Method for extracting f wave from single-lead electrocardiography signal

Patent metadata

Field	Value
Publication number	US-11375942-B2
Application number	US-202117497052-A
Country	US
Kind code	B2
Filing date	Oct 8, 2021
Priority date	Oct 9, 2020
Publication date	Jul 5, 2022
Grant date	Jul 5, 2022

Abstract

A method for extracting f wave from a single-lead electrocardiography signal is provided. The method includes: establishing a two-channel temporal convolutional neural network model, including two coding submodules, two mask estimation submodules, an information fusion module and two decoding submodules; constructing a training data set of ECG signals; training the two-channel temporal convolutional neural network model, and saving the trained model. The two-channel temporal convolution neural network encodes and decodes a QRST complex and the f wave respectively, and uses a mask of information fusion to estimate and construct regular items to restrict a distribution difference of QRST component coding features, so as to reduce the distortion of the QRST complex, select potential features of a QRST complex and f wave with high signal-to-noise ratio, and thus improve a detection accuracy of the f wave.

First claim

What is claimed is:

1. A single-lead f wave extraction method, comprising:
establishing a two-channel temporal convolutional neural network model, wherein the two-channel temporal convolutional neural network model comprises two coding submodules, two mask estimation submodules, an information fusion module and two decoding submodules;
constructing a training data set of electrocardiography (ECG) signals, training the two-channel temporal convolutional neural network model according to the training data set of ECG signals to obtain a trained model, and saving the trained model; and
extracting a reconstructed time domain signal of an f wave by inputting a measured mixed ECG signal into the trained model;
wherein the two coding submodules are respectively configured to extract a coding feature vector of a ventricular QRST complex and a coding feature vector of the f wave from the measured mixed ECG signal; the two mask estimation submodules are respectively configured to extract a potential feature vector of the f wave and a potential feature vector of the ventricular QRST complex according to the coding feature vector of the ventricular QRST complex and the coding feature vector of the f wave; the information fusion module is configured to perform feature fusion on the potential feature vector of the f wave and the potential feature vector of the ventricular QRST complex in a coding space, to estimate a mask feature vector of the ventricular QRST complex and a mask feature vector of the f wave; and the two decoding submodules are respectively configured to reconstruct a time domain signal of the ventricular QRST complex and a time domain signal of the f wave according to a weighted result of the mask feature vector and the coding feature vector of the ventricular QRST complex and a weighted result of the mask feature vector and the coding feature vector of the f wave to obtain a reconstructed time domain signal of the ventricular QRST complex and the reconstructed time domain signal of the f wave;
wherein the two-channel temporal convolutional neural network model is established based on a probability graph model, and the probability graph model is formulated by a probability factorization formula expressed as follows:

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Assignees

• Univ Guangdong Technology

Inventors

• Lu Jun
• Li Lei
• Xie Shengli
• Luo Jie
• Wu Zongze
• Yang Qiyu
• Qiu Sihai
• Yan Binbin
• Chen Dewei

Classifications

• G06F2218/08

  Feature extraction · CPC title

• G06F18/253

  of extracted features · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/045

  Combinations of networks · CPC title