Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. (1st March 2018)

• Record Type: Journal Article
• Authors: Tan, Jen Hong; Hagiwara, Yuki; Pang, Winnie; Lim, Ivy; Oh, Shu Lih; Adam, Muhammad; Tan, Ru San; Chen, Ming; Acharya, U. Rajendra
• Is Part Of: Computers in biology and medicine. Volume 94(2018)
• Page Start: 19
• Page End: 26
• Publication Date: 2018-03-01
• Subjects: Coronary artery disease -- Convolutional neural network -- Deep learning -- Electrocardiogram signals -- Long short-term memory -- PhysioNet database; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.12.023
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Graphical abstract: Highlights: Classification of normal and CAD ECG signals. Implemented two deep learning approaches. Subject-specific data classification. Obtained accuracy of 99.85% using blindfold method.