Paper Info
Title
Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals.
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.
Year
DOI
Venue
2018
10.1016/j.compbiomed.2017.12.023
Computers in Biology and Medicine
Keywords
Field
DocType
Coronary artery disease,Convolutional neural network,Deep learning,Electrocardiogram signals,Long short-term memory,PhysioNet database
CAD,Pattern recognition,Computer science,Convolutional neural network,Abnormal ECG,Long short term memory,Software,Artificial intelligence,Deep learning,Heart disease
Journal
Volume
ISSN
Citations 
94
0010-4825
29
PageRank 
References 
Authors
0.87
19
9
Name
Order
Citations
PageRank
Jen-Hong Tan174532.04
Yuki Hagiwara264129.34
Winnie Pang3290.87
Ivy Lim4290.87
Shu Lih Oh553625.57
Muhammad Adam640716.51
Ru San Tan720018.12
Ming Chen858185.60
Rajendra Acharya U94666296.34