Electrocardiogram classification method of deep residual neural network based on attention mechanism

A neural network and classification method technology, applied in the field of electrocardiogram classification, can solve problems such as limited generalization ability of ordinary convolutional neural network, difficulty in automatic classification of arrhythmia, and deviation of disease prediction ability, so as to improve generalization ability and contribute to The effect of model convergence and improving learning efficiency

Active Publication Date: 2019-09-20
SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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Problems solved by technology

The ECG of the same type of arrhythmia in different stages of the same patient is likely to have obvious changes, and the difference in the ECG of the same type of arrhythmia in differ

Method used

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Embodiment Construction

[0029] The present invention will be further described below.

[0030] A kind of electrocardiogram classification method based on the depth residual neural network of attention mechanism, comprises the following steps:

[0031] a) The computer obtains the ECG data from the MIT-BIH arrhythmia database, and according to the lead records in the ECG data, selects the upper signal as the signal of lead II and the lower signal as the signal of the chest lead I as the experimental data;

[0032] b) Use the double-scale wavelet transform method to denoise the experimental data, locate the QRS wave group in the experimental data, obtain the positions of the P wave and T wave in the ECG signal through the QRS wave group, and obtain a heart beat data, through The edge filling random clipping algorithm obtains the expanded data set of heart beat data;

[0033] c) Add Gaussian white noise to the expanded data set to obtain the data set set, through the formula X={(x 11 ,x 12 ,...x 1N),...

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Abstract

The invention relates to an electrocardiogram classification method of deep residual neural network based on an attention mechanism. According to the invention, automatic classification of arrhythmia is realized by multi-lead ECG data and a deep residual neural network model based on the attention mechanism, firstly, the multi-lead ECG contains more ECG information than the single-lead ECG, and secondly, the depth residual network can learn characteristics of the model in a higher mode, which helps the model to converge, and finally, an attention mechanism module automatically enhances a feature map, improves the generalization ability of the model, and improves the learning efficiency of the network and the accuracy of ECG recognition.

Description

technical field [0001] The invention relates to the technical field of electrocardiogram classification, in particular to an electrocardiogram classification method based on a deep residual neural network of an attention mechanism. Background technique [0002] Electrocardiogram examination has become a common test item in hospitals. Electrocardiogram is the most basic indicator for doctors to judge the heart condition of patients. The electrocardiogram signal is a non-stationary periodic biological signal caused by the electrical activity of the heart, which contains a large amount of complex heart activity information, and only professionally trained doctors can accurately interpret it. Because of the complex structure of the heart and the regularity of heart activity, there are many types of arrhythmias. The electrocardiogram of the same type of arrhythmia in different stages of the same patient is likely to have obvious changes, and the difference in the electrocardiogr...

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Application Information

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IPC IPC(8): A61B5/0402
CPCA61B5/7203A61B5/726A61B5/7267A61B5/7275A61B5/318
Inventor 王英龙成曦朱清舒明雷周书旺刘瑞霞
Owner SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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