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WO2025207628A1 - Système d'identification d'origine d'arythmie - Google Patents

Système d'identification d'origine d'arythmie

Info

Publication number
WO2025207628A1
WO2025207628A1 PCT/US2025/021336 US2025021336W WO2025207628A1 WO 2025207628 A1 WO2025207628 A1 WO 2025207628A1 US 2025021336 W US2025021336 W US 2025021336W WO 2025207628 A1 WO2025207628 A1 WO 2025207628A1
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WO
WIPO (PCT)
Prior art keywords
site
sites
patient
accuracy
adjusted
Prior art date
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Pending
Application number
PCT/US2025/021336
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English (en)
Inventor
Christopher J. T. VILLONGCO
David E. KRUMMEN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Vektor Group Inc
University of California Berkeley
University of California San Diego UCSD
Original Assignee
Vektor Group Inc
University of California Berkeley
University of California San Diego UCSD
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Publication date
Application filed by Vektor Group Inc, University of California Berkeley, University of California San Diego UCSD filed Critical Vektor Group Inc
Publication of WO2025207628A1 publication Critical patent/WO2025207628A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/364—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/361—Detecting fibrillation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/363—Detecting tachycardia or bradycardia
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/367—Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device

Definitions

  • Heart disorders can cause symptoms, morbidity (e.g., syncope or stroke), and mortality.
  • Common heart disorders include atrial fibrillation (“AF”), ventricular fibrillation (“VF”), atrial tachycardia (“AT”), ventricular tachycardia (“VT”), atrial flutter, and premature ventricular contractions (“PVC”).
  • the sources of heart disorders include stable electrical rotors, recurring electrical focal sources, and so on. These sources are important drivers of sustained or clinically significant episodes of heart disorders.
  • These heart disorders can be treated with therapeutic ablation, radiofrequency waves, pulsed fields, cryogenic, ultrasound, external radiation sources, and so on by targeting the source of the heart disorder. To target the source of a heart disorder, the source location of the heart disorder should be identified.
  • one method uses an electrode basket catheter that needs to be inserted into the heart (e.g., left ventricle) to collect from within the heart measurements of the electrical activity of the heart, such as during an induced VF. The measurements can then be analyzed to help identify a possible source location.
  • an electrode basket catheter is expensive.
  • Another method uses a body surface vest with electrodes to collect from the patient’s body surface measurements, which can be analyzed to help identify a possible source location.
  • a body surface vest is expensive, is difficult to manufacture, and may interfere with the placement of defibrillator pads needed after inducing a fibrillation to collect measurement during fibrillation.
  • the vest analysis requires a CT scan and is unable to sense the interventricular and interatrial septa.
  • the identification has often been based on analysis of a 12-lead electrocardiogram (ECG) to identify patterns and criteria indicative of whether the arrhythmia is an LVOT arrhythmia (i.e., originating from the LVOT) or an RVOT arrhythmia (i.e., originating from the RVOT).
  • ECG electrocardiogram
  • An LVOT arrhythmia may be indicated when the ECG shows a right bundle branch block (RBBB) pattern or shows a less pronounced inferior axis due to its closer proximity to the left side of the heart.
  • RBBB right bundle branch block
  • an RVOT arrhythmia may be indicated when an ECG shows a left bundle branch block (LBBB) pattern with an inferior axis.
  • an LVOT arrhythmia or an RVOT arrhythmia is incorrectly indicated, the treatment based on that indication may be suboptimal. For example, if an LVOT arrhythmia is incorrectly indicated as an RVOT arrhythmia, a patient may be treated less aggressively than if the OT was correctly indicated. Such a less aggressive treatment may initially start with lifestyle changes or medication. In contrast, if the LVOT arrhythmia was correctly indicated, the patient may be treated more aggressively such as with an ablation procedure because of LV’s critical role in systemic circulation. In addition to identifying the correct OT, it is important to identify the precise source location within the OT to help ensure that an ablation treatment is as effective as possible.
  • both atrial arrhythmias and ventricular arrhythmias can originate at various locations throughout the heart.
  • VTs may originate in various regions of the left ventricle such as near the LVOT, fascicular regions, the mitral annulus, or the septal area.
  • ATs may originate at various regions near the pulmonary veins, the tricuspid or mitral valve annuli, or the coronary sinus. The accurate identification of the origin of any arrhythmia and subsequent treatment based on that identification can help improve patient outcomes.
  • Figure 1 is a flow diagram that illustrates an origin identification method for treating a patient in some embodiments.
  • Figure 2 is a block diagram that illustrates components of an origin identification system in some embodiments.
  • Figure 3 is a flow diagram that illustrates the processing of a generate adjusted site probabilities component of the object identification system in some embodiments.
  • Figure 4 is a flow diagram that illustrates the processing of an adjusted site probability graphic component of the ridge identification system in some embodiments.
  • an origin identification (OID) system combines techniques for identifying a site that is an origin (i.e., source location) to improve the accuracy of the identification.
  • the OID system combines a region identification technique or a region accuracy assessment technique with a site identification technique.
  • a region identification system may employ a region identification technique to identify a region such as the LVOT or the RVOT as the likely region of the origin of a PVC.
  • the region identification system may assign an OT region score (e.g., or probability) to each OT region as being the likely OT region of origin.
  • the region identification system may assign an OT region score based on analysis of characteristics of a patient ECG.
  • a site identification system may identify a specific site within the LVOT and a specific site within the RVOT as possible sites of the PVC.
  • the site identification system may assign an OT site score to an LVOT site and to an RVOT site as being the likely OT site of the origin.
  • the site identification system may assign an OT site score based on similarity of a patient ECG to an ECG (simulated or clinical) associated with an OT site.
  • the OID system may identify likely OT sites that may be the origin by combining the OT region scores and the OT site scores.
  • the OID system may allow a user to input the OT region scores or specify the ECG analysis technique from which the OT region scores are derived (or combinations of them) that are to be used to generate the adjusted OT site scores.
  • the OID system may generate multiple adjusted OT site probabilities for each site that are based on different mapping systems, ECG analysis techniques, and/or user inputs.
  • the OID system may display the multiple adjusted OT site probabilities to help inform treatment of a patient.
  • FIG. 3 is a flow diagram that illustrates the processing of a generate adjusted site probabilities component of the object identification system in some embodiments.
  • the object identification system 300 receives a patient ECG and generates adjusted site probabilities for potential origins of an arrhythmia.
  • the component applies the mapping system to the patient ECG to generate sites and site scores of potential origins of the arrhythmia.
  • the component selects sites with the highest site scores.
  • the component selects the next site of the sites with the highest site scores for further processing.
  • decision block 304 if all such sites have already been selected, then the component continues at block 306, else the component continues at block 305.
  • the component In block 305, the component generates an adjusted site score based on a region accuracy of the mapping system or an ECG analysis system. The component then loops to block 303 to select the next site. In block 306, the component sums the adjusted site scores of the sites. In block 307, the component selects the next site. In decision block 308, if all the sites have already been selected, then the component completes, else the component continues at block 309. In block 309, the component sets the adjusted site probability for that site to the adjusted site score for that site divided by the sum of the adjusted site scores and then loops to block 307 to select the next site.
  • decision block 404 if all the sites have already been selected, then the component continues at block 406, else the component continues at block 405.
  • the component adds to the 3D graphic an indication of the adjusted site probability of the site data that is associated with the selected site and loops to block 403 to select the next site.
  • the adjusted site probabilities and other site data may be displayed as text in a table.
  • the component displays the 3D graphic augmented with the indications derived from the site data.
  • decision block 407 if the processing of the component is to terminate, then the component completes, else the component continues at block 408.
  • the component receives a request to update the 3D graphic.
  • the request may be indicated in various ways such as a cursor hovering over an indication of a site, selecting a site from a list of displayed site data, and so on.
  • the component updates the displayed 3D graphic to reflect the request and loops to block 406 to display the updated 3D graphic.
  • the 3D mesh used to generate the graphic of a heart may be patient specific in the sense that it is generated based on the 3D images (e.g., computed tomography images) of the patient’s heart.
  • the 3D mesh may alternatively be a generic 3D mesh that is based on the cardiac geometry of a generic heart.
  • the 3D mesh may be a simulated 3D mesh that was employed to generate mappings of ECGs to sites used by the mapping system.
  • the simulated 3D mesh may be a 3D mesh of a simulation from which a simulated ECG was generated that matches the patient’s ECG.
  • the OID system may map those sites to corresponding sites of the patient-specific 3D mesh. After the sites are identified by the mapping system, the OID system may adjust those sites based on differences between the simulated 3D mesh and the patientspecific 3D mesh. The OID system may calculate site scores for the adjusted sites by applying the site score ML model to the patient ECG and the adjusted sites. Techniques for generating adjusted sites are described in PCT App. No. 2023/168017 entitled “Overall Ablation Workflow System” and published on September 7, 2023, which is hereby incorporated by reference.
  • a mapping system ML model may be trained using training data using a computational model of a heart.
  • the computational model models electromagnetic output of the heart over time based on a source configuration of the heart.
  • the electromagnetic output may represent electrical potential, a current, a magnetic field, and so on.
  • the source configuration may include information on geometry and muscle fibers of the heart, torso anatomy, scar locations, rotor locations, electrical properties, and so on, and the EM output is a collection of the electric potentials at various heart locations over time.
  • a simulation may be performed for simulation steps of a step size (e.g., 1 ms) to generate an EM mesh for that step.
  • the EM mesh may be a finite-element mesh that stores the value of the electric potential at each heart location for that step.
  • the left ventricle may be defined as having approximately 70,000 heart locations with the EM mesh storing an electromagnetic value for each heart location. If so, a three-second simulation with a step size of 1 ms would generate 3,000 EM meshes that each include 70,000 values. The collection of the EM meshes is the EM output for the simulation.
  • a computational model is described in C. T. Villongco, D. E. Krummen, P. Stark, J. H. Omens, & A. D.
  • the training data may be generated by running many simulations, each based on a different source configuration, which is a set of different values for the configuration parameters of the computational model.
  • the configuration parameters for the heart may be cardiac geometry, rotor location, focal source location, ventricular orientation in the chest, ventricular myofiber orientation, cardiomyocyte intracellular potential electrogenesis and propagation, and so on.
  • Each configuration parameter may have a set or range of possible values.
  • a focal source location parameter may have 20 possible locations in an LVOT. Since a simulation may be run for each combination of possible values, the number of simulations may be in the millions.
  • EM outputs of the simulations may be used to generate derived EM data such as a simulated ECG or a simulated VCG from the EM output of a simulation and the torso anatomy of the source configuration.
  • the library mappings may be the derived EM data and the source location (e.g., site of the origin).
  • the mapping ML model may be trained using the library mappings.
  • the library mapping may include source configuration parameters used in the simulation from which the simulated ECG was derived.
  • the mapping system may perform a calibration as described in the 754 patent for the identification of a simulated ECG that is similar to the patient ECG.
  • the region scores may factor in characteristics of the patient’s heart such as those of the configuration parameters as described above. For example, the accuracy of a mapping system or an ECG analysis system may vary between patients with different characteristics. The mapping system may be more accurate at identifying sites in patient who are younger than 50. In such a case, the OID system uses different region scores for those younger than 50 and those older than 50.
  • An ML model employed by the OID system may be any of a variety or combination of supervised, semi-supervised, self-supervised, unsupervised, or reinforcement learning ML models including a neural network such as fully connected, convolutional, recurrent, or autoencoder neural network, a restricted Boltzmann machine, a support vector machine, a Bayesian classifier, K-means clustering, K Nearest Neighbors (KNN), and so on.
  • the ML model is a deep neural network
  • the model is trained using training data that includes features derived from data and labels corresponding to the data.
  • the data may be images of ECGs with a feature being the image itself, and the labels may be sites.
  • the training results in a set of weights for the activation functions of the layers of the deep neural network.
  • the trained deep neural network can then be applied to new data to generate a label for that new data.
  • An ML model may generate values of discrete domain (e.g., classification), probabilities, and/or values of a continuous domain (e.g., regression value, classification probability).
  • a neural network model has three major components: architecture, loss function, and search algorithm.
  • the architecture defines the functional form relating the inputs to the outputs (in terms of network topology, unit connectivity, and activation functions).
  • the search in weight space for a set of weights that minimizes the loss function is the training process.
  • a neural network model may use a radial basis function (RBF) network and a standard or stochastic gradient descent as the search technique with backpropagation.
  • RBF radial basis function
  • An implementation of the OID system may employ any combination or sub-combination of the aspects and may employ additional aspects.
  • the processing of the aspects may be performed by one or more computing systems with one or more processors that execute computer-executable instructions that implement the aspects and that are stored on one or more computer-readable storage mediums.
  • the techniques described herein relate to a method wherein the mapping system is a machine learning (ML) model that inputs a cardiogram and outputs OT sites and OT site scores, the ML model being trained with training data that includes simulated cardiograms labeled with OT sites.
  • the techniques described herein relate to a method wherein the ML model is a neural network that inputs features derived from a cardiogram and outputs an OT site and an OT site score.
  • the techniques described herein relate to a method wherein the ML model is a convolutional neural network that inputs an image of a cardiogram and outputs an OT site and an OT site score.
  • the techniques described herein relate to a method wherein the cardiogram is represented as a voltage-time series. In some aspects, the techniques described herein relate to a method wherein the mapping system compares the patient cardiogram to a library of library cardiograms mapped to OT sites and OT site scores to identify OT sites mapped to library cardiograms that are similar to the patient cardiogram. In some aspects, the techniques described herein relate to a method wherein the OT region accuracy is derived from an assessment of accuracy of the mapping system in identifying OT regions that include OT sites. In some aspects, the techniques described herein relate to a method wherein the assessment of accuracy of the mapping system is based on the OT sites identified by the mapping system being targets of successful ablation procedures.
  • the techniques described herein relate to a method wherein the OT region accuracy is derived from an assessment of accuracy of an algorithm that is not based on the mapping system in identifying OT regions that include OT sites. In some aspects, the techniques described herein relate to a method wherein the OT region accuracy is derived from an assessment of accuracy of the mapping system in identifying OT regions that include OT sites and from an assessment of accuracy of an algorithm that is not based on the mapping system in identifying OT regions that include OT sites. In some aspects, the techniques described herein relate to a method wherein the OT scores are based on a similarity metric derived from similarity between the patient cardiogram and simulated cardiograms. In some aspects, the techniques described herein relate to a method wherein the OT scores are based on a probability of an OT site being origin of the arrhythmia.
  • the techniques described herein relate to a method wherein the outputting includes outputting an indication of an OT site to an ablation navigation device that controls movement of an ablation catheter to that OT site.
  • the techniques described herein relate to a method wherein the mapping system is a machine learning (ML) model that inputs a cardiogram and outputs OT sites and OT site scores that are probabilities, the ML model being trained with training data that includes simulated cardiograms labeled with OT sites.
  • the techniques described herein relate to a method wherein the mapping system compares the patient cardiogram to library cardiograms mapped to OT sites to identify OT sites mapped to library cardiograms that are similar to the patient cardiogram.
  • ML machine learning
  • the techniques described herein relate to a method wherein the accuracy is derived from an assessment of accuracy of an algorithm that is not based on the mapping system in identifying OT regions that include OT sites that are origins of arrhythmias. In some aspects, the techniques described herein relate to a method wherein the accuracy is derived from an assessment of accuracy of the mapping system in identifying OT regions that include OT sites and from an assessment of accuracy of an algorithm that is not based on the mapping system in identifying OT regions that include OT sites.
  • the techniques described herein relate to one or more computing systems for treating a patient having an arrhythmia
  • the one or more computing systems including: one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to: receive from a cardiogram collection device a patient cardiogram collected from the patient; identify a plurality of sites within the heart of the patient by applying a mapping system to the patient cardiogram to identify the sites, each site associated with a site score indicative of that site being an origin of the arrhythmia; generate an adjusted site score for each site based on a region score relating to identification of origins of arrhythmias with regions of a heart; and output indications of the adjusted sites scores; and one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions.
  • the techniques described herein relate to one or more computing systems wherein the adjusted site score is based on accuracy in identifying regions that include origins of arrhythmias. In some aspects, the techniques described herein relate to one or more computing systems wherein the accuracy is based on accuracy of the mapping system. In some aspects, the techniques described herein relate to one or more computing systems wherein the accuracy is based on accuracy of a technique other than the mapping system. In some aspects, the techniques described herein relate to one or more computing systems wherein the regions include a left ventricular outflow tract and a right ventricular outflow tract. In some aspects, the techniques described herein relate to one or more computing systems wherein the regions include the endocardium and the epicardium.
  • the techniques described herein relate to one or more computing systems wherein the regions include the anterior papillary muscle and the posterior papillary muscle. In some aspects, the techniques described herein relate to one or more computing systems wherein the regions include sub regions of an outflow tract.
  • the techniques described herein relate to a method performed by one or more computing systems for assessing potential target sites for an ablation procedure to treat a patient having an arrhythmia, the method including: receiving a patient cardiogram collected from the patient; identifying outflow tract (OT) sites within the heart of the patient by applying a mapping system to the patient cardiogram, each OT site associated with an OT site score indicating a likelihood that that OT site is an origin of the arrhythmia; for each of a plurality of OT sites, generating an adjusted OT site score based on the OT site score for that OT site and an accuracy associated with distinguishing between OT regions as containing an origin of an arrhythmia; and outputting indications based on the adjusted OT site scores to inform an ablation treatment for the arrhythmia.
  • OT outflow tract

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Abstract

L'invention concerne un système d'identification de sites d'origine d'une arythmie. Le système entre un ECG arythmique. Le système identifie des sites de la voie d'éjection (OT, de l'anglais "outflow tract") qui peuvent être l'origine de l'arythmie par application d'un système de mappage à l'ECG arythmique. Chaque site d'OT peut être associé à un score de site d'OT. Le système calcule un score de site d'OT ajusté pour chaque site d'OT qui est basé sur son score de site d'OT ajusté par une précision de région d'OT d'un système d'identification de région d'OT. Le système calcule également une probabilité de site d'OT ajustée qui est le score de site d'OT ajusté divisé par la somme des scores de site d'OT ajustés pour les sites d'OT. Le système génère et affiche ensuite un graphique d'un cœur avec une indication de la probabilité de site d'OT ajustée pour un site d'OT.
PCT/US2025/021336 2024-03-25 2025-03-25 Système d'identification d'origine d'arythmie Pending WO2025207628A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020038093A1 (en) * 2000-03-15 2002-03-28 Mark Potse Continuous localization and guided treatment of cardiac arrhythmias
US20140122048A1 (en) * 2012-10-30 2014-05-01 The Johns Hopkins University System and method for personalized cardiac arrhythmia risk assessment by simulating arrhythmia inducibility
US20190090774A1 (en) * 2017-09-27 2019-03-28 Regents Of The University Of Minnesota System and method for localization of origins of cardiac arrhythmia using electrocardiography and neural networks
US20200383595A1 (en) * 2019-06-10 2020-12-10 Vektor Medical, Inc. Heart graphic display system
WO2024044719A1 (fr) * 2022-08-25 2024-02-29 Vektor Medical, Inc. Affinement automatique de sélection d'électrogramme

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020038093A1 (en) * 2000-03-15 2002-03-28 Mark Potse Continuous localization and guided treatment of cardiac arrhythmias
US20140122048A1 (en) * 2012-10-30 2014-05-01 The Johns Hopkins University System and method for personalized cardiac arrhythmia risk assessment by simulating arrhythmia inducibility
US20190090774A1 (en) * 2017-09-27 2019-03-28 Regents Of The University Of Minnesota System and method for localization of origins of cardiac arrhythmia using electrocardiography and neural networks
US20200383595A1 (en) * 2019-06-10 2020-12-10 Vektor Medical, Inc. Heart graphic display system
WO2024044719A1 (fr) * 2022-08-25 2024-02-29 Vektor Medical, Inc. Affinement automatique de sélection d'électrogramme

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