primary methods for imaging tissues within a patient's body during surgery). For example, fluoroscopy cannot detect the cardiac conduction system or directly visualize the diaphragm; echocardiography has too low resolution to distinguish the diaphragm from the rest of the heart and cannot detect the cardiac conduction pathway. Similarly, MRI / CT imaging of the cardiac conduction system requires contrast dyes, is performed only externally, cannot be done in real time, and is rarely available in catheter insertion laboratories (also known as "catheterization labs"). Therefore, despite being considered the preferred lead placement configuration, current conduction system lead placement still requires trial and error, involves extended procedure times, and is typically performed only by the most skilled electrophysiologists. For example, compared to right ventricular pacing, conduction system pacing increases procedure time by 27% and fluoroscopy time by 39%.

[0011] Furthermore, other existing cardiac mapping systems typically cannot visualize the cardiac conduction system and / or diaphragm directly in real time or with sufficient accuracy to guide and attach pacing leads to target locations for conduction pacing. For example, electroanatomical mapping (“EAM”) systems are not designed to identify concentrated electrical signals through the His bundle, LBB, RBB, and Purkinje fibers. Instead, commercially available EAM systems are optimized for creating histological maps and visualizing wavefront propagation across the atrial and ventricular walls. This non-fluorescent fluoroscopic mapping approach is typically based on tracking and locating the tip of the mapping catheter using activation sequences combined with catheter-recorded electrical activity. Standard methods for developing such maps can take an hour or more and require repeated placement of sensing electrodes at different sites on the heart to collect data over several cardiac cycles. This extended time spent mapping a patient's heart (compared to conventional navigation methods) adds considerable cost to the procedure and can be detrimental to patients who may not be physically able to tolerate such prolonged procedures. Another EAM technique uses a non-contact 64-electrode basket catheter placed inside the heart to map multiple points inside the ventricle. However, mapping a patient's heart using such techniques cannot be performed in real time and / or with sufficient accuracy for conduction system pacing. These techniques are optimized to locate broad wavefronts propagating above the surface of the ventricle by interpolating the wavefront based on the timing of signals received or calculated for different points along the surface. Because this type of interpolation is not well-suited for mapping narrow paths that may be missed due to the placement of sensing electrodes, some clinicians (referred to herein as "implanters") have resorted to detailed, time-consuming multi-electrode mapping of the ventricular septal crest.

[0012] Another EAM method, often referred to as iEAM, reconstructs cardiac electrical activity from recorded surface potentials using an inverse algorithm. These potentials are collected from a large number (e.g., 64 or 182) of surface electrodes whose three-dimensional (“3D” positioning must be known with reasonable accuracy) and projected onto a patient-specific chest and cardiac model obtained from MRI or CT data. However, even iEAM maps are insufficient to accurately locate the His bundle, LBB, RBB, and / or other segments of the cardiac conduction system. Therefore, the EAM / iEAM method has been used to help pre-plan ablation procedures for ventricular tachycardia, atrial flutter, and atrial tachycardia, but not for real-time visualization during surgical procedures.

[0013] Other techniques used to diagnose cardiac dysfunction include vectorcardiography (“VCG”) and “CineECG”. VCG is calculated based on ECG signals received from electrodes precisely positioned at specific, predefined anatomical locations on the patient's body. The intensity of each signal is used to weight the sum of the vectors from the center of the heart to the electrode, resulting in a motion vector based on the center of the heart. The tip of this motion vector traces a spatial curve that can be used to detect acute myocardial infarction, right ventricular hypertrophy, and Tawar arm block. CineECG integrates the changes in VCG over time to produce an estimate of the motion of the mean location of electrical activation, also known as the mean time-space isochron (“iTSI”).

[0014] These current methods typically require the collection of MRI or CT data and integration into an analysis system to generate useful data. None of these methods can generate sufficiently accurate and / or timely maps of the septum, His bundle, LBB, RBB, Purkinje fibers, and / or other central segments of the cardiac conduction system / pathway to provide real-time guidance of the pacing lead to the endocardial target. Therefore, conduction system pacing cannot be readily achieved using these EAM methods.

[0015] For these and other reasons, there is a need for improved systems, devices, and methods to accurately image the diaphragm, His bundle, RBB, LBB, and / or other segments of the electrical conduction system in real time to diagnose cardiac conditions and / or guide instruments and / or leads to endocardial targets. Summary of the Invention

[0016] According to one aspect of this disclosure, the technology described herein relates to a system for mapping at least a portion of a cardiac conduction pathway in a patient, the system comprising: a plurality of sensors; a data collection system for collecting sensor data from the plurality of sensors; a data processing system for calculating center of electrical activity (CEA) data based on the sensor data; and a display system for presenting a three-dimensional (3D) model of a portion of the cardiac conduction pathway based on the calculated CEA data.

[0017] In some respects, the techniques described herein relate to a system in which the CEA data is determined by calculating a single equivalent dipole (SED).

[0018] In some respects, the technology described herein relates to a system in which the 3D model of the portion of the cardiac conduction pathway is displayed relative to an image of the gross anatomy of the heart.

[0019] In some respects, the technology described herein relates to a system in which the plurality of sensors detect signals propagating through segments of the cardiac conduction pathway during the PR segment of the cardiac cycle.

[0020] In some respects, the technology described herein relates to a system in which the display system shows the portion of the cardiac conduction pathway located between the atrioventricular (AV) node and Purkinje fibers in the heart.

[0021] In some respects, the technology described herein relates to a system in which the data collection system includes a digital converter for generating high-resolution data from extremely low voltage signals sensed by the plurality of sensors.

[0022] In some respects, the techniques described herein relate to a system in which the voltage signal is less than 0.1 mV.

[0023] In some respects, the techniques described herein relate to a system in which the data processing system enhances the sensor data by one or more of the following: a low-pass filter; a high-pass filter; common-mode rejection; and / or differential weighting of data from different sensors among the plurality of sensors.

[0024] In some respects, the technology described herein relates to a system that further includes a sensor localization system for identifying the location of each of the plurality of sensors in 3D space.

[0025] In some respects, the techniques described herein relate to a system in which the sensor positioning system includes a scanner, CT scanner, and / or MRI machine configured to generate 3D images.

[0026] In some respects, the technology described herein relates to a system in which a machine learning algorithm trained to identify sensors in a 3D image identifies and locates the plurality of sensors in the 3D image generated by the sensor localization system.

[0027] In some respects, the technology described herein relates to a system that further includes clothing and / or straps for housing the plurality of sensors.

[0028] In some respects, the technology described herein relates to a system in which the data collection system is configured to indicate to a user whether a particular sensor among the plurality of sensors is improperly located and / or malfunctions.

[0029] In some respects, the technology described herein relates to a system in which the data collection system is configured to cause one or more of the plurality of sensors to emit one or more signals.

[0030] In some respects, the techniques described herein relate to a system configured to filter sensor data by applying different wideband pass filters and / or narrowband pass filters to selected frequencies.

[0031] In some respects, the technology described herein relates to a system in which the selected frequency is between about 0.5 Hz and 55 Hz or between about 65 Hz and 300 Hz.

[0032] In some respects, the technology described herein relates to a system in which the data processing system is configured to remove CEA data corresponding to a specific time point in time where the voltage is below a threshold.

[0033] In some respects, the techniques described herein relate to a system in which each of the plurality of sensors is weighted when determining the CEA based on the voltage drop across at least one chordae tendineae among the sensors.

[0034] In some respects, the techniques described herein relate to a system in which the location of a portion of the cardiac conduction pathway is determined by combining CEA data from multiple cardiac cycles.

[0035] In some respects, the technique described herein relates to a system in which combined CEA data from multiple cardiac cycles take into account differences caused by cardiac motion during each cardiac cycle.

[0036] In some respects, the techniques described herein relate to a system in which the combined CEA data includes combining CEA data from the plurality of cardiac cycles into a best-fit model.

[0037] In some respects, the technology described herein relates to a system, wherein the system is further configured to use data collected from a transmitting device to determine the location of the septum of a patient's heart.

[0038] In some respects, the technology described herein relates to a system in which the positioning of the diaphragm constrains the best-fit model of the cardiac conduction pathway.

[0039] In some respects, the technology described herein relates to a system for navigating a catheter to a target on the diaphragm.

[0040] In some respects, the technology described herein relates to a system in which the probability distribution of the location of the cardiac conduction pathway is graphically displayed on an image of the diaphragm.

[0041] In some respects, the technology described herein relates to a system that further includes a transmitting device, wherein the data processing system is configured to determine the positioning of the transmitting device relative to a portion of the cardiac conduction pathway.

[0042] In some respects, the technology described herein relates to a system that further includes a control device connected to the proximal end of the transmitting device.

[0043] In some respects, the technology described herein relates to a system in which the control device activates and / or controls signals emitted by the transmitting device.

[0044] In some respects, the technology described herein relates to a system in which the control device controls the movement of the transmitting device.

[0045] In some respects, the technology described herein relates to a system that further includes a transmitting device, wherein the data processing system is configured to determine the orientation of the transmitting device relative to the location of the portion of the cardiac conduction pathway.

[0046] In some respects, the technology described herein relates to a system in which the location of said portion of the cardiac conduction pathway is determined before and after the administration of therapy to said patient.

[0047] In some respects, the technology described herein relates to a system in which the data processing system enhances the sensor data by selecting a filter based on whether the implanted pacing lead has recently delivered a pacing signal.

[0048] In some respects, the technology described herein relates to a system that is also used to navigate a catheter to a target in the heart.

[0049] In some respects, the technology described herein relates to a system that can also be used to develop pacing strategies for patients.

[0050] In some respects, the techniques described herein relate to a system that can also be used to determine whether conduction system pacing has been achieved.

[0051] In some aspects, the technology described herein relates to a method for mapping at least a portion of a cardiac conduction pathway, the method comprising: sensing signals indicating cardiac electrical signals propagating through the cardiac conduction pathway via sensors; combining the signals from each sensor via a data collection system to generate a first data stream; identifying waveforms from the first data stream via a data processing system through low-resolution sampling of the first data stream; sampling segments of the identified waveforms from the first data stream at high resolution via the data processing system to generate a second data stream; determining center of electrical activity (CEA) data based on the second data stream via the data processing system; and generating a three-dimensional (3D) model of the CEA data in real time via a display system, wherein the 3D model indicates the cardiac conduction pathway.

[0052] In some respects, the techniques described herein relate to a method in which determining the CEA data includes performing a single equivalent dipole (SED) analysis on the second data stream.

[0053] In some respects, the techniques described herein relate to a method that further includes generating a real-time image of the heart, and overlaying the 3D model onto the real-time image of the heart.

[0054] In some respects, the techniques described herein relate to a method in which the identified waveform is an electrocardiogram including P waves, QRS complexes, and T waves.

[0055] In some respects, the techniques described herein relate to a method in which the segment of the identified waveform is a PR segment of the identified waveform.

[0056] In some respects, the techniques described herein relate to a method that further includes determining the location of the sensor relative to the heart of a subject.

[0057] In some respects, the techniques described herein relate to a method in which determining the location of the sensor includes scanning the subject and the sensor, wherein the sensor is disposed on the subject's body.

[0058] In some respects, the techniques described herein relate to a method in which the determination of CEA data is further based on the determined positioning of the sensor.

[0059] In some respects, the technology described herein relates to a method that further includes sensing a monopole signal from a transmitting device placed within the heart of a subject via the sensor.

[0060] In some respects, the techniques described herein relate to a method that further includes using SED analysis via the processing system to determine the location and orientation of the transmitting device.

[0061] In some respects, the technology described herein relates to a method that further includes generating a 3D representation of the transmitting device relative to the 3D model indicating the cardiac conduction pathway via the display system, wherein the 3D representation indicates the positioning and orientation of the transmitting device in real time.

[0062] In some respects, the technology described herein relates to a method that further includes navigating the transmitting device such that the transmitting device approaches a desired portion of the cardiac conduction pathway based on the 3D representation.

[0063] In some respects, the techniques described herein relate to a method that further includes a detection electrode being placed close to the cardiac conduction pathway for capture.

[0064] Details of one or more aspects of this disclosure are set forth in the following drawings and description. Other features, objectives, and advantages of the technology described in this disclosure will be apparent from the description, drawings, and claims. Attached Figure Description

[0065] The foregoing and other features and advantages of this disclosure will become apparent from the following description of embodiments illustrated in the accompanying drawings. The accompanying drawings, which are incorporated herein and form a part of this specification, further explain the principles of this disclosure and are intended to enable those skilled in the art to make and use embodiments of this disclosure. The drawings are not drawn to scale.

[0066] Figure 1 This shows an anatomical cross-sectional view of a mammalian heart.

[0067] Figure 2 A graph showing the ECG signal in a mammalian patient is presented.

[0068] Figure 3 A schematic diagram of a system for performing medical procedures according to an embodiment is shown.

[0069] Figure 4A These are images of a sensor array attached to a pig subject, the images being derived from a digital 3D scan of the subject.

[0070] Figure 4B A front view and a rear view of a sensor array attached to a human subject according to an embodiment are depicted.

[0071] Figure 5 This is a flowchart illustrating a method for collecting data according to an embodiment.

[0072] Figure 6 This is a flowchart illustrating a method for processing a data stream according to an embodiment.

[0073] Figure 7 This is a flowchart illustrating a data processing method according to an embodiment.

[0074] Figure 8 This is a flowchart illustrating a data collection and processing method according to an embodiment.

[0075] Figure 9A and Figure 9B It is a plot of data obtained from one or more cardiac cycles according to an embodiment.

[0076] Figure 9C A spatial curve graphical representation of a segment of the cardiac conduction pathway according to an embodiment is depicted.

[0077] Figures 10A to 10C A spatial curve graphical representation according to an embodiment is depicted, which shows the propagation of a signal along a segment of the cardiac conduction pathway.

[0078] Figure 11 This is a hardware block diagram of a computing device according to an embodiment. Detailed Implementation

[0079] Reference will now be made in detail to current embodiments of the present technology, examples of which are illustrated in the accompanying drawings. Throughout this disclosure, the same reference numerals are used to identify the same elements. The following description is not intended to limit this disclosure to the specific embodiments, and it should be construed as including various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0080] The techniques presented in this paper enable real-time visualization of cardiac conduction pathways for diagnostic and / or surgical purposes. For example, the techniques provided here offer target localization for conduction system pacing (sometimes referred to as “physiological pacing”) and guide implanters to these locations. These techniques enable a more diverse range of clinicians to perform procedures quickly and accurately, and improve the efficacy of the subjects (sometimes referred to as “patients” in this paper). Specifically, the techniques provided here offer real-time or near-real-time (e.g., within 100 milliseconds (“ms”)) 3D mapping of the cardiac conduction system (such as the SA node, AV node, His bundle, LBB, and / or RBB) for real-time visualization of the navigation or guidance of target localization within the heart (e.g., on the diaphragm). Therefore, clinicians can quickly and easily navigate to the diaphragm (compared to conventional techniques) and attach pacing leads for conduction system pacing (“CSP”) to the desired location thereon.

[0081] For example, the techniques proposed herein include using single equivalent moving dipole (“SEMD”) analysis to locate moving electrical activation centers (“MCEAs”) within the cardiac conduction system. MCEAs can represent electrical signals as they travel along the cardiac conduction pathway (e.g., from the SA node, through the AV node, His bundle, LBB, RBB, and ultimately through Purkinje fibers). SEMD analysis of electrocardiogram (ECG) data is a method for analyzing the spectrum of electrical signals originating from the heart (e.g., the cardiac conduction system) and reaching electrodes on the body surface. As electrical signals propagate along the cardiac conduction pathway at a series of time points corresponding to the ECG waveform, the method estimates the location (in 3D coordinates) and torque (in 3D direction vectors) of the single equivalent dipole (“SED”) of the electrical signal. Specifically, the series of time points corresponds to desired intervals or segments, such as the P-to-R peak interval of the ECG waveform, and in some embodiments, the series of time points may span the PR segment (see [link to documentation]). Figure 2 The P-to-R peak interval begins at the peak of the P wave and ends at the peak of the R wave. In some implementations, the desired segment for SEMD analysis may be the PR segment. The PR segment may begin at the end of the P wave and end at the beginning of the Q wave or QRS complex. In the absence of a detected Q wave, the desired segment may extend from the end of the P wave to the beginning of the R wave.

[0082] Additionally, SEMD data can be used to locate the origin of the abnormal electrical excitation in the heart that causes ventricular tachycardia (VT). For example, myocardial scarring caused by infarction can preserve a chain of electrically active cells, allowing activating waves that have passed through the ventricle to return and stimulate new activating waves—causing additional “beats.” The “origin” of the arrhythmia is where such an abnormal pathway (also known as a “re-entry loop”) exits the scar. When based on high temporal and data resolution data, SEMD can reveal the location of this “origin.” If the origin re-excites the ventricle after the main wave of electrical activity (e.g., the QRS complex) has passed (i.e., during the low-voltage ST segment), this location is directly revealed by SEMD. Otherwise, when the signal from the re-entry is overwhelmed by the signal from the QRS complex or T wave, the origin can be determined by applying suprathreshold electrical stimulation to the heart at the same rate as ventricular tachycardia using a catheter with an electrode tip, and then looking for changes in the path and torque orientation of SEMD based on high-resolution data.

[0083] SEMD can also be used to detect the location and orientation (relative to the cardiac conduction pathway) of transmitters (e.g., electrodes) placed near or inside the heart: dipole signals emitted from bipolar ablation electrodes can be juxtaposed with SEMD data in “image space”, allowing, for example, an ablation catheter to be navigated to the origin of tachycardia on the epicardial or endocardial surface of the ventricular free wall, whereby the origin can be ablated. Similarly, if dipole signals are emitted by a pacing lead or other sensors / electrodes near the pacing lead (e.g., on a catheter used to place the pacing lead), the location and / or orientation of the pacing lead can be juxtaposed with the MCAD (e.g., SEMD) of the cardiac conduction system (e.g., 3D drawing of the His bundle and / or LBB and / or RBB).

[0084] However, conventional systems typically cannot extract useful information from the PR segment (the segment of the ECG waveform that begins at the end of the P wave and ends at the beginning of the Q wave or QRS complex), where the amplitude of the ECG signal is relatively low compared to the noise level of the signal. Therefore, signals from the PR segment are almost never examined or used, except occasionally as part of “normalizing” the transbeat rhythm data and / or as a baseline for estimating beat noise. Furthermore, conventional systems using traditional SEMD analysis cannot accurately locate the AV junction, His bundle, LBB, RBB, or Purkinje fibers in the cardiac conduction pathway in real time using data from the low-amplitude signals generated during the PR segment.

[0085] Using the techniques presented herein, SEMD analysis of high temporal resolution data from the PR segment can identify, locate, and / or trace the propagation of the MCEA along the AV junction, His bundle, LBB, RBB, and Purkinje fibers, and / or other parts of the cardiac conduction pathway. Generally, as used herein, the term "high temporal resolution" refers to a sampling rate greater than 500 Hz, and the term "high data resolution" refers to more than 16 bits of data per sample. The term "low temporal resolution" refers to a sampling rate less than or equal to 500 Hz, and the term "low data resolution" refers to less than or equal to 16 bits of data per sample.

[0086] Therefore, the techniques proposed herein detect, locate (e.g., in 3D space) and display cardiac signals propagating from the AV junction and through the His bundle, LBB, RBB, and / or Purkinje fibers in real time (e.g., within 100 ms, 50 ms, and / or preferably within 10 ms) during the PR segment of the cardiac cycle. These techniques can be used with a variety of human and non-human patients. The PR segment of a cardiac waveform corresponds to the time period during which the wavefront of a cardiac electrical signal or pulse propagates from the AV junction through the His bundle, LBB, RBB, and Purkinje fibers. According to at least one embodiment proposed herein, the low-amplitude signal generated during the PR segment can be used to calculate the MCEA using SEMD analysis to locate the His bundle, LBB, RBB, and Purkinje fibers and / or portions thereof in the cardiac conduction system.

[0087] Now for reference Figure 3 The diagram illustrates a system 10 for detecting and displaying conduction pathways in mammals according to an embodiment. System 10 includes a data collection system 100, a sensor positioning system 200, a data processing system 300, and a display system 400.

[0088] The data collection system 100 includes a converter 110, a collector 120, and one or more sensors 150. In some embodiments, the data collection system 100 includes a field-programmable gate array (“FPGA”) board. In some embodiments, the data collection system 100 includes a processor (such as CPU 40) or communicates with the processor via wired and / or wireless communication (e.g., WiFi, Zigbee, NFC, Bluetooth, etc.).

[0089] Data collection system 100 receives and processes data from signals emitted by sensor 150 (e.g., cleaning, sorting, prioritizing, filtering, removing outliers, and / or otherwise processing the data). Sensor positioning system 200 applies and positions sensor 150 on the outer surface of the patient's body or skin. Data processing system 300 processes the data received from data collection system 100 into MCEA data. Display system 400 generates and displays a 3D model of the MCEA data from data processing system 300 and may include a graphical user interface (“GUI”). The GUI may include input devices for receiving input from the user (e.g., keyboard, mouse, joystick, foot pedal, microphone, touchpad, etc.).

[0090] In the depicted embodiment, the converter 110 of the data collection system 100 converts the voltage of the electrical signal detected by each sensor 150 into a digital (e.g., binary) value. That is, the converter 110 may include one or more analog-to-digital (“AD”) converters. The converter 110 samples the voltage (e.g., an indicative heart voltage signal) from one or more sensors 150 at a rate that can be measured in Hertz (samples per second), converts the voltage signal into digital data, and transmits the data to the collector 120.

[0091] In some embodiments, converter 110 may include a plurality of converter components and / or devices configured to receive signals from sensor 150. For example, each converter component may receive one or more signals from one or more sensors 150 and convert the one or more signals into digital data. In some embodiments, converter 110 includes one or more analog-to-digital converters having high data resolution (e.g., 12, 16, 20, 24, 36, or 48-bit resolution).

[0092] In some cases, converter 110 may comprise one or more converters contained within a "receiving module" connected to sensor 150 (e.g., one converter may be connected to one to thirty-two or sixty-four sensors 150). That is, the receiving module may comprise multiple converters 110, each electrically coupled or connected to at least one sensor 150 via a conductor (e.g., wire). Each receiving module combines the outputs of the included converters 110 into a single partial data stream sent to collector 120. In some embodiments, the receiving module of data collection system 100 may be integrated into applicator 50, which will be discussed in detail below with reference to sensor positioning system 200.

[0093] Regardless of the structure of converter 110, data from converter 110 is transmitted to collector 120 to generate a raw data stream. That is, collector 120 assembles the data received or acquired from converter 110 into a single data stream (e.g., Figure 5 The data stream (DS1) is then used for subsequent processing. In some implementations, the data stream may be saved to a storage device.

[0094] Sensor 150 may include one or more ECG sensors, which include any of a variety of voltage sensing devices. In some embodiments, sensor 150 includes one, two, or more magnetic field detectors. Sensor 150 may include one or more devices that can be passive and / or active in one or more ways, such as by being powered and / or by being configured to transmit and receive signals (e.g., DC and / or AC signals). Sensor 150 is capable of sensing voltages from a patient or subject, such as voltages from the subject's heart. Sensor 150 is configured to attach to the patient's skin to electrically couple sensor 150 to the subject's skin. For example, sensor 150 may be secured to the subject's skin with an adhesive pad and / or pierced through the patient's skin surface. In some embodiments, one or more sensors 150 are concentrated in the area in which the SEMD torque is oriented during the PR segment. In some embodiments, sensor 150 is arranged radially around the torso and longitudinally between areas above and below the heart. In some cases, sensor 150 is placed on the left arm, right arm, left leg, and / or right leg to provide a reference signal. In some embodiments, a pair of sensors 150 are placed on the patient's skin at a location between the fourth and fifth ribs on the left and / or right side of the sternum. One or more sensors 150 are configured to electrically couple to a converter 110, sense voltage from the subject's body, and transmit the sensed signal to the converter 110.

[0095] In some implementations, the data collection system 100 may be further configured to determine whether an individual sensor 150 is functioning correctly, sometimes referred to herein as “sensor verification.” The sensor 150 may be connected to the data collection system 100 before or shortly after it is attached to the patient’s skin. The data collection system 100 may determine whether a signal is received from the sensor 150 and whether said signal has sufficiently high quality (e.g., above a predetermined threshold and / or a clinician-adjustable threshold). For example, the data collection system 100 may determine that the signal is weak, has a low signal-to-noise ratio, and / or does not reflect a spectrum of cardiac electrical activity detected by one or more other sensors 150. The signal used for sensor verification may be generated by the heart and / or by one or more other sensors 150 emitting one or more verification signals.

[0096] Still referencing Figure 3System 10 may include one or more central processing units (“CPU”) 40 (e.g., processors) and / or other computing components. CPU 40 and / or other components of system 10 may include one or more electronic elements, electronic assemblies, and / or other electronic components, such as components selected from the group consisting of: memory storage components; analog-to-digital converters; rectifier circuit systems; state machines; microprocessors; microcontrollers; filters and other signal conditioners; sensor interface circuit systems; converter interface circuits; and combinations thereof. CPU 40 further includes memory 45 containing instructions for system 10 to generate a 3D model of the cardiac conduction pathway. The instructions may further include guiding an instrument to an endocardial target using the generated 3D model. In some embodiments, each of the data acquisition system 100, sensor positioning system 200, data processing system 300, and / or display system 400 includes one or more CPUs 40 and / or communicates with CPUs 40. In some cases, memory 45 is external to one or more CPUs 40 and communicates with said one or more CPUs.

[0097] In the depicted embodiments, system 10 further includes an imager 80 and a transmitting device 90. The imager 80 (e.g., a fluorescence microscope, MRI, and / or CT device) images the interior of the patient. In some embodiments, the interior of the patient can be imaged before or during surgery. The imaging data can be used for reference purposes during the generation of a 3D model of the cardiac conduction pathway. Alternatively or additionally, the imager 80 can be used during surgery.

[0098] The transmitting device 90 (e.g., a catheter, electrode, and / or other device configured for insertion into the patient and for transmitting electrical signals) tracks the positioning of the pacing lead relative to a target in the cardiac conduction system, and / or its general positioning within the subject's heart and body. The proximal end of the transmitting device 90 may include control devices for controlling movement of the transmitting device 90 (e.g., the distal end of the transmitting device) and / or controlling the signals transmitted by the transmitting device 90. In some embodiments, the transmitting device 90 includes an epicardial walker, an endoscope, and / or catheters such as an electrophysiological sensing catheter, a lead placement catheter, and / or another catheter for navigation inside and / or outside the heart. However, the transmitting device 90 can be any device used in vivo to move around, within, and / or on the heart, and can be internally positioned on and / or near the patient's heart to transmit electrical signals through tissue.

[0099] In some embodiments, the distal end of the transmitting device 90 includes one or more electrodes configured to emit a monopolar, bipolar, or multipolar electric field. In some embodiments, each such electrode is electrically coupled to a signal generator 91. In some cases, the signal generator 91 is located near or electrically coupled to the proximal end of the transmitting device 90. In some embodiments, the transmitting device may be configured to sense an electric field in its vicinity. For example, the CPU 40 may instruct the signal generator 91 to temporarily shut down or change mode (e.g., for 1 millisecond, 10 milliseconds, or 100 milliseconds, or for 1 second, 10 seconds, or 60 seconds), causing the electrodes to become electrically coupled to the data collection system 100.

[0100] In some embodiments, the electrical signal emitted by the transmitting device 90 may be a 75 Hz, 90 Hz, 150 Hz, 200 Hz, 300 Hz, 500 Hz, 800 Hz, or 1600 Hz sine wave, or a combination of such sine waves. In some embodiments, the signal emitted by the device 90 may be fixed or variable, for example, at frequencies of 50 Hz, 65 Hz, 70 Hz, 75 Hz, 80 Hz, 90 Hz, 300 Hz, or 800 Hz. In some embodiments, a narrow-bandpass filter (e.g., of the data processing system 300) may be configured to isolate the signal emitted by the transmitting device 90. In some embodiments, the voltage level of the signal emitted by the transmitting device 90 is set to a level that does not affect the patient's heartbeat. In some embodiments, such a safe voltage level for the transmitting device 90 is determined for each individual subject.

[0101] Now for reference Figure 4A and Figure 4B However, continue to refer to Figure 3 Multiple sensors 150 are applied to the subject (e.g., Figure 4A Pigs in Figure 4B The sensor positioning system 200 determines the positioning of the sensors relative to the subject's body (the human torso). The sensor positioning system 200 may include an applicator 50 and / or a scanner 60. The applicator 50 can guide the positioning and spacing of each sensor 150 on the patient's body. For example, the applicator 50 can be used to attach the sensors 150 to the patient's body and / or otherwise position them on the patient's body. The applicator 50 includes one or more rigid or flexible supports that ensure the position of the sensors 150 relative to each other and the patient's body.

[0102] like Figure 4AAs shown in the embodiments, the applicator 50 may be one or more linear strips 52. In the depicted embodiments, a plurality of sensors 150 are distributed along the strips 52 and maintained in desired positions relative to each other and to the body. The strips 52 may have adhesive surfaces for adhesion to the body of a subject. In some embodiments, the strips may be glued or adhesively bonded to the body. The strips may be formed from flexible polymer strips with regularly spaced holes. A button for a snap-on electrode (e.g., sensor 150) is pushed through a hole in the strip, and adhesive protective tape is removed from the bottom of the sensor 150.

[0103] In such Figure 4A In the depicted embodiment, a first sensor 150 on a first strip 52A is aligned with the pig's anterior clavicle and left shoulder to extend along the longitudinal axis of the pig's body. Once aligned, the first sensor 150 is adhered to or otherwise attached to the subject's skin. Subsequent sensors 150 are placed on the strip and adhered to or otherwise attached to the subject's body. Additional strips 52B, 52C with sensors 150 are similarly placed on the subject, wherein the sensors 150 are evenly distributed and symmetrically positioned around the subject's transverse circumference. Once the sensors 150 are positioned on and adhered to the body, the strip 52 can be removed. Thus, the sensors 150 are coupled to the pig's body in a desired arrangement and aligned along the pig's body. Although in Figure 4A Only three strips 52A, 52B, and 52C are visible in the data collection system 100, but in some embodiments, more than three strips 52 may be used. For example, eight strips 52 with sensor 150 may be radially aligned around the subject's body. In some embodiments, multiple leads may connect each sensor 150 to one or more components of the data collection system 100, such as converter 110, collector 120, and / or its sub-assemblies.

[0104] In some embodiments, the applicator 50 guides the positioning of one or more sensors 150 and a patch 54, the patch being able to adhere the sensor 150 to the subject's skin, such as... Figure 4B As depicted. In the depicted embodiment, four snap-on electrodes (e.g., sensors 150) are substantially evenly spaced and aligned anteriorly along the bottom of the subject's clavicle. Simultaneously, four evenly spaced sensors 150 are arranged posteriorly across the top of the scapula. Additional sensors 150 are longitudinally positioned below each of these top sensors 150. The applicator 50 may evenly space the additional electrodes along the longitudinal axis of the subject's body. Figure 4B In the illustrated embodiment, eight vertically or longitudinally extending columns, each having eight sensors 150, are arranged on the subject's body, for a total of sixty-four sensors 150.

[0105] In some embodiments, each column may include more than eight sensors or fewer than eight sensors. For example, each column may include four, six, eight, ten, twelve, fourteen, or sixteen sensors, for a total of 32, 48, 64, 80, 96, 112, or 128 sensors placed on the subject. Alternatively or additionally, more than eight columns or fewer of sensor 150 may be placed on the subject. In some embodiments, the applicator 50 may position the sensors using a non-linear pattern (e.g., a circular or square pattern).

[0106] In some embodiments, the applicator 50 may include one or more guides including markers for aligning the applicator 50 and sensor 150 with one or more anatomical landmarks. For example, the applicator 50 may be a ruler, tool, bracket, guide garment, suspender, and / or belt that can be worn over the torso and supports the placement of the sensor 150. The guide garment, suspender, and / or belt may be made wholly or partially of spandex or other elastic materials to expand and conform to the torso when worn by a patient. Regardless of the construction, the applicator 50 ensures that the sensor 150 is properly and predictably placed at the desired location on the subject's body. In some embodiments, confirmation of the applicator 50 is achieved by placing the sensor with sufficient accuracy such that additional positioning determination steps, such as scanning or sensor cross-signal transduction, are not included as part of the sensor positioning system 200.

[0107] Furthermore, in some embodiments, the applicator 50 may also have one or more markings indicating the location where the sensor 150 should be placed and / or identifying the location where a wire or waveguide from the data collection system 100 should be attached. The applicator 50 may also include an integrated transmission path (e.g., wires, circuitry, etc.) from the sensor 150 to reduce the number of separate wires connecting the sensor 150 to the data collection system 100. That is, the transmission path can be integrated into the applicator 50 to connect the sensor 150 to the data collection system 100. Figure 4B In the embodiment depicted, a data centralization module 152 for receiving leads coupled to sensor 150 is attached to belt 154. Each data centralization module 152 is coupled to eight leads from a corresponding sensor 150 in a corresponding vertical sensor column. That is, each column of sensors 150 is coupled to a corresponding data centralization module 152.

[0108] Each central module 152 includes a group cable with eight leads for coupling to the corresponding column of sensors 150. Each central module 152 may further include an “RX pod” with a multiplexed eight-channel data acquisition chip having a 24-bit ADC resolution specifically designed for ECG signals. The data acquisition chip may be coupled to an opto-isolator and a serializer to transmit the digitized signal to a field-programmable gate array (“FPGA”). The FPGA is capable of receiving data at 8,000 samples per second (“kSPS”). The high-resolution ADC isolates and extracts the extremely weak cardiac signal in the PR segment. The RX pod can represent... Figure 3 The converter 110. The RX container group is configured to output a data representation of the cardiac signal to collector 120 or another combination of system 10 for further data processing.

[0109] After sensor 150 is applied to the body, scanner 60 can determine the location of the sensor in two-dimensional (“2D”) or three-dimensional (“3D”) space. Scanner 60 can be any device capable of imaging the body and generating data for a 3D image. For example, scanner 60 can be an optical scanner, laser scanner, radio wave scanner, MRI imager, CT imager, fluorescence microscope, electromagnetic scanner, smartphone, tablet computer, and / or any other type of scanner capable of scanning 3D objects to generate data for a 3D model.

[0110] Regardless of the type of scanning device, scanner 60 scans the subject's body and the applied sensors 150 to generate data for a 3D model of the subject, the data indicating the positioning of the applied sensors 150. Scanner 60 may pass over / around the subject and / or the subject may move relative to scanner 60 to generate scan data for desired portions of the subject's body (e.g., the patient's torso). Scanner 60 may also generate a 3D model based on the scan data. In some embodiments, sensor positioning system 200, display system 400, and / or CPU 40 may communicate with scanner 60 and generate a 3D model based on the scan data.

[0111] Regardless of which component generates the 3D model based on the scan data from scanner 60, system 10 can use algorithms to automatically identify sensor 150 from the 3D model. For example, the processor and / or memory (e.g., CPU 40) coupled to scanner 60 may include pattern recognition algorithms and / or other algorithms configured to identify the location of sensor 150 in the 3D model. In some cases, scanner 60 may capture two or more images of the subject, such as from different angles, and the location of sensor 150 may be identified by algorithms, such as using stereo computing to determine the location of sensor 150. The algorithms may include pattern recognition algorithms and / or other algorithms configured to identify the location of sensor 150, such as machine learning, neural networks, and / or another artificial intelligence algorithm (referred to herein as "AI algorithms"). In some embodiments, a user may indicate, highlight, or select the location of sensor 150 based on the 3D model generated from the scan data, and in some embodiments, the algorithms may identify the 3D location of the sensor based on the user's indication / selection.

[0112] In some embodiments, the sensors 150 are located using an algorithm that includes a geometric mesh algorithm. In such embodiments, two or more sensors 150 are adapted (using gradient descent or other optimization or heuristic methods) to an oval cylinder or a more anatomically accurate 3D torso model. In some embodiments, one or more sensors 150 may be identified as being located at anatomical locations with specific corresponding positions on the torso model. The torso model can then be warped to best match one or more distances between the sensors 150. In some embodiments, the warping is constrained to maintain anatomical conformation.

[0113] In some embodiments, after the sensors 150 are placed, the distance between two or more sensors 150 is physically measured (e.g., by means of a tape measure, ruler, and / or other measuring device). In some embodiments, the distance may be determined based on the spacing guide inserted into the applicator 50. In some embodiments, the distance may be determined by having the first sensor 150 emit a signal of known intensity (e.g., a sine wave with known frequency and amplitude), having at least a second sensor 150 detect the intensity of the signal received from the first sensor 150, and then calculating the distance between the sensors 150 based on the decrease in signal intensity (e.g., based on a voltage drop using Ohm's law). This process may be repeated for each sensor 150 (in some embodiments, for a large number of sensor 150 pairs) to measure / calculate all locations of the sensors 150. Distance calculation may be improved if the patient's torso skin impedance is measured first. In some embodiments, skin impedance is calculated based on the decrease in signal intensity between two sensors 150 that are at a known distance. Other embodiments use alternative methods to locate the sensors.

[0114] In some embodiments, a 3D “standard” torso model is used to determine the positioning of sensor 150. The “standard” torso model may be adjusted, distorted, and / or otherwise fitted based on one or more measurements of the subject / patient's torso to create an “adjusted torso model.” Sensor 150 is positioned at a specific location on the patient's torso relative to an anatomical reference point (in some embodiments, an applicator 50 with one or more spaced guides is used). The 3D positioning of sensor 150 is then determined based on the positioning of the same reference point on the adjusted torso model.

[0115] In a consistent embodiment, the relative positioning of sensor 150 can be improved as follows. First, an initial estimate of positioning is determined based on one of the methods described herein. Then, each sensor 150 sequentially transmits a signal, such as a sine wave at 10 Hz, 20 Hz, 50 Hz, 100 Hz, and / or 1000 Hz. Next, the non-signal-transmitted sensor 150 uses a single equivalent dipole (“SED”) algorithm to calculate the electrical center of activity (“CEA”) of the signal-transmitted sensor 150. The calculated CEA then becomes a new estimate of the positioning of the signal-transmitted sensor 150. After this process has been completed for all sensors 150, the process can be repeated until the difference between the new estimate of sensor positioning and the previous estimate of sensor positioning is below a threshold (e.g., a predetermined threshold and / or a clinician-adjustable threshold).

[0116] Regardless of the method used to capture and indicate sensor positioning data, sensor positioning system 200 determines the positioning of sensors 150 relative to each other and the subject's body to verify appropriate sensor application and / or achieve accurate SED calculations. In some implementations, sensor positioning system 200 may be omitted, and the positioning of sensors 150 may be indicated by a clinician and uploaded or otherwise entered into the system.

[0117] Now for reference Figure 5 The diagram illustrates a flowchart of method 2 for collecting cardiac data via data collection system 100. In step 2-1, sensor 150 senses or collects electrical signals (e.g., analog signals) indicating the electrical activity of the heart and / or the location of transmitters during the cardiac cycle, and transmits the signals to converter 110. In step 2-2, converter 110 receives the analog signals from sensor 150 and converts them into digital signals (e.g., data). In step 2-3, collector 120 aggregates or combines the digital data from converter 110. In step 2-4, collector 120 generates and / or outputs a raw data stream DS1 from the combined digital data. In some embodiments, steps 2-3 and 2-4 may be combined into a single step.

[0118] In some embodiments, in steps 2-3, collector 120 combines partial data streams from all converters 110 to generate a raw data stream DS1 in steps 2-4. In some embodiments, converter 110 samples signals (e.g., voltages) from sensor 150 at, for example, 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz, 4000 Hz, 5000 Hz, 8000 Hz, 16000 Hz, 32000 Hz, or 64000 Hz. Collector 120 assembles data from converter 110 (e.g., from all converters of converter 110) into a single raw data stream DS1. In some embodiments, signals are distributed across one or more converter components of converter 110 to maintain coherent timestamps on the data packets (e.g., despite high sampling rates). That is, converter 110 may include one or more converter components, each of which converts data received from one or more sensors 150. The raw data stream DS1 contains digitized data from one or more sensors 150 and may be represented as a real-time buffered and / or unbuffered data stream, and / or as a binary, ASCII, or otherwise formatted file indexed by time and sensor identifiers. The raw data stream DS1 may be stored in an electronic database, which is stored in memory accessible by the data processing system 300, such as a hard disk, SSD, and / or other device. That is, the data processing system 300 may further include memory, and / or may communicate with the memory 45 of the CPU 40. Any such database and / or memory used herein may be optimized for real-time storage and access.

[0119] At any time, the data stream DS1 stored in the database and / or memory 45 can be sampled, and the data from said data stream can be displayed graphically or otherwise (e.g., via user interface 41 and / or one or more displays of display system 400) using standard or custom software packages. This midstream data evaluation can be used to assess the performance of different steps in the entire data processing stream. In some embodiments, values ​​across multiple sensors 150 and individual center of electrical activity (“CEA”) coordinates varying over time can be displayed. Data can be extracted in segments and stored in memory 45 for backup and / or future analysis. For example, data can be extracted in time intervals or segments of 0.1 minutes, 1 minute, and / or 10 minutes.

[0120] Now for reference Figure 6 A flowchart illustrating method 3 for determining the MCEA of a subject's conduction pathway and / or emission device according to an embodiment is shown. Method 3 includes transforming a raw data stream DS1 into MCEA data streams DS2 and / or DS3 via a data processing system 300. Figure 6 As shown, the raw data stream DS1 of the signal collected by sensor 150 in Method 2 of Figure 4 is processed in several parallel paths (e.g., 3A, 3B, 3C, and 3E). Data transmitted along the paths can be passed as real-time buffered and / or unbuffered data streams, and / or as files formatted in binary, ASCII, or other ways, indexed by time and sensor identifiers. At each processing stage, the refined process data stream can be stored (e.g., in memory 45) in a new file, in a separate section of an existing file, in a single database indexed by processing stage and sampling rate, and / or in some other format. The steps of Method 3 do not need to be in the format shown in Figure 3. Figure 6 The same order of execution is shown. For example, the execution order of steps 3-2 and 3-3 or steps 3-6 can be reversed. Similarly, steps 3-8 and / or 3-9 can be executed after step 3-12. In some cases, one or more steps can be omitted.

[0121] In the depicted embodiment, method 3 includes transferring the original data stream DS1 (from...) Figure 5 Steps 2-4) divide or replicate the data into two virtual streams that travel along a first processing path 3A and a second processing path 3B in parallel. Each of the virtual streams can be processed at a different temporal resolution. For example, a stream can be processed at a low temporal resolution to analyze the entire cardiac cycle or the P-to-R interval and identify “good” heartbeats and their segments (e.g., cardiac cycles or portions thereof with the desired morphology). The identified segments of the selected “good” heartbeats are analyzed at a high temporal and / or high data resolution. This configuration enables efficient, real-time or near real-time (e.g., within 100 ms, 10 ms, 1 ms, or 0.1 ms) processing of ECG data from specific portions of interest (e.g., the PR segment) from the cardiac cycle. In addition to allowing for detailed analysis of low-intensity signals during the PR segment that was previously impossible, this bifurcation also enables high-fidelity analysis of deviations in the QT interval caused by downstream conduction defects. According to the embodiments disclosed herein, this is a level of analysis that can be used to identify and locate conduction defects (such as reentrant circuits) on the free wall of the ventricle that lead to ventricular arrhythmias such as ventricular tachycardia.

[0122] In the first processing path 3A (e.g., the first data path), the data stream DS1 is first downsampled in step 3-1. This allows for faster processing in subsequent steps. In some embodiments, the data stream DS1 is downsampled to 50 Hz, 100 Hz, 250 Hz, 500 Hz, 1000 Hz, or 2500 Hz. In various embodiments, downsampling can occur through various techniques such as decimation, resampling, interpolation, linear averaging, and / or curve fitting.

[0123] In step 3-2, the data stream DS1 may undergo common-mode rejection (“CMR”), where the average signal from the body within the data stream over that time period is subtracted from the corresponding voltage value of each sensor 150 at each time. CMR reduces common-mode noise. In some embodiments, the average signal is the sum of signals from the left arm, right arm, and left leg (referred to as the Wilson center terminal). In some embodiments, the average signal is obtained from a collection of signals from a plurality of sensors 150 attached to the torso and / or other body locations.

[0124] In or after step 3-2, the data from the first processing path 3A is then further split into two data streams or paths (e.g., a third processing path 3C and a fourth processing path 3E). In step 3-3, the third processing path 3C of the first processing path 3A can be reduced, for example, by using a narrow-bandpass filter (e.g., included in the data processing system 300) to capture and isolate electrical signals emitted from one or more transmitting devices (e.g., transmitting device 90). If more than one transmitting device 90 is deployed and tracked, this process can be repeated. Figure 6 Steps 3-3, 3-4, and 3-5 are used to isolate the signal from each transmitter 90.

[0125] Using the now isolated transmitter signal, each sensor 150 signal with a positive peak above a threshold voltage can be identified in steps 3-4. In some embodiments, this step can be performed by examining a signal containing multiple peaks over a time period and selecting, for example, the top 1%, 5%, or 10% of the voltage values. All values ​​in the stream of the time period can be replaced by the average of the identified peak values ​​and / or values ​​above the threshold voltage. For example, the signal may contain a 100 Hz sine wave, the time period may be 0.1 seconds, and it may contain 10 peaks. If the maximum voltage during such a 0.1-second time period is 10 mV for a sensor 150, then all values ​​above a threshold voltage of, for example, 9.5 mV can be averaged, and the signal from this sensor 150 over the entire 0.1-second time period will be set to the average value. In some embodiments, the signal from the transmitter 90 on each channel (e.g., the signal from each sensor 150) is processed individually as follows: System 10 (e.g., via an algorithm and / or data processing system 300) examines the forward-moving signal to find the initial positive peak. Once the location of the peak is identified, system 10 moves forward the signal time period and then searches for the next peak in a region surrounding the region, which may be 1%, 5%, 10%, or 20% of the signal time period. This process is repeated for the entire dataset of all channels. System 10 then calculates the CEA for all data points and / or a short period of time for each peak using data processing system 300 and / or CPU 40. The SED at the peak is then averaged over a period of time, and this average can be used to track the MCEA of the transmitting device 90 in real time. In some embodiments, a moving average of multiple peaks is used to set the value of the signal from the sensor.

[0126] In steps 3-5, the data processing system 300 determines the CEA coordinates of the signal from the transmitting device 90. Additionally, (when using a SED) the dipole vector of the signal from the transmitting device 90 can be determined, and thus the orientation of the transmitting device 90 can be determined. Specifically, the processing system 300 determines the CEA coordinates of the signal from the transmitting device 90 based on the determined MCEA (determined by the positioning system 200) of the transmitting device 90 and the sensor 150. This data can be timestamped and can be part of the processed data stream DS2 from step 3-5. Steps 3-3 to 3-5 can be omitted if a transmitter is not used.

[0127] Simultaneously, in steps 3-6, the data generated in step 3-2 traveling along the fourth processing path 3E is filtered by a 55 Hz low-pass filter (e.g., to eliminate power line noise) and a 1 Hz high-pass filter (e.g., to eliminate variations from slow movement) of the data processing system 300. For example, removing 60 Hz and its harmonics while maintaining the remainder leaves irrelevant noise, but allows for the removal of said noise by periodic averaging. All filtering (e.g., steps 3-3, 3-6, 3-11) can be processed in segments large enough to minimize end effects but small enough to be computationally efficient. However, filtering too large a data stream can consume excessive computational resources and increase latency. Filters with specific limits, such as the 55 Hz low-pass filter, have different parameters for different sampling frequencies. For example, there may be one set of parameters for an 8 kHz sampling frequency and another set for a 500 Hz sampling frequency. There are trade-offs related to the “sharpness” of the filters implemented by system 10. For software-implemented filters, the filter software requires more parameters and therefore takes longer to compute when the boundary between the pass frequency and the attenuation / suppression frequency is sharper, faster, and / or more difficult. In some embodiments, the data processing system 300 determines a set of parameters that optimizes the trade-off between noise and computational efficiency to obtain clinically valuable data in real time. In some embodiments, additional filtering is applied to the data stream when the system detects or is notified of a triggering pacing signal from an implanted pacing lead.

[0128] In steps 3-7, the data processing system 300 uses the filtered data from steps 3-6 to identify electrophysiological milestones for each heartbeat in order to identify relevant portions of the cardiac cycle (see...). Figure 9AThis is for analysis. Next, a subset of signals from sensor 150 can be selected to compute a differential ECG signal. In some embodiments, this subset of signals comes from sensors 150 placed on the patient's limbs. In some embodiments, signals from a pair of sensors 150 applied to the torso are selected as a subset of the signal. In some embodiments, algorithms such as signal smoothing algorithms can be applied to the differential ECG signal stream to improve event detection. Pattern recognition algorithms can be applied to identify timestamps for each significant milestone in the ECG cycle (e.g., peaks, beginnings, and ends of P, Q, R, S, and T). In some embodiments, the interval between the P peak and the R peak can be identified as an ECG milestone. In some embodiments, timestamps can be determined based on the goodness of the voltage spectrum predicted by the sensors using a SED algorithm: a small final SED fit error indicates a high concentration of electrical activity on the heart, as is the case for the PR segment. The pattern recognition algorithm can be any known algorithm and / or software for physiological signal processing. The data processing system 300 can use these ECG milestones to timestamp the start / stop line of each heartbeat (e.g., the timestamp can be set at a fixed time period before the P peak milestone). Steps 3-6 and 3-7 can be performed in parallel with steps 3-3 and 3-4. In some embodiments, filtering can be performed in other ways.

[0129] In some embodiments, any irregular cardiac cycles / beats (such as premature ventricular contractions (“PVCs”), heartbeats for which the software cannot recognize all milestones, heartbeats with abnormally long or short waves, and / or other heartbeats that are abnormal in some other way) are identified and marked or labeled as “bad”. Method 3 may later exclude “bad” heartbeats. In some embodiments, the data processing system 300 may exclude “bad” beats from the data in steps 3-7. In some embodiments, all steps performed on low-resolution data are instead performed on high-resolution data.

[0130] In steps 3-8, in the second processing path 3B, high temporal resolution or high data resolution data from a subset of data is extracted from the original data stream DS1. The high temporal resolution data is determined by the data processing system 300 based on the milestone timestamps determined in steps 3-7. That is, heartbeats marked as "bad" are excluded from the data stream DS1, and only the desired heartbeats are selected. Further, the subset of data corresponding to the PR segment is selected as the desired segment. Because this step cannot be finalized until steps 3-7 are completed, the processing of the high temporal resolution or high data resolution data may lag behind the low-resolution processing in the fourth processing path 3E for the entire duration of the PR segment. When using SED, this lag can be minimized by predicting when the desired segment (e.g., the PR segment) will occur based on data from previous heartbeats, and by calculating the SED starting from the center of the predicted PR segment. In some embodiments, when the Q wave cannot be detected, located, or otherwise determined, the desired segment extends from the end of the P wave to the beginning of the R wave.

[0131] In steps 3-9 of the second processing path 3B, data from the intervals selected in steps 3-8 are downsampled for faster processing by the data processing system 300. The downsampling in steps 3-9 can be substantially similar to the downsampling in step 3-1 described above. However, in some embodiments, the data may be downsampled to a lesser or greater extent than in step 3-1. In some implementations, step 3-9 may be skipped or omitted because only a relatively small portion of the data corresponding to the desired segment (e.g., the PR segment) is processed. Because the duration between milestones (e.g., segments) may vary with heartbeats, downsampling may involve resampling (e.g., by curve fitting and / or weighted averaging) such that the number of samples in the interval remains constant between heartbeats.

[0132] In some embodiments, for a desired segment (e.g., the PR segment), system 10 (e.g., via data processing system 300) resamples the ECG data in the segment of each “good” cardiac cycle such that there are the same number of data points in the segment of each cycle. This resampling can be performed because the duration of each segment varies between cycles, and the actual number of data points collected during the desired segment can vary between cycles. When the desired segment extends from the end of the P wave to the beginning of the R wave, system 10 uses the first portion of the desired segment (e.g., the first 25%, 30%, 50%, 70%, or 90% of the interval) to analyze the PR segment.

[0133] In steps 3-10, CMR is applied to the processed data stream output from steps 3-9 (or from steps 3-8 in embodiments where steps 3-9 are omitted) in a similar manner to CMR application in step 3-2. In steps 3-11, the processed data from steps 3-10 is filtered, for example using a 55 Hz low-pass filter (which eliminates power line noise and automatically excludes any signals from the transmitting device, if used, at higher frequencies such as 75 Hz, 90 Hz, 120 Hz, 200 Hz, 300 Hz, or 800 Hz) and a 1 Hz high-pass filter to eliminate changes from slow movement (e.g., due to breathing, heartbeat, and the operation of the subject, internal tissues, surgical instruments, and / or the transmitting device 90).

[0134] In steps 3-12, based on the processed data stream obtained from step 3-11 and the sensor location data determined from step 3-13, the CEA coordinates and dipole vectors of the cardiac electrical signal at each sampling time point during the selected interval are determined, as discussed in detail below. The spatial curve representing the MCEA data stream DS3 (see...) Figure 9C and Figures 10A to 10C The data stream DS3 is generated based on a set of CEA coordinates. That is, the CEA coordinates are plotted over time and correspond to electrical impulses or MCEAs propagating along the cardiac conduction pathway (e.g., AV node, His bundle, RBB, LBB, and Purkinje fibers) during the PR segment. The plotted CEA coordinates generate a spatial curve representing the MCEA data. Therefore, the MCEA data corresponds to the location of the AV node, His bundle, RBB, LBB, and Purkinje fibers. Thus, the spatial curve plot is also a graphical representation of segments of the cardiac conduction pathway (e.g., AV node, His bundle, RBB, LBB, and Purkinje fibers). In some embodiments, data regarding the CEA fitting error is added to the data stream DS3. This data can be timestamped and can be part of the processed data stream DS3.

[0135] Because CEA calculations require knowledge of the relative 3D positioning of sensor 150, this positioning data must be fed into those calculations in steps 3-13. In steps 3-13, as described above, the positioning of sensor 150 is determined as part of sensor positioning system 200. In some embodiments, system 10 (e.g., sensor positioning system 200, data collection system 100, etc.) maintains a list of “bad” sensors 150 and automatically excludes and / or reduces the weight of these sensors based on CEA calculations. For example, a “bad” sensor might be one that system 10 identifies as having no signal or a low signal-to-noise ratio.

[0136] In some embodiments, the CEA forward propagation calculation is adjusted by weighting sensor data based on the expected field propagation impedance between the heart and sensor 150. This impedance-based weighting can be determined by system 10 (e.g., via CPU 40) using a pure dipole signal emitted by transmitter 90. For example, the weights of sensor 150 are adjusted until the predicted sensor-recorded voltage reflects the observed voltage.

[0137] Alternatively or additionally, trunk impedance at the ends of different chordae tendineae can be measured by having a first sensor 150 positioned on one side of the body emit a signal while a second sensor 150 positioned on the opposite side of the body receives the signal, and by calculating the decrease in signal strength. In some embodiments, the voltage drop across the ends of the chordae tendineae passing through the heart can be used to calculate the weights of those sensors 150 on either side of the chordae tendineae. Alternatively or additionally, voltage drop data at all ends of the chordae tendineae can be used to generate a trunk impedance map. Such a map can be based on a 3D anatomical model of organ volume and location, where the resistance estimates of individual organs are adjusted to fit droplet vector data. In some embodiments, the 3D anatomical model is adjusted for anticipated abdominal fat deposition, such as adjustments performed based on sex, BMI, and / or other factors (e.g., by algorithm).

[0138] In some embodiments, SED analysis is used to calculate CEA; however, other techniques, such as iTSI, can also be used. SED attempts to describe the electric dipole (position and orientation / torque) that will generate an electric field, which will sequentially produce a voltage on sensor 150 placed at a specific point on the surface of the subject's or patient's torso. The SED can be inferred from the sensed voltage using an inverse algorithm, as described below. The SED can be plotted over time to show the SEMD corresponding to the MCEA along the cardiac conduction pathway. Therefore, SED analysis of sensor data can be used to calculate CEA, as it travels along the cardiac conduction pathway.

[0139] Standard calculations of propagation voltage assume uniform conductivity within the body. However, conductivity in the torso is non-uniform due to the presence of bones and organs. While standard calculations cannot accurately predict surface voltage, inverse algorithms can find a “best fit” between the SED model and sensed or observed data. Any distortion in SED localization due to torso non-uniformity varies continuously with the movement of the CEA in the cardiac conduction pathway. Furthermore, for SEDs defined for multiple entities close to each other, such as the cardiac conduction pathway in the heart and the transmitter 90 placed within the subject's heart, the distortion is smaller (e.g., within 100 micrometers to a few millimeters). Therefore, the body's non-uniform conductivity can be considered, but does not need to be, in the SED model to effectively represent the cardiac conduction pathway and / or the transmitter 90, and to enable navigation / guidance of the transmitter 90 to the cardiac conduction pathway.

[0140] Using the techniques described herein, system 10 generates the spatial curve of the SEMD in the “SED space” (a slightly distorted version of real space) representing the MCEA data stream DS3, and also generates the dipole positioning, orientation, and movement (if present) of the transmitter 90 representing the transmitter of the MCEA data stream DS2. Using this information, system 10 can display the cardiac conduction pathway (e.g., AV junction, His bundle, LBB, RBB, etc.) and transmitter 90 (if present), allowing clinicians to clearly navigate transmitter 90 to the desired target within the patient's heart. That is, for example, system 10 generates a visual representation of the device's SED relative to the heart's SEMD (indicating the MCEA), enabling the device to be guided into the cardiac conduction pathway. To further enable this navigation, system 10 can overlay the SED space with an image of the heart, allowing catheters to be guided into the cardiac conduction pathway. Thus, a pacing lead near transmitter 90 can be guided into and juxtaposed with the cardiac conduction pathway to achieve conduction system pacing. In some embodiments, system 10 is both a display and a navigation system.

[0141] Several methods exist for calculating the SED parameters at a given time point (e.g., steps 3-5 and 3-12). In some embodiments, this calculation is performed by system 10 using a gradient descent algorithm that optimizes the error in the SED fitting sensor data. In some embodiments, the calculation is performed as a nonlinear approximation using an approximation positive operator, trained neural network, machine learning, AI, and / or in some other way. In some embodiments, the calculation is performed via voxel grid search (as described below). Figure 7 (As described) to complete.

[0142] Now for reference Figure 7 A flowchart of method 5 for determining the location of a SED according to an embodiment is shown. The voxel grid search performed by system 10 can be iterative, thereby searching more carefully each time the region where the best SED fit is found is reached. In step 5-1, the search is initialized by determining and setting the center and size of the search region / volume. In some embodiments, the initial search volume is centered on the patient's torso and / or heart and is large enough to cover the entire torso. In some embodiments, the search volume is a cube. In some embodiments, the center and size of the search volume are defined using the positioning coordinates of the sensor 150 placed on the torso. In some embodiments, the center is offset towards the left ventral side of the patient's chest. In some embodiments, the search volume is limited to 1, 2, or 3 times the volume of the patient's heart.

[0143] In some embodiments, the search is centered on the SED solution within the most recent time period or cardiac cycle. For example, the search for an SED within a time period may be centered on: the location of the SED in previous time periods, a prediction of the location of the next SED based on the location of the SED in some recent time periods, or the average location of the SED at the same relative time in one or more previous cardiac cycles, or some combination thereof. In some embodiments, based on previous SEDs, the search volume is limited to a region with a 95% or 99% chance of containing the SED.

[0144] In step 5-2, a search set containing the voxel volume to be searched is determined based on the sensitivity factor S (e.g., via data processing system 300). The search region / volume is divided into S... 3 Voxels are generated to create an SxSxS voxel cube. Any voxels outside the search volume, such as the trunk volume or heart volume, are removed from the search set. In some embodiments, the search volume is defined by the positioning coordinates of the sensor 150 placed on the trunk. In some embodiments, the search volume is defined by an elliptical cylinder fitted to the coordinates of the sensor 150. In embodiments that remove voxels falling outside the heart volume, the heart volume may be determined by the system 10 based on imaging data (e.g., captured by an imager 80, which may be, for example, a fluorescein, MRI, or CT) and / or based on a standard anatomical model adjusted based on the dimensions of the patient's trunk as defined by the positioning of the sensor 150 on the trunk.

[0145] In steps 5-3, system 10 calculates the SED fitting error value for each remaining voxel in the search set to find the voxel with the best SED fit. System 10 (e.g., data processing system 300) calculates the difference between the calculated electric field strength of the dipole located at the center of a specific voxel of each sensor 150 and the actual electric field strength measured by each sensor 150 for that specific voxel. Individual voxel errors are aggregated (e.g., by weighted summation). The SED fitting error can then be calculated using various methods.

[0146] In step 5-4, the SED fitting error and / or the size of voxel S are compared with criteria. The criteria include the SED fitting error, for example, less than 0.01%, 0.1%, 1%, 2%, 5%, or 10% of the weighted sum of the squares of the voltages at each sensor, and the voxel size may be, for example, less than 1 mm, less than 0.25 mm, or less than 0.01 mm. If the criteria are not met, the method continues to step 5-5. If the criteria are met, the method continues to step 5-6.

[0147] In step 5-5, the center of the voxel with the lowest error value is selected as the new center of the search cube. Method 5 returns to step 5-2 and repeats iteratively until the SED fitting error and voxel size meet the criteria in step 5-4. When the method returns to step 5-2, the new search cube is equal to the size of 27 voxels containing and surrounding the previously determined lowest value voxel. In some embodiments, once the voxel size reaches a termination value, if the SED fitting error is not small enough, the search restarts with a larger initial volume. In some embodiments, the criterion may be the number of times the voxel search space is refined. For example, the voxel search space criterion may be a predetermined number of times the voxel search space is refined. In some cases, the criterion is met after two, three, four, five, or more iterations of voxel search space refinement.

[0148] Returning to step 5-4, if the error is sufficiently small or the voxel size is sufficiently small, the criteria are met, the search ends, the SED coordinates are set to the center of the voxel, and the method proceeds to step 5-6. In some embodiments, the terminating voxel size will be less than 3 mm, less than 1 mm, less than 0.25 mm, or less than 0.01 mm. In step 5-6, the dipole moment vector is calculated from the processed data stream DS3 data from the sensor 150 on the torso.

[0149] In some embodiments, system 10 (e.g., via data processing system 300) checks the temporal continuity of each new CEA calculation and rejects solutions that suggest discontinuous movement in the spatial curve. In other embodiments, the resulting spatial curves of the CEA points are smoothed, and each CEA value is modified so that the CEA calculation can take into account a broader dataset, including locations that are close in time, thereby reducing the standard error of the CEA estimate.

[0150] In some embodiments, CEA data are averaged over two or more cardiac cycles to better estimate the average conduction pathway. However, because the heart physically moves and stretches during beating, and because this movement can vary with each heartbeat, the spatial profile of the conduction pathway relative to the trunk can also vary with each heartbeat. Therefore, to develop a more accurate estimate of the conduction pathway, cardiac movement can be detected, for example, by monitoring respiration, cardiac noise, echocardiography, fluoroscopy, and / or signals from an accelerometer on the lead-placement transmitter 90, and said information can be used to adjust the CEA spatial profile for each cardiac cycle. In some embodiments, CEA data acquired during the QRS interval is used to estimate cardiac movement.

[0151] Now for reference Figure 8A flowchart illustrating method 4 for displaying data according to one or more embodiments is shown. Display system 400 receives MCEA data streams DS2 and / or DS3 generated by data processing system 300 and generates or produces a graphical 3D model for displaying information on a GUI or display. The 3D model may include a graphical representation of the heart (a general heart shape obtained from imaging data or a patient-specific heart) superimposed with one or more spatial curves generated from the MCEA data. As described above, MCEA can be used to generate a time-varying CEA spatial curve; the spatial curve may be a SEMD spatial curve generated using SED analysis. The spatial curve indicates cardiac conduction pathways (e.g., AV junction, His bundle, LBB, RBB, etc.) and can be fitted to a graphical representation of the subject's heart. In some embodiments, the 3D model may further include a graphical representation of the location and orientation of the transmitting device 90 (if present).

[0152] like Figure 8 As described herein, method 4 includes, in step 4-1, accumulating multiple selected intervals from the stream DS3 of the MCEA points, such as MCEA localization, and, if using SED, the dipole vector for each time sample. In some embodiments, any MCEA sequence deemed unrepresentative based on criteria (e.g., ECG waveforms deviating from the normal waveform of the patient's heartbeat and / or otherwise abnormal due to prolonged or shortened waves or segments, missing, extra or malformed waves, arrhythmias, incorrect heartbeats, signal interference, etc.) is excluded (e.g., by data processing system 300 and / or display system 400). In some embodiments, the selected interval is the PR segment, and the MCEA point corresponds to an electrical signal propagating along the AV junction, His bundle, LBB, and RBB. However, any desired interval or desired segment of the ECG wave can be selected to detect the desired conduction pathway.

[0153] In steps 4-2, system 10 can use the collected interval dataset (e.g., via data processing system 300 and / or display system 400) to develop and / or update a 3D model of the electrophysiology of the heart (e.g., the cardiac conduction system). In some embodiments, the 3D model is a simple average of the MCEA localization / dipole vectors across multiple heartbeats. For example, the 3D model can be represented as a SEMD space curve (e.g., Figure 9C and Figures 10A to 10CThose shown in the figures above, as described above, can be averaged to account for variations in cardiac motion. That is, the 3D model can account for distortions caused by cardiac motion. Additionally, since the cardiac conduction pathway (“CCP”) may be slightly diffuse in some patients, and the CEA may vary with each beat, averaging can provide a more accurate representation of the CCP center. In some embodiments, all or some portions of the data are fitted to a linear, parametric, and / or another model, and a standard error is calculated. For example, a straight line or planar curve can be fitted to the PR segment MCEA to represent the AV junction, His bundle, LBB, and / or RBB. In some embodiments, where a planar curve is fitted to the PR segment, the plane contained in the curve can be used as an estimate of the location of the cardiac septum. In some embodiments, the septal plane determined by the transmitting device described below can be used to inform or constrain the curve fitting to the MCEA. In some embodiments, the accuracy of the curve fitting (e.g., standard error) is calculated either once or dynamically. In some embodiments, this accuracy can be displayed graphically. For example, the probability distribution of the CCP location can be displayed on an image of the septum. In some embodiments, the MCEA spatial curve can be overlaid with a graphical representation of the heart. A graphical representation of the heart can be based on data from fluoroscopy, MRI, CT, and / or other imaging techniques. In some implementations, the range of values ​​that the MCEA may have (e.g., its standard error) is superimposed with a graphical representation of a space curve, such as with error bars or transparent error clouds.

[0154] In some embodiments, system 10 can be configured to collect data while determining its CEA (Cardiac Elevation Area) as the transmitter 90 moves around the interior of the heart. This data can be used (e.g., via data processing system 300 and / or display system 400) to create SEMD (Sequencing and Mapping of the Endocardial Surface) spatial maps of the location of different parts of the heart, such as the septum of the ventricle or atrium, the apex, and the free wall. System 10 can use a set of CEA points within the heart to interpolate and map the surface of the endocardium. In some embodiments, system 10 uses this technique to locate the surface of the septum, which in turn informs and / or constrains the MCEA spatial curve fitting to the 3D model. For example, fluorescence perspectrography can be used to navigate the transmitter to the estimated location of the septum and then move around in all directions while gradually decreasing the depth toward the valves. Such techniques avoid the trabeculae of the lower septum and the chordae tendineae near the upper septum. Algorithms such as shrink-wrapped manifold mesh generation algorithms can be used to identify surfaces that cover the location of the transmitter in a cloud map. The septal portion of the surface can be isolated based on its orientation. The surface can also be used to correctly locate, scale, or distort the superimposed cardiac model.

[0155] In some embodiments, the fit is modified by system 10 (e.g., via data processing system 300 and / or display system 400) to account for physical distortions during heartbeats. In some embodiments, data regarding the MCEA fit quality or inter-period error is used to further refine the model. Once this model is created, it can be updated by system 10 when additional data is collected. The model can be stored in memory and / or reset during the procedure (e.g., running before and after the application of therapy), for example, to determine the effects of pacing and / or other treatments on the model.

[0156] In steps 4-3, once system 10 has created this 3D model of the patient's heart, the SED positioning and dipole vector of the transmitter 90 can be displayed in real time along with the 3D model via display system 400. Therefore, clinicians can position the transmitter 90 and its orientation relative to the diaphragm and / or the cardiac conduction system model in real time, and then move the transmitter 90 until it reaches the desired position and orientation relative to the diaphragm and / or the cardiac conduction system model, and therefore relative to the patient's heart (e.g., a plane perpendicular to the diaphragm). For example, in some embodiments, the dipole vector of the transmitter 90 can be used to determine the orientation of the transmitter 90, which can then be used to adjust the angle at which the transmitter 90 inserts an instrument, such as a pacing lead, into a target (e.g., the diaphragm). In some embodiments, the CEA positioning of the transmitter 90 relative to the diaphragm can be used to calculate the depth of instrument insertion.

[0157] System 10 includes a GUI configured to provide a number of features designed to assist users (e.g., clinicians). The GUI may be a component of display system 400 or a component of CPU 40 (e.g., user interface 41) communicating with one or more of data collection system 100, sensor positioning system 200, data processing system 300, and display system 400. For example, the GUI may display standard ECG signals and / or vector electrocardiograms, CEA data, and a representation of the heart (e.g., a transparent and / or sectional 3D model of the heart, a 3D model of the diaphragm only, and / or an image of the heart provided by an imager). The GUI may be configured to rotate and / or translate the CEA data and the image of the heart representation based on input from the user, for example, to achieve a desired viewpoint. The GUI may also be configured to display more than one viewpoint simultaneously. The GUI may also display illustrative graphics of the lead and emitter 90, such as models oriented in space relative to the representation of the heart, the representation of the diaphragm, and / or the MCEA space curve of the PR segment.

[0158] In some embodiments, the GUI is configured to display data based on an electric field detected by one or more electrodes on the transmitting device 90. For example, this data may indicate the proximity of the transmitting device to electrically active cardiac tissue, or it may indicate the location of the transmitting device 90 in the heart (e.g., in the atrium or ventricle) based on the shape of the detected ECG pattern.

[0159] In some embodiments, the GUI includes one or more flat panel displays and / or tablet computers connected via wired or wireless connection to one or more other components of system 10. Control for managing the user interface 41 and / or modifying parameters used to process sensor data and MCEA data can be performed via a touchscreen and / or a separate user input device, such as a keyboard, mouse, joystick, trackpad, and / or custom-designed control or haptic control device. The touchscreen may also be a display showing the MCEA and a graphical representation, or it may be a separate device.

[0160] In some embodiments, the GUI can be configured to allow control of the transmitter 90. For example, controls on the GUI can manage (e.g., turn the signal on or off, and / or adjust the frequency or amplitude of the signal) the signal transmitted from the transmitter 90. In some embodiments, such control is implemented via a module of the user interface 41 attached to a wire or signal generator 91 at the proximal end of the transmitter 90, or via a wireless connection (e.g., Bluetooth, WiFi, or ZigBee) to the proximal end of the transmitter 90 or the signal generator 91. In some embodiments, the user interface 41 can issue commands to a robot controller connected to the transmitter 90, wherein the controller can actuate the movement of the transmitter 90 within the heart of the device.

[0161] The GUI can be configured to display the location of the His-Purkinje system (including bundle branches) within the diaphragm, such as by using only the most recent information (direction, amplitude, or angle) of the His-Purkinje system collected via MCEA data or by using a 3D model of the cardiac conduction system described above.

[0162] User interface 41 may be a standalone display or integrated (e.g., co-registered) into another image of the heart created by another imaging modality (such as imager 80, which may be a fluorescence microscope, CT imager, MRI imager, and / or other imaging device).

[0163] The system 10 disclosed herein has a variety of similar and alternative uses. For example, spatial curves representing MCEA data collected by the system 10 for part or all of the cardiac cycle can be used to: study the variability of cardiac function (systolicity, cardiac output, etc.); optimize left ventricular lead placement; optimize pacemaker timing (AV interval, VV interval, etc.); aid in understanding cardiac conduction velocity throughout the cardiac cycle; diagnose arrhythmias and / or locate the source of arrhythmias (e.g., return pathways); and / or aid in understanding the impact of myocardial scarring on cardiac conduction. MCEA spatial curves determined by the system 10 before and after intervention (e.g., pacing lead placement) can be used by the system 10 (e.g., by algorithms) to assess the impact of the intervention on interrupted or dysfunctional conduction pathways. For example, spatial curves can be evaluated to determine whether conduction system pacing (“CSP”) has been achieved. Individual cardiac cycles or average SEMD spatial curves can be compared to standard curves from healthy patients or patients with specific cardiac pathologies. The system 10 may include one or more algorithms (e.g., obtained through machine learning) applied to the SEMD spatial curves to identify specific cardiac functional pathologies. These and other types of clinical analyses of the data generated by System 10 can be used to: diagnose patients; determine whether a patient is a good candidate for a specific type of pacing (such as conduction system pacing); validate the effectiveness of the procedure after intervention; and / or monitor patients for months and years after intervention.

[0164] For example, if ECG and / or SEMD values ​​meet criteria (e.g., desired morphology, desired value, etc.), a specific type of pacing (i.e., pacing strategy) can be selected for the patient. Alternatively, if ECG and / or SEMD values ​​do not meet the criteria, the patient may not be a good candidate for pacing. After conduction system pacing procedures, ECG and / or SEMD values ​​can be compared with secondary criteria (e.g., desired morphology, desired value, etc.) to determine the effectiveness of the intervention. For example, ECG and / or SEMD data can be compared with the desired morphology. The smaller the deviation between the desired morphology and the measured morphology, the greater the predictive effectiveness of the pacing.

[0165] Now for reference Figure 9A It depicts valid ECG data extracted from 64 individual electrodes (e.g., sensor 150), averaged over 34 heartbeats, with emphasis on the PR segment. Figure 9B From Figure 9AThe graph shows the X, Y, and Z SEMD values ​​calculated from the extracted effective ECG data in the PR segment. Although the ECG signal is relatively weak during the PR segment, as mentioned above, different combinations of 8kHz, 24-bit sampling, appropriate noise filtering, and / or cross-heartbeat averaging allow for amplification of the PR segment to achieve high temporal resolution SEMD during the transmission of the electrical signal from the AV junction to the His bundle, LBB, and RBB. Based on this data, spatial curves of the amplified PR segment across multiple heartbeats can be modeled for various mammalian pairs.

[0166] Figure 9C An example SEMD space curve is plotted, showing the calculated orientation and amplitude of each SED point during the P-to-R interpeak interval within a 3D volume. Specifically, the cooler (blue to green) beginning of the space curve represents the orientation and amplitude of the SED point at the start of the P-to-R interpeak interval, while the warmer (red) middle section represents the orientation and amplitude of the SED point during the middle of the P-to-R interpeak interval. Finally, the warmer (orange to yellow) ending section represents the orientation and amplitude of the SED point at the end of the P-to-R interpeak interval. This can be seen from... Figure 9C Separate the desired segment (e.g., the PR segment) from the P-peak to the R-peak interval in the SEMD space curve, for example, as described below. Figures 10A to 10C As depicted in the text. Figure 9C The spatial curve can represent the ECG wave propagating through the AV junction, His bundle, LBB, RBB, and Purkinje fibers during the P-peak to R-peak interval. Therefore, using the technique proposed in this paper, cardiac conduction pathways, including the AV junction, His bundle, LBB, and RBB, can be detected and mapped in real time.

[0167] Figures 10A to 10C The SEMD space curves shown illustrate the PR segments representing three different sets of data for the MCEA propagating through the AV junction, His bundle, LBB, and RBB. As the MCEA propagates along the cardiac conduction pathway, its 3D localization is represented by the SED moving in 3D space, and... Figures 10A to 10C The SEMD space curve is displayed as multiple arrows or vectors over a given time period. Each arrow on the SEMD space curve indicates the 3D location and orientation of the SED at a specific moment. For example, the cooler (blue) portion of the SEMD space curve indicates the SED at the beginning of the desired segment (e.g., the PR segment). The warmer (red to orange) middle portion of the space curve indicates the SED at the MCEA in the middle of the desired segment. Finally, the warmer (yellow) ending portion of the space curve simply indicates the SED at the end of the desired segment.

[0168] In each of these SEMD space curves, during the desired segment (e.g., the PR segment), the significant changes in the torque, direction of movement, and velocity of the SED are caused by… Figures 10A to 10CThe spacing and direction of travel of multiple arrows / vectors are used to illustrate this. Specifically, during these selected segments, the amplitude of the torque is small (indicating that only a small number of polarized cells are involved in the wavefront at any given time), and the SED (indicated by arrows at specific locations along the cardiac conduction pathway) first pauses and then moves rapidly in a single direction (as indicated by the location, spacing, and color of the arrows / vectors). These changes correspond appropriately to the expected location and behavior of the resulting electrical signal as the cardiac conduction wave is “delayed” in the AV junction and then proceeds along the His bundle, LBB, and RBB to the Purkinje fibers. In contrast, the SEMD during the QRS complex associated with the broad cellular excitation wave associated with ventricular contraction is known to have a larger dipole torque and moves in the opposite direction to the SEMD during the PR segment. Similarly, the magnitude and movement of the dipole torque during the P wave are consistent with the joint activation known to occur during the aforementioned segments. Because cellular firing is highly concentrated in the PR segment, the SED location delineates the physical path from the AV junction to the His bundle, LBB, RBB, and Purkinje fibers.

[0169] Therefore, using the techniques described herein, high-resolution SEMD data (representing MCEA) is achieved by sampling different combinations of high temporal resolution (e.g., 8 kHz) and high data resolution (e.g., 24-bit) electric fields generated during the desired segment between the relatively weak PR segment or the end of the P wave and the beginning of the R wave (when the beginning of the Q wave cannot be detected or otherwise determined) through appropriate noise filtering and / or averaging across heartbeats. This SEMD data can be plotted and displayed to clinicians in real time as a spatial curve. Furthermore, using the techniques proposed herein, the location and orientation of the dipole emission catheter or electrode can be clearly presented in the same reference frame as the spatial curve of the SEMD data, enabling clinicians to navigate the catheter to a targeted location in the cardiac conduction system. That is, MCEA data can be overlaid with an image of the heart in real time. The catheter can also be visible in the image, thereby providing at least the catheter tip, the MCEA data on the cardiac conduction system, and a 3D view of the heart. Clinicians can then use the 3D image to navigate the catheter to the desired location in the heart. The catheter can then be used to apply therapy (e.g., ablation, attachment of pacing leads or other electrodes, etc.) to the desired location on the heart.

[0170] In some embodiments, the system can be configured to provide feedback on whether an electrode is positioned to capture the conduction system (i.e., induce conduction system pacing) and / or confirm the specific physiological location of the electrode (His bundle, LBB, RBB, etc.). The system can also indicate whether the electrode is positioned outside the conduction system pathway. In some embodiments, the system can confirm conduction system capture (i.e., pacing relative to intrinsic) by measurements with and without electrode pacing. Such measurements can include QRS interval, QRS width, QRS axis, latent-to-QRS interval, QRS morphological transition, pacing latency, positive / negative QRS presentation in different leads of a 12-lead ECG (e.g., positive QRS in lead II), and additional 12-lead ECG measurements (e.g., R-wave peak time in lead V5 or V6). Such measurements can be captured directly from other electrodes or synthesized. In some embodiments, the system can confirm conduction system pacing based on the morphology of MCEA spatial curves with and without pacing.

[0171] Now for reference Figure 11 A hardware block diagram of a computing device 600, which can perform operations as described herein, is depicted. Figures 3 to 10C The technical discussion described herein relates to operational functions. In various embodiments, computing devices or equipment (such as computing device 600 or any combination of computing devices 600) can be configured for use with... Figure 3 The technical discussion describes any entity that performs the operation of the various components discussed herein (e.g., system 10, data collection system 100, sensor positioning system 200, data processing system 300, display system 400, CPU 40, scanner 60 and / or imager 80).

[0172] In at least one embodiment, computing device 600 can be any device that may include one or more processors 602, one or more memory elements 604, storage device 606, bus 608, one or more network processor units 610 interconnected with one or more network input / output (I / O) interfaces 612, one or more I / O interfaces 614, and control logic 620. In various embodiments, instructions related to the logic of computing device 600 may overlap in any way and are not limited to the specific assignment of instructions and / or operations described herein.

[0173] In at least one embodiment, processor 602 is at least one hardware processor configured to perform various tasks, operations, and / or functions of computing device 600 as described herein, according to software and / or instructions configured for computing device 600. Processor 602 (e.g., hardware processor) can execute any type of data-related instructions to implement the operations described in detail herein. In one instance, processor 602 can transform elements or artifacts (e.g., data, information) from one state or thing to another. Any potential processing element, microprocessor, digital signal processor, controller, system, CPU, GPU, device, and / or machine described herein can be interpreted as encompassed within the broad term "processor." In some embodiments, processor 602 utilizes a graphics processing unit (GPU) or other parallel processing architecture to process multiple threads simultaneously, such as simultaneously evaluating the SED error or average value over different intervals of multiple voxels.

[0174] In at least one embodiment, memory element 604 and / or storage device 606 are configured to store data, information, software, and / or instructions associated with computing device 600, and / or logic configured for memory element 604 and / or storage device 606. For example, in various embodiments, any combination of memory element 604 and / or storage device 606 may be used to store any logic described herein (e.g., control logic 620) for computing device 600. It should be noted that in some embodiments, memory 606 may be combined with (or vice versa) memory element 604, or may overlap / exist in any other suitable manner.

[0175] In at least one embodiment, bus 608 can be configured as an interface enabling one or more elements of computing device 600 to communicate for exchanging information and / or data. Bus 608 can be implemented using any architecture designed to pass control, data, and / or information between processors, memory elements / storage devices, peripherals, and / or any other hardware and / or software components that can be configured for computing device 600. In at least one embodiment, bus 608 can be implemented as a fast kernel-managed interconnect, potentially utilizing shared memory (e.g., logic) between processes, which can enable efficient communication paths between processes.

[0176] In various embodiments, the network processor unit 610 may enable communication between the computing device 600 and other systems, entities, etc., via the network I / O interface 612 (wired and / or wireless) to facilitate the operation discussed with respect to the various embodiments described herein. In various embodiments, the network processor unit 610 may be configured as a combination of hardware and / or software, such as one or more Ethernet drivers and / or controllers or interface cards, Fibre Channel (e.g., optical) drivers and / or controllers, wireless receivers / transmitters / transceivers, baseband processors / modems, and / or other similar network interface drivers and / or controllers now known or developed hereafter to enable communication between the computing device 600 and other systems, entities, etc., to facilitate the operation of the various embodiments described herein. In various embodiments, the network I / O interface 612 may be configured as one or more Ethernet ports, Fibre Channel ports, any other I / O ports, and / or antennas / antenna arrays now known or developed hereafter. Therefore, the network processor unit 610 and / or the network I / O interface 612 may include suitable interfaces for receiving, transmitting, and / or otherwise transferring data and / or information to other devices and / or systems.

[0177] I / O interface 614 allows input and output of data and / or information with other entities that can be connected to computing device 600. For example, I / O interface 614 can provide connectivity to external devices such as keyboards, keypads, touchscreens, and / or any other suitable input and / or output devices now known or developed in the future. In some cases, external devices may also include portable computer-readable (non-transitory) storage media such as database systems, thumb drives, portable optical discs or disks, and memory cards. In still other cases, external devices may be mechanisms for displaying data to a user, such as computer monitors, displays, etc.

[0178] In various embodiments, control logic 620 may include instructions that, when executed, cause processor 602 to perform operations, which may include, but are not limited to, providing overall control operations for the computing device; interacting with other entities, systems, etc., described herein; maintaining and / or interacting with stored data, information, parameters, etc. (e.g., memory elements, storage devices, data structures, databases, tables, etc.); combinations thereof; and / or the like, to facilitate various operations of the embodiments described herein.

[0179] The programs described herein (e.g., control logic 620) can be identified based on the applications in which they are implemented in particular embodiments. However, it should be understood that any particular programmatic terminology used herein is merely for convenience; therefore, the embodiments herein should not be limited to the use described only in any particular application identified and / or implied by such terminology.

[0180] In various embodiments, any entity or device as described herein may store data / information in any suitable volatile and / or non-volatile memory item (e.g., magnetic hard disk drive, solid-state hard disk drive, semiconductor storage device, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), application-specific integrated circuit (ASIC), etc.), software, logic (fixed logic, hardware logic, programmable logic, analog logic, digital logic), hardware, and / or any other suitable component, device, element, and / or object as appropriate. Any memory item discussed herein should be construed as encompassing the broad term 'memory element'. Data / information tracked and / or sent to one or more entities as discussed herein may be provided in any database, table, register, list, cache, memory, and / or storage structure: all of which may be referenced at any suitable timeframe. Any such storage options may also be included in the broad term 'memory element' as used herein.

[0181] It should be noted that in some example embodiments, the operations as described herein may be implemented by logic encoded in one or more tangible media capable of storing instructions and / or digital information, and may include non-transitory tangible media and / or non-transitory computer-readable storage media (e.g., embedded logic provided in ASICs, digital signal processing (DSP) instructions, software [potentially including object code and source code], etc.) for execution by one or more processors and / or other similar machines. Typically, memory element 604 and / or storage device 606 may store data, software, code, instructions (e.g., processor instructions), logic, parameters, and / or combinations thereof for the operations described herein. This includes memory element 604 and / or storage device 606 capable of storing data, software, code, instructions (e.g., processor instructions), logic, parameters, combinations thereof, etc., which are executed to perform the operations according to the teachings of this disclosure.

[0182] In some cases, the software of this embodiment may be obtained through non-transitory computer-readable media (e.g., magnetic or optical media, magneto-optical media, CD-ROMs, DVDs, storage devices, etc.), downloadable files, file packages, objects, packages, and / or containers of a fixed or portable program product device. In some cases, the non-transitory computer-readable storage medium may also be removable. For example, in some embodiments, a removable hard disk drive may be used as a memory / storage device. Other examples may include optical discs and disks, thumb drives, and smart cards, which may be inserted and / or otherwise connected to a computing device for transfer to another computer-readable storage medium.

[0183] With respect to data storage in the embodiments presented herein, the embodiments may employ any number of any conventional or other databases, data storage devices or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information.

[0184] It should be noted that in this specification, the submission of various features (e.g., elements, structures, nodes, modules, components, engines, logic, steps, operations, functions, characteristics, etc.) included in 'one embodiment,' 'example embodiment,' 'an embodiment,' 'another embodiment,' 'some embodiments,' 'various embodiments,' 'other embodiments,' 'alternative embodiments,' etc., is intended to mean that any such feature is included in one or more embodiments of this disclosure, but may or may not be combined in the same embodiment. It should also be noted that modules, engines, clients, controllers, functions, logic, etc., as used herein, may include executable files containing instructions that can be understood and processed on servers, computers, processors, machines, computing nodes, combinations thereof, etc.; and may further include library modules, object files, system files, hardware logic, software logic, or any other executable modules loaded during execution.

[0185] It should also be noted that the operations and steps described with respect to the foregoing figures only illustrate some possible scenarios that can be performed by one or more entities discussed herein. Where appropriate, some of these operations may be deleted or removed, or these steps may be modified or altered without departing from the scope of the presented concepts. Furthermore, the timing and sequence of these operations can be significantly altered, while still achieving the results taught in this disclosure. The foregoing operational flow has been provided for the purposes of example and discussion. The embodiments offer considerable flexibility, as any suitable arrangement, timing, configuration, and timing mechanism can be provided without departing from the teachings of the concepts discussed.

[0186] Example 1. A system for mapping at least a portion of a cardiac conduction pathway in a patient, the system comprising: a plurality of sensors; a data collection system for collecting sensor data from the plurality of sensors; a data processing system for calculating center of electrical activity (CEA) data based on the sensor data; and a display system for presenting a three-dimensional (3D) model of a portion of the cardiac conduction pathway based on the calculated CEA data.

[0187] Example 2. The system according to Example 1, wherein the CEA data is determined by calculating a single equivalent dipole (SED).

[0188] Example 3. The system according to Example 1 or 2, wherein the 3D model of the portion of the cardiac conduction pathway is displayed relative to an image of the gross anatomy of the heart.

[0189] Example 4. The system according to any one of Examples 1 to 3, wherein the plurality of sensors detect signals propagating through the segment of the cardiac conduction pathway during the PR segment of the cardiac cycle.

[0190] Example 5. The system according to any one of Examples 1 to 4, wherein the display system displays the portion of the cardiac conduction pathway located between the atrioventricular (AV) node and Purkinje fibers of the heart.

[0191] Example 6. A system according to any one of Examples 1 to 5, wherein the data collection system includes a digital converter for generating high-resolution data from extremely low voltage signals sensed by the plurality of sensors.

[0192] Example 7. The system according to Example 6, wherein the voltage signal is less than 0.1 mV.

[0193] Example 8. A system according to any one of Examples 1 to 7, wherein the data processing system enhances the sensor data by one or more of the following: a low-pass filter; a high-pass filter; common-mode rejection; and / or differential weighting of data from different sensors among the plurality of sensors.

[0194] Example 9. The system according to any one of Examples 1 to 8, further comprising a sensor positioning system for identifying the position of each of the plurality of sensors in 3D space.

[0195] Example 10. The system according to Example 9, wherein the sensor positioning system includes a scanner, CT machine and / or MRI machine configured to generate 3D images.

[0196] Example 11. The system according to Example 10, wherein a machine learning algorithm trained to identify sensors in a 3D image identifies and locates the plurality of sensors in the 3D image generated by the sensor localization system.

[0197] Example 12. The system according to Example 9 further includes clothing and / or straps for housing the plurality of sensors.

[0198] Example 13. The system according to any one of Examples 1 to 12, wherein the data collection system is configured to indicate to a user whether a particular sensor among the plurality of sensors is improperly located and / or malfunctions.

[0199] Example 14. The system according to any one of Examples 1 to 13, wherein the data collection system is configured to cause one or more of the plurality of sensors to emit one or more signals.

[0200] Example 15. A system according to any one of Examples 1 to 14, wherein the system is configured to filter the sensor data by applying different wideband pass filters and / or narrowband pass filters to selected frequencies.

[0201] Example 16. The system according to Example 15, wherein the selected frequency is between about 0.5 Hz and 55 Hz or between about 65 Hz and 300 Hz.

[0202] Example 17. The system according to Example 15, wherein the data processing system is configured to remove CEA data corresponding to the time point in time at which the voltage is below a threshold at a specific time point.

[0203] Example 18. The system according to any one of Examples 1 to 17, wherein each of the plurality of sensors is weighted when determining the CEA based on the voltage drop across at least one chordae tendineae among the sensors.

[0204] Example 19. The system according to any one of Examples 1 to 18, wherein the location of a portion of the cardiac conduction pathway is determined by combining CEA data from multiple cardiac cycles.

[0205] Example 20. The system according to Example 19, wherein the combined CEA data from multiple cardiac cycles take into account the differences caused by the movement of the heart during each cardiac cycle.

[0206] Example 21. The system according to Example 19, wherein the combined CEA data includes combining the CEA data from the plurality of cardiac cycles into a best-fit model.

[0207] Example 22. The system according to Example 21, wherein the system is further configured to use data collected from the transmitting device to determine the location of the septum of the patient's heart.

[0208] Example 23. The system according to Example 22, wherein the positioning of the diaphragm constrains the best-fit model of the cardiac conduction pathway.

[0209] Example 24. The system according to Example 22, wherein the system is used to navigate a catheter to a target on the diaphragm.

[0210] Example 25. The system according to Example 21, wherein the probability distribution of the location of the cardiac conduction pathway is graphically displayed on an image of the diaphragm.

[0211] Example 26. The system according to any one of Examples 1 to 25, further comprising a transmitting device, wherein the data processing system is configured to determine the positioning of the transmitting device relative to a portion of the cardiac conduction pathway.

[0212] Example 27. The system according to Example 26 further includes a control device connected to the proximal end of the transmitting device.

[0213] Example 28. The system according to Example 27, wherein the control device activates and / or controls the signal emitted by the transmitting device.

[0214] Example 29. The system according to Example 27, wherein the control device controls the movement of the transmitting device.

[0215] Example 30. The system according to any one of Examples 1 to 29, further comprising a transmitting device, wherein the data processing system is configured to determine the orientation of the transmitting device relative to the location of the portion of the cardiac conduction pathway.

[0216] Example 31. The system according to any one of Examples 1 to 30, wherein the location of the portion of the cardiac conduction pathway is determined before and after the treatment is administered to the patient.

[0217] Example 32. The system according to any one of Examples 1 to 31, wherein the data processing system enhances the sensor data by selecting a filter based on whether the implanted pacing lead has recently delivered a pacing signal.

[0218] Example 33. The system according to any one of Examples 1 to 32, wherein the system is further used to navigate a catheter to a target in the heart.

[0219] Example 34. The system according to any one of Examples 1 to 33, wherein the system is also capable of being used to develop pacing strategies for patients.

[0220] Example 35. The system according to any one of Examples 1 to 34, wherein the system is also capable of determining whether conduction system pacing has been achieved.

[0221] Example 36. A method for mapping at least a portion of a cardiac conduction pathway, the method comprising: sensing signals indicating cardiac electrical signals propagating through the cardiac conduction pathway via sensors; combining the signals from each sensor via a data collection system to generate a first data stream; identifying waveforms from the first data stream via a data processing system through low-resolution sampling; sampling segments of the identified waveforms from the first data stream at high resolution via the data processing system to generate a second data stream; determining center of electrical activity (CEA) data based on the second data stream via the data processing system; and generating a three-dimensional (3D) model of the CEA data in real time via a display system, wherein the 3D model indicates the cardiac conduction pathway.

[0222] Example 37. The method described in Example 36, wherein the CEA data is determined to contain a single equivalent dipole (SED) analysis performed on the second data stream.

[0223] Example 38. The method according to Example 36 or 37 further includes generating a real-time image of the heart, and overlaying the 3D model with reference to the real-time image of the heart.

[0224] Example 39. The method according to any one of Examples 36 to 38, wherein the identified waveform is an electrocardiogram comprising a P wave, a QRS complex, and a T wave.

[0225] Example 40. The method according to Example 39, wherein the segment of the identified waveform is the PR segment of the identified waveform.

[0226] Example 41. The method according to any one of Examples 36 to 40, further comprising determining the location of the sensor relative to the heart of the subject.

[0227] Example 42. The method according to Example 41, wherein determining the location of the sensor comprises scanning the subject and the sensor, wherein the sensor is disposed on the subject's body.

[0228] Example 43. The method according to Example 41, wherein the determination of CEA data is further based on the determined positioning of the sensor.

[0229] Example 44. The method according to any one of Examples 36 to 43, further comprising sensing a monopole signal from a transmitting device disposed within the heart of the subject via the sensor.

[0230] Example 45. The method according to Example 44 further includes using SED analysis through the processing system to determine the location and orientation of the transmitting device.

[0231] Example 46. The method according to Example 45 further comprises generating a 3D representation of the transmitting device relative to the 3D model indicating the cardiac conduction pathway via the display system, wherein the 3D representation indicates the positioning and orientation of the transmitting device in real time.

[0232] Example 47. The method according to Example 46 further includes navigating the transmitting device such that the transmitting device approaches a desired portion of the cardiac conduction pathway based on the 3D representation.

[0233] Example 48. The method according to Example 46 further includes the detection electrode being placed close to the cardiac conduction pathway for capture.

[0234] Each example embodiment disclosed herein has been included to present one or more different features. However, all disclosed example embodiments are designed to work together as part of a single larger system or method. This disclosure explicitly contemplates composite embodiments that combine multiple previously discussed features from different example embodiments into a single system or method.

[0235] Although the invention has been described and illustrated in detail with reference to specific embodiments thereof, it is not intended to be limited to the details shown, as it will be apparent that various modifications and structural changes can be made without departing from the scope of the invention and within the scope and equivalents of the claims. Furthermore, various features from one embodiment may be incorporated into another. Therefore, it should be understood that the appended claims, as set forth in the following claims, should be interpreted broadly and in accordance with the scope of this disclosure.

[0236] Reference can be made to the spatial relationships between various components and the spatial orientation of various aspects of the components as depicted in the accompanying drawings. However, as those skilled in the art will recognize upon a complete reading of this disclosure, the apparatus, components, members, and devices described herein can be positioned in any desired orientation. Therefore, the use of terms such as “above,” “below,” “upper,” “lower,” “top,” “bottom,” or other similar terms to describe the spatial relationships between various components or to describe the spatial orientation of various aspects of such components should be understood as describing the relative relationships between components or the spatial orientation of various aspects of such components, since the components described herein can be oriented in any desired direction. When used to describe a series of dimensions and / or other characteristics (e.g., time, pressure, temperature, distance, etc.) of elements, operations, conditions, etc., the phrase “between X and Y” indicates a range including both X and Y.

[0237] For example, it should be understood that terms such as “left,” “right,” “top,” “bottom,” “front,” “rear,” “side,” “height,” “length,” “width,” “up,” “down,” “inner,” “outer,” “next,” and “external” as used herein describe reference points only and do not limit the invention to any particular orientation or configuration. Furthermore, the term “exemplary” as used herein is used to describe an instance or illustration. Any embodiment described herein in an exemplary manner should not be construed as a preferred or advantageous embodiment, but rather as an example or illustration of a possible embodiment.

[0238] Furthermore, reference numerals and / or letters may be repeated in various instances of this disclosure. Such repetition is for simplicity and clarity and does not in itself indicate a relationship between the various embodiments and / or configurations discussed.

[0239] Similarly, when used herein, the term “comprises” and its derivatives (e.g., “comprising”, etc.) should not be construed as having an exclusionary meaning; that is, these terms should not be interpreted as excluding the possibility that the described and defined content may include additional elements, steps, etc. At the same time, when used herein, the term “approximately” and its family of terms (e.g., “approximate”, etc.) should be understood as indicating a value very close to the value associated with the aforementioned term. That is, deviations from precise values ​​within a reasonable range should be acceptable, as those skilled in the art will understand that such deviations from the indicated values ​​are unavoidable due to inaccuracies in measurement, etc. The same applies to “about,” “around,” and “substantially.”

[0240] As used herein, unless explicitly stated otherwise, the phrases “at least one of…”, “one or more of…”, “and / or”, and variations thereof, etc., are open-ended expressions in operation that are both connective and disjoint for any and all possible combinations of the related listed items. For example, each of the expressions “at least one of X, Y, and Z”, “at least one of X, Y, or Z”, “one or more of X, Y, and Z”, “one or more of X, Y, or Z”, and “X, Y, and / or Z” can mean any of the following: 1) X, but not Y or Z; 2) Y, but not X or Z; 3) Z, but not X or Y; 4) X and Y, but not Z; 5) X and Z, but not Y; 6) Y and Z, but not X; or 7) X, Y, and Z.

[0241] Additionally, unless explicitly stated to the contrary, the terms “first,” “second,” “third,” etc., are intended to distinguish the specific nouns they modify (e.g., element, condition, node, outlet, inlet, valve, module, activity, operation, etc.). Unless explicitly stated to the contrary, the use of these terms is not intended to indicate any type of order, rank, importance, temporal sequence, or hierarchy of the modified nouns. For example, “first X” and “second X” are intended to refer to two “X” elements, which are not necessarily limited by any order, rank, importance, temporal sequence, or hierarchy of said two elements. Furthermore, as mentioned herein, “at least one of…” and “one or more of…” can be represented using the “(multiple)” nomenclature (e.g., one or more elements).

Claims

1. A system for mapping at least a portion of a cardiac conduction pathway in a patient, the system comprising: Multiple sensors; A data collection system is used to collect sensor data from the plurality of sensors; A data processing system for calculating electrical activity center (CEA) data based on the sensor data; as well as A display system for presenting a three-dimensional (3D) model of a portion of the cardiac conduction pathway based on calculated CEA data.

2. The system according to claim 1, wherein the CEA data is determined by calculating a single equivalent dipole (SED).

3. The system of claim 1, wherein the 3D model of the portion of the cardiac conduction pathway is displayed relative to an image of the gross anatomy of the heart.

4. The system of claim 1, wherein the plurality of sensors detect signals propagating through segments of the cardiac conduction pathway during the PR segment of the cardiac cycle.

5. The system of claim 1, wherein the display system displays the portion of the cardiac conduction pathway located between the atrioventricular (AV) node and Purkinje fibers of the heart.

6. The system of claim 1, wherein the data collection system includes a digital converter for generating high-resolution data from extremely low voltage signals sensed by the plurality of sensors.

7. The system of claim 6, wherein the voltage signal is less than 0.1 mV.

8. The system of claim 1, wherein the data processing system enhances the sensor data by one or more of the following: low-pass filter; high-pass filter; common-mode rejection; and / or differential weighting of data from different sensors among the plurality of sensors.

9. The system of claim 1, further comprising a sensor positioning system for identifying the position of each of the plurality of sensors in 3D space.

10. The system of claim 9, wherein the sensor positioning system comprises a scanner, CT scanner, and / or MRI machine configured to generate 3D images.

11. The system of claim 10, wherein a machine learning algorithm trained to identify sensors in a 3D image identifies and locates the plurality of sensors in the 3D image generated by the sensor localization system.

12. The system of claim 9, further comprising clothing and / or straps for housing the plurality of sensors.

13. The system of claim 1, wherein the data collection system is configured to indicate to a user whether a particular sensor among the plurality of sensors is improperly located and / or malfunctions.

14. The system of claim 1, wherein the data collection system is configured to cause one or more of the plurality of sensors to emit one or more signals.

15. The system of claim 1, wherein the system is configured to filter the sensor data by applying different wideband pass filters and / or narrowband pass filters to selected frequencies.

16. The system of claim 15, wherein the selected frequency is between about 0.5 Hz and 55 Hz or between about 65 Hz and 300 Hz.

17. The system of claim 15, wherein the data processing system is configured to remove CEA data corresponding to the time point in which the voltage is below a threshold at a specific time point in the time frame.

18. The system of claim 1, wherein each of the plurality of sensors is weighted when determining the CEA based on the voltage drop across at least one chordae tendineae among the plurality of sensors.

19. The system of claim 1, wherein the location of a portion of the cardiac conduction pathway is determined by combining CEA data from multiple cardiac cycles.

20. The system of claim 19, wherein the combined CEA data from multiple cardiac cycles take into account differences caused by cardiac motion during each cardiac cycle.

21. The system of claim 19, wherein the combined CEA data comprises combining the CEA data from the plurality of cardiac cycles into a best-fit model.

22. The system of claim 21, wherein the system is further configured to use data collected from the transmitting device to determine the location of the septum of the patient's heart.

23. The system of claim 22, wherein the positioning of the diaphragm constrains the best-fit model of the cardiac conduction pathway.

24. The system of claim 22, wherein the system is used to navigate a catheter to a target on the diaphragm.

25. The system of claim 22, wherein the probability distribution of the location of the cardiac conduction pathway is displayed graphically on an image of the diaphragm.

26. The system of claim 1, further comprising a transmitting device, wherein the data processing system is configured to determine the positioning of the transmitting device relative to a portion of the cardiac conduction pathway.

27. The system of claim 26, further comprising a control device connected to a proximal end of the transmitting device.

28. The system of claim 27, wherein the control device activates and / or controls the signal transmitted by the transmitting device.

29. The system of claim 27, wherein the control device controls the movement of the transmitting device.

30. The system of claim 1, further comprising a transmitting device, wherein the data processing system is configured to determine the orientation of the transmitting device relative to the portion of the cardiac conduction pathway.

31. The system of claim 1, wherein the location of the portion of the cardiac conduction pathway is determined before and after the treatment is administered to the patient.

32. The system of claim 1, wherein the data processing system enhances the sensor data by selecting a filter based on whether the implanted pacing lead has recently delivered a pacing signal.

33. The system of claim 1, wherein the system is further configured to navigate a catheter to a target in the heart.

34. The system of claim 1, wherein the system is also capable of developing pacing strategies for patients.

35. The system of claim 1, wherein the system is further capable of determining whether conduction system pacing has been achieved.

36. A method for mapping at least a portion of a cardiac conduction pathway, the method comprising: The sensor senses a signal indicating the propagation of cardiac electrical signals through the cardiac conduction pathway; The signals from each sensor are combined using a data collection system to generate a first data stream; The data processing system identifies waveforms from the data stream through low-resolution sampling of the first data stream; The data processing system samples segments of the identified waveform of the first data stream at high resolution to generate a second data stream. The data processing system determines the Electrical Activity Center (CEA) data based on the second data stream; as well as The system generates a three-dimensional (3D) model of the CEA data in real time, wherein the 3D model indicates the cardiac conduction pathway.

37. The method of claim 36, wherein determining the CEA data includes performing a single equivalent dipole (SED) analysis on the second data stream.

38. The method of claim 36, further comprising generating a real-time image of the heart, and overlaying the 3D model with reference to the real-time image of the heart.

39. The method of claim 36, wherein the identified waveform is an electrocardiogram comprising a P wave, a QRS complex, and a T wave.

40. The method of claim 39, wherein the segment of the identified waveform is the PR segment of the identified waveform.

41. The method of claim 36, further comprising determining the location of the sensor relative to the heart of the subject.

42. The method of claim 41, wherein determining the location of the sensor comprises scanning the subject and the sensor, wherein the sensor is disposed on the subject's body.

43. The method of claim 41, wherein the determination of CEA data is further based on the determined positioning of the sensor.

44. The method of claim 36, further comprising sensing a monopole signal from a transmitting device disposed within the heart of the subject via the sensor.

45. The method of claim 44, further comprising using SED analysis via the processing system to determine the location and orientation of the transmitting device.

46. ​​The method of claim 45, further comprising generating a 3D representation of the transmitting device relative to the 3D model indicating the cardiac conduction pathway via the display system, wherein the 3D representation indicates the positioning and orientation of the transmitting device in real time.

47. The method of claim 46, further comprising navigating the transmitting device such that the transmitting device approaches a desired portion of the cardiac conduction pathway based on the 3D representation.

48. The method of claim 46, further comprising that the detection electrode has been positioned close to the cardiac conduction pathway for capture.

Images