Han Chuang, Que Wenge, Wang Zhizhong, Wang Songwei, Li Yanting, Shi Li
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, P. R. China.
Department of Automation, Tsinghua university, Beijing 100000, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):1019-1026. doi: 10.7507/1001-5515.202212010.
Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.
心肌梗死（MI）具有死亡率高、突发性强和隐匿性等特点。临床实践中存在诊断延迟、误诊和漏诊等问题。心电图（ECG）检查是诊断MI最简单、最快的方法。基于ECG的MI智能辅助诊断研究具有重要意义。在MI的病理生理机制和ECG特征变化的基础上，总结了ECG的特征点提取与形态识别以及基于机器学习和深度学习的MI智能辅助诊断方法。比较了不同方法的模型、数据集、ECG数量、导联数量、输入模式、评估方法及效果。最后指出了未来的研究方向和发展趋势，包括MI的数据增强、ECG的特征点和动态特征提取、模型的泛化能力和临床可解释性等，有望为MI智能辅助诊断相关领域的研究人员提供参考。
