融合单导联心电图传统与深度特征的常见心律失常识别方法研究

doi:10.3969/j.issn.0258-8021.2022.01.004

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Abstract Cardiac arrhythmia is one of the most common types of cardiovascular diseases. Long-term monitoring of few-leads ECGs based on portable devices is helpful to improve the detection rate of arrhythmias. But the large amounts of long-range ECG data generated impose a great burden on clinicians, which leads to missed detection and misjudgments. Therefore, this paper developed an automatic identification method for common arrhythmias by combining features of the traditional single-lead ECG with deep network features. The new method first extracted the traditional features in frequency domain, time domain, and morphology of common arrhythmias. Then a residual block deep convolutional neural network and a bidirectional long-short memory network were built to extract the deep network features. These three types of the features were fused in one deep network to classify heart rhythms including normal and arrhythmias. Finally, 6 877 sets of static and 8 528 sets of Holter data provided by the 2018 Chinese Physiological Signals Challenge and the 2017 Global Atrial Fibrillation Challenge were used to verify the method in this paper. With single-lead of static ECG signal, the method achieved an average F1 score of 0.855 for categorizing six arrhythmic rhythms and one normal rhythm, which is better than the existing relevant methods. As for single-lead dynamic ECG, the method achieved an average F1 score of 0.827 for categorizing AF, other arrhythmias, and normal rhythms, which is comparable to two methods tied for first in 2017 Global AF Challenge and superior to other related methods. Thus, this method has a good prospect of application in wearable remote monitoring and the auxiliary diagnosis of common arrhythmias.

Key words： arrhythmia single-lead ECG traditional features depth feature identify

Received: 14 September 2020

PACS:

R318

Corresponding Authors: ^* E-mail: raonn@uestc.edu.cn

About author:: ^#Member, Chinese Society of Biomedical Engineering

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	Li Quanchi
	Huang Xin
	Luo Chengsi
	Huang Huiquan
	Rao Nini

Cite this article:

Li Quanchi,Huang Xin,Luo Chengsi, et al. Research on Intelligent Recognition Method of Common Arrhythmia Combining Traditionaland Deep Features of Single-Lead ECG[J]. Chinese Journal of Biomedical Engineering, 2022, 41(1): 31-40.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.01.004 OR http://cjbme.csbme.org/EN/Y2022/V41/I1/31

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