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Method and system for identifying atrial fibrillation electrocardiosignals through weighted multi-scale limited penetrable visibility graph

A technology of ECG signal and identification method, which is applied in the field of weighted multi-scale finite traversal visual image identification of atrial fibrillation ECG signal, can solve the problems of irregular RR intervals and difficulty in obtaining clean data, and achieve improved accuracy and results Enhanced effect

Active Publication Date: 2021-08-17
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because many non-AF rhythms resemble AF rhythms with irregular RR intervals, it is difficult to obtain clean data

Method used

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  • Method and system for identifying atrial fibrillation electrocardiosignals through weighted multi-scale limited penetrable visibility graph
  • Method and system for identifying atrial fibrillation electrocardiosignals through weighted multi-scale limited penetrable visibility graph
  • Method and system for identifying atrial fibrillation electrocardiosignals through weighted multi-scale limited penetrable visibility graph

Examples

Experimental program
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Effect test

Embodiment 1

[0041] Such as figure 1 As shown, the method for identifying atrial fibrillation ECG signals in the weighted multi-scale limited traversing visual image of this embodiment specifically includes:

[0042] Step S101: Obtain an ECG signal of a set length.

[0043] For example, the electrocardiographic signal of this embodiment is taken from the 2017 PhysioNet / Cinc Challenge, and the electrocardiographic signal of each patient is recorded. According to the waveform and rhythm of the ECG signal, identify the ECG of patients with atrial fibrillation. The ECG categories include: normal rhythm, atrial fibrillation rhythm, other rhythms and noise records.

[0044] This example contains 8528 single-lead ECG records ranging in duration from 9 seconds to just over 60 seconds. The ECG was sampled at 300Hz and band-pass filtered. Each piece of data includes a signal record and the category corresponding to the signal, and the category is shown in Table 1.

[0045] Table 1 ECG waveform s...

Embodiment 2

[0110] Such as Figure 5 As shown, the present embodiment provides a system for identifying atrial fibrillation ECG signals with weighted multi-scale limited traversal visual images, which specifically includes the following modules:

[0111] An electrocardiographic signal acquisition module, which is used to acquire an electrocardiographic signal of a set length;

[0112] An original feature extraction module, which is used to extract the original features of the ECG signal to obtain a corresponding original feature vector;

[0113] A network feature extraction module, which is used to convert the electrocardiographic signal into a network form, extract corresponding network features, and obtain corresponding network feature vectors;

[0114] The electrocardiographic signal identification module is used to input the original feature vector and the network feature vector into the machine learning model to obtain the category identification result of the electrocardiographic s...

Embodiment 3

[0117] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in the method for identifying an atrial fibrillation electrocardiogram as described above are implemented.

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Abstract

The invention belongs to the field of atrial fibrillation electrocardiosignal processing, and provides a method and system for identifying atrial fibrillation electrocardiosignals through a weighted multi-scale limited penetrable visibility graph. The method comprises the following steps: acquiring electrocardiosignals with a set length; extracting original features of the electrocardiosignals to obtain corresponding original feature vectors; converting the electrocardiosignals into a network form, and extracting corresponding network features so as to obtain corresponding network feature vectors, wherein the network features comprise newly-added local efficiency entropies; and fusing the original feature vectors and the network feature vectors and then inputting the fused vectors into a machine learning model so as to obtain a category identification result of the electrocardiosignals, wherein the categories of the electrocardiosignals comprise atrial fibrillation electrocardiosignals, normal electrocardiosignals, noise signals and other electrocardiosignals.

Description

technical field [0001] The invention belongs to the field of atrial fibrillation electrocardiographic signal processing, and in particular relates to a method and system for identifying atrial fibrillation electrocardiographic signals with weighted multi-scale finite traverse visual images. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Time series analysis is a central topic in physics and a powerful method for characterizing biological, medical and economic data and understanding their underlying dynamic origins. Physiological signal analysis is an important branch of time series analysis. As a common physiological signal, the ECG signal contains a lot of information about the state of the heart and relevant information corresponding to different physiological states. Atrial fibrillation (AF) is defined by the American College of Card...

Claims

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Application Information

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IPC IPC(8): A61B5/318A61B5/361A61B5/00G06K9/00G06N20/00
CPCA61B5/7235A61B5/7267G06N20/00G06F2218/08G06F2218/12
Inventor 王红李威韩书庄鲁贺张慧王正军杨杰杨雪滑美芳李刚梁成王吉华
Owner SHANDONG NORMAL UNIV
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