IEEE Rev Biomed Eng. 2017;10:264-298. doi: 10.1109/RBME.2017.2757953. Epub 2017 Oct 16.
There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors-information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.
越来越多的研究集中在使用计算机算法自动检测缺血和心肌梗死 (MI)。在临床环境中，使用心电图 (ECG) 记录以及包括患者症状、病史和危险因素在内的医学背景信息来诊断缺血和 MI，这些信息通常存储在电子健康记录中。检查 ECG 信号以识别形态变化，如 ST 段偏移和 T 波变化。一些提出的方法自动计算相似的特征，而另一些方法则使用非传统特征，如小波系数。本综述提供了该领域已提出方法的概述，重点介绍了它们的历史发展、用于评估其性能的公开数据集以及用于 ECG 和 EHR 分析的算法细节。还介绍了用于评估所提出方法性能的验证策略。最后，本文为未来研究提供了建议，以解决当前存在方法的缺点，并考虑实际情况，使提出的技术解决方案能够在临床实践中应用。
