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Detection of activation in electrograms using neural-network-trained preprocessing of intracardiac electrograms

A technique of electrogram and preprocessing, applied in biological neural network model, neural architecture, diagnostic signal processing, etc.

Pending Publication Date: 2022-02-22
BIOSENSE WEBTER (ISRAEL) LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This makes the analysis of unipolar signals challenging

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  • Detection of activation in electrograms using neural-network-trained preprocessing of intracardiac electrograms
  • Detection of activation in electrograms using neural-network-trained preprocessing of intracardiac electrograms
  • Detection of activation in electrograms using neural-network-trained preprocessing of intracardiac electrograms

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Embodiment Construction

[0029] overview

[0030] Intracardiac electrophysiological (EP) mapping is a catheter-based method that can sometimes be used to characterize cardiac EP wave propagation abnormalities, such as those leading to arrhythmias. In a typical catheter-based procedure, the distal end of a catheter comprising a plurality of sensing electrodes is inserted into the heart to acquire a set of data points including (i) the measured location on the wall tissue of the heart chamber and (ii) a corresponding EP signal, the EP mapping system can generate an EP mapping map according to the EP signal. An example of an EP map that can be used to diagnose cardiac arrhythmias is an EP timing map map of a region of heart chamber wall tissue (referred to as a local activation time (LAT) map).

[0031] In order to generate a LAT map, a processor may have to analyze intracardiac ECG signals (hereinafter referred to as electrograms (EGMs)) acquired at various points in the heart to identify in each sig...

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Abstract

The invention provides the detection of activation in electrograms using neural-network-trained preprocessing of intracardiac electrograms. A method includes collecting a plurality of bipolar electrograms and respective unipolar electrograms of patients, the electrograms including annotations in which one or more human reviewers have identified and marked a window-of-interest and one or more activation times inside the window-of-interest. A ground truth data set is generated from the electrograms, for training at least one electrogram-preprocessing step of a Machine Learning (ML) algorithm. The ML algorithm is applied to the electrograms, to at least train the at least one electrogram-preprocessing step, so as to detect an occurrence of an activation in a given bipolar electrogram within the window-of-interest.

Description

technical field [0001] The present invention relates generally to the analysis of intracardiac electrophysiological signals, and in particular to the use of machine learning (ML) to assess electrical propagation in the heart. Background technique [0002] In patients with cardiac arrhythmias, pulses of electrical activity can follow pathological paths in cardiac tissue. In electrophysiological research, one of the goals of the physician is to understand the path followed by pulses of electrical activity, for example by inserting a multi-electrode catheter into a patient's heart and measuring intracardiac ECG signals through the catheter. Typically, a catheter is passed through blood vessels to the heart to measure electrical activity in the endocardium. Alternatively, a catheter may be inserted through an incision in the chest in order to measure electrical activity in the epicardium. [0003] To measure electrical activity, some electrodes of the catheter are brought into...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/283A61B5/318A61B5/346
CPCA61B5/283A61B5/318A61B5/7264A61B5/72A61B5/346A61B5/349A61B5/7267G16H50/20G06N3/08A61B5/287G06N3/04A61B5/339
Inventor E·拉文纳N·S·卡茨A·E·基里亚索夫E·I·库日娜列娃A·V·肖夫昆
Owner BIOSENSE WEBTER (ISRAEL) LTD