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Framework for performing electrocardiography analysis

An electrocardiographic, image-based technique applied to equipment that performs analysis of electrocardiogram, G signal and automatically detects AFib or VFib. It can solve problems such as shallow neural network and inability to collect context features.

Pending Publication Date: 2022-01-07
TENCENT AMERICA LLC
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Problems solved by technology

These neural networks are very shallow and cannot capture contextual features

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  • Framework for performing electrocardiography analysis
  • Framework for performing electrocardiography analysis
  • Framework for performing electrocardiography analysis

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Embodiment Construction

[0020] Atrial fibrillation (AFib) and ventricular fibrillation (VFib) are two cardiac arrhythmias encountered in clinical practice. Most detection methods for AFib and VFib involve electrocardiogram (ECG) analysis. An ECG test records the electrical activity of the heart and displays the pulses as waveforms. A damaged heart conducts irregular pulses, causing fluttering waves in the ECG.

[0021] In at least one embodiment of the present application, a deep learning based system and method is provided to detect and classify irregular pulses between regular heartbeats and AFib or VFib. For example, the system of the embodiment of the present application takes a one-dimensional ECG signal as an input, and transforms the one-dimensional ECG signal into a multi-dimensional image sequence, and the multi-dimensional image sequence encodes more temporal information and spatial information. Then, the data was fed to the system's deep learning-based model training framework for AFib a...

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Abstract

A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Patent Application No. 16 / 658,943, filed October 21, 2019, in the U.S. Patent and Trademark Office, the disclosure of which is incorporated by reference in its entirety. Background technique [0003] An EKG records electrical signals in the heart. It is a common test used to detect heart problems such as atrial fibrillation (AFib), ventricular fibrillation (VFib), and myocardial infarction. Most conventional electrocardiogram (ECG) analysis methods are based on one-dimensional digital signals. These methods apply digital signal processing algorithms such as wavelet transforms to extract features from ECG signals such as P-wave and R-wave intervals. However, these methods lack reliability when some features are missed in the inspection. Some methods apply neural networks to AFib detection. These neural networks are very shallow and cannot capture contextual features. Contents...

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Application Information

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IPC IPC(8): A61B5/00
CPCG16H50/20G16H30/40G06N3/08G06N3/045G06N3/044
Inventor 王旭王堃张尚卿涂旻陈晓钟范伟
Owner TENCENT AMERICA LLC