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A novel interpretable multilevel wavelet decomposition deep network for actual heartbeat classification

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Arrhythmias may lead to sudden cardiac death if not detected and treated in time.A supraventricular premature beat(SPB)and premature ventricular contraction(PVC)are important categories of arrhythmia disease.Recently,deep learning methods have been applied to the PVC/SPB heartbeats detection.However,most researchers have focused on time-domain information of the electrocardiogram and there has been a lack of exploration of the interpretability of the model.In this study,we design an interpretable and accurate PVC/SPB recognition algorithm,called the interpretable multilevel wavelet decomposition deep network(IMWDDN).Wavelet decomposition is introduced into the deep network and the squeeze and excitation(SE)-Residual block is designed for extracting time-domain and frequency-domain features.Additionally,inspired by the idea of residual learning,we construct a novel loss function for the constant updating of the multilevel wavelet decomposition parameters.Finally,the IMWDDN is evaluated on the Third China Physiological Signal Challenge Dataset and the MIT-BIH Arrhythmia database.The comparison results show IMWDDN has better detection performance with 98.51％accuracy and a 93.75％F1-macro on average,and its areas of concern are similar to those of an expert diagnosis to a certain extent.Generally,the IMWDDN has good application value in the clinical screening of PVC/SPB heartbeats.

actual heartbeat classificationelectrocardiograminterpretable deep networkmultilevel discrete wavelet decomposition layerSE-Residual block

JIN YanRui、LI ZhiYuan、TIAN YuanYuan、WEI XiaoYang、LIU ChengLiang

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School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

National Postdoctoral Program for Innovative TalentsChina Postdoctoral Science FoundationShanghai Municipal Science and Technology Major Project

BX202302152023M7322192021SHZDZX0102

2024

10.1007/s11431-023-2639-9

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(6)