Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
National Heart Centre Singapore, Singapore; Duke-National University of Singapore Medical School, Singapore.
Comput Biol Med. 2018 Mar 1;94:19-26. doi: 10.1016/j.compbiomed.2017.12.023. Epub 2018 Jan 2.
Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.
冠心病（CAD）是全球最常见的心脏病病因。这是因为在疾病进展到晚期之前，其在初始阶段没有表现出任何症状。心电图（ECG）是一种广泛应用的诊断 CAD 的工具，可捕捉心脏的异常活动。然而，它的诊断灵敏度却不足。其中一个原因是，由于 ECG 信号的幅度非常低，因此很难对其进行视觉解释。因此，临床医生识别异常 ECG 形态可能容易出错。因此，开发一种可以自动、客观地解释 ECG 信号的软件非常重要。本文提出了一种使用长短时记忆（LSTM）网络和卷积神经网络（CNN）的方法，以准确地自动诊断 CAD 的 ECG 信号。我们提出的深度学习模型能够以 99.85%的诊断准确率检测 CAD 的 ECG 信号，在进行盲测时也能达到这一效果。该模型已准备好进行适当的大型数据库测试，之后即可用于临床。
