Atrial fibrillation recognition method based on group convolution residual error network and long/short-term memory network

Abstract

The invention discloses an atrial fibrillation recognition method based on a group convolution residual error network and a long / short-term memory network and relates to the field of machine recognition of atrial fibrillation. According to the method, characteristics of electrocardiosignals of three different frequency bands are respectively extracted through three channels on a network structurebased on a group convolution residual error network and a long / short-term memory network, characteristic analysis on a time domain is further implemented through LSTM (long short term memory), and finally electrocardiosignal segments are classified into a normal segment, an atrial fibrillation segment, a segment with large noise and a segment of other rhythms. By adopting the network model, atrialfibrillation recognition accuracy can be improved in a situation of noise interference, the analysis time can be shortened, and the instantaneity of an algorithm can be improved; and on the basis ofthe group convolution residual error network, a classification accurate rate can be increased on premise that parameter complexity is not improved, benefits of a topology structure of group convolution blocks in a residual error module are taken into play, and meanwhile, data amounts of hyper-parameters can be also increased.

Description

technical field [0001] The invention relates to the technical field of atrial fibrillation machine identification, in particular to an atrial fibrillation identification method based on a group convolution residual network and a long-short-term memory network. Background technique [0002] Atrial fibrillation (AF) is the most common cardiac arrhythmia, with a prevalence of around 0.4-1% in the general population, increasing to 8% in people over the age of 80. The occurrence of atrial fibrillation symptoms is also closely related to diseases such as coronary heart disease, hypertension and heart failure. Atrial fibrillation itself does not directly threaten the life and health of patients. But without prompt treatment, atrial fibrillation can cause serious complications, such as heart failure and stroke. Heart failure can seriously affect the quality of life of patients, while stroke is listed by the World Health Organization as the second leading cause of death in the worl...

AI Technical Summary

Problems solved by technology

In the process of monitoring and detection, a relatively clean ECG signal is often required, and it is easy to misreport the segment mixed with noise as an atrial fibrillation signal, and the recognition accuracy is not high

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