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（心梗）具有致死率高、突发性和隐蔽性强等特点，临床上存在诊断不及时、误诊和漏诊等问题。心电图检查是诊断心梗最简单和快速的方法，开展基于心电图的心梗智能辅助研究具有重要意义。本文首先介绍心梗的病理生理机制及其心电图的特征性改变；在此基础上，分别综述了心电图特征点提取与形态识别方法、基于机器学习和深度学习的心梗辅助诊断方法，并着重对比分析了不同方法所用模型、数据集和数据量、导联数和输入模式、模型评估方式和效果，最后从心梗数据增强、心电图特征点提取、动态特征提取、模型泛化性与临床可解释性等方面归纳目前存在的问题并对发展趋势进行展望，可望为心梗智能辅助诊断等相关领域的科研工作者提供参考。.
Keywords: Electrocardiogram; Feature extraction; Intelligent auxiliary diagnosis; Machine learning; Myocardial infarction.