基于心电图的心肌梗死识别分类研究

心肌梗死（MI）是一种严重的心脏病，症状前的健康检查可以发现早期的MI。心电图（ECG）是一种常用的无创健康检查诊断工具。一些使用ECG预测MI的研究存在基于私人数据集、样本量小、分析方法简单等不足。为了解决这些问题，本研究提出在英国最大的开放采集生物信息资源平台UK Biobank上进行MI的首次基准预测实验，涵盖基于临床特征的机器学习方法和基于ECG信号的深度学习方法。结果显示，基于临床特征的AUC为0.690，深度学习使用原始ECG信号的AUC为0.728，提升近4%。证明深度学习基于原始ECG信号能学习到比临床特征更多的信息。另外，对XGBoost和ResNet方法的结果进行了初步的可解释性分析，发现ST波与MI的关联更密切。

ECG-based identification and classification of myocardial infarction

Abstract: Myocardial infarction (MI) is a kind of severe heart diseases. Pre-symptomatic health examination can detect MI diseases at early stage. Electrocardiogram (ECG) is a common-used non-invasive health examination and diagnostic tool. Some studies that use ECG to predict MI have some shortcomings such as private data sets-based, small sample sizes, simple analysis methods. To solve these issues, the first benchmark prediction experiment of MI on the available collection of biological information resource platform UK Biobank is proposed, which covers machine learning methods based on clinical features and deep learning methods based on ECG signals. The results show that the AUC based on clinical characteristics is 0.690, and the AUC of deep learning using the original ECG signals is 0.728, which is an increase of nearly 4%. It is proved that deep learning based on the original ECG can learn more information than clinical features. In addition, a preliminary interpretability analysis is performed on the results obtained by XGBoost and ResNet methods, and it is found that ST wave is more closely related to MI.