基于深度学习的心电图自动诊断在多系统疾病中的研究进展

doi:10.20047/j.issn1673-7210.2022.30.07

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Abstract Electrocardiogram is a non-invasive method for detection, which can reflect the cardiac electrophysiology. Clinical findings show that many diseases can be complicated with cardiovascular events or change the normal working state of the heart. These changes can be reflected in small changes in electrocardiogram, which are not easy to be recognized by manual or traditional machine algorithms. In recent years, as the core of artificial intelligence development, deep learning can find nonlinear and subtle changes in electrocardiogram, and its application in this field has become an inevitable trend. At present, screening, monitoring, classification, diagnosis, and prediction of a variety of diseases can be realized through deep learning models. This paper reviews the research progress of electrocardiogram automatic diagnosis based on deep learning in multiple system diseases, and analyzes its future development trend.

Key words： Electrocardiogram Cardiovascular disease Multiple system diseases Artificial intelligence Convolutional neural network

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	ZHANG Xiling WANG Xinkang

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ZHANG Xiling WANG Xinkang. Research progress of electrocardiogram automatic diagnosis based on deep learning in multiple system diseases[J]. 中国医药导报, 2022, 19(30): 36-40.

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https://www.yiyaodaobao.com.cn/EN/10.20047/j.issn1673-7210.2022.30.07 OR https://www.yiyaodaobao.com.cn/EN/Y2022/V19/I30/36

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