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.
李全池, 黄鑫, 罗成思, 黄惠泉, 饶妮妮. 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[J]. 中国生物医学工程学报, 2022, 41(1): 31-40.
Li Quanchi, Huang Xin, Luo Chengsi, Huang Huiquan, Rao Nini. Research on Intelligent Recognition Method of Common Arrhythmia Combining Traditionaland Deep Features of Single-Lead ECG. Chinese Journal of Biomedical Engineering, 2022, 41(1): 31-40.
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