Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals

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 abn...

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Vydáno v:Computers in biology and medicine Ročník 94; s. 19 - 26
Hlavní autoři: Tan, Jen Hong, Hagiwara, Yuki, Pang, Winnie, Lim, Ivy, Oh, Shu Lih, Adam, Muhammad, Tan, Ru San, Chen, Ming, Acharya, U. Rajendra
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.03.2018
Elsevier Limited
Témata:
Accuracy
Artificial neural networks
Automation
Cardiology
Cardiovascular disease
Classification
Computer memory
Convolutional neural network
Coronary artery
Coronary artery disease
Coronary Artery Disease - diagnosis
Coronary Artery Disease - physiopathology
Coronary vessels
Decomposition
Deep learning
Diagnosis, Computer-Assisted - methods
Diagnostic software
Diagnostic systems
Echocardiography
EKG
Electrocardiogram signals
Electrocardiography
Female
Heart attacks
Heart diseases
Heart rate
Humans
Internal Medicine
Long short-term memory
Machine learning
Male
Neural networks
Neural Networks (Computer)
Other
PhysioNet database
Short term
Signal Processing, Computer-Assisted
Deep learning
Long short-term memory
Electrocardiogram signals
PhysioNet database
Coronary artery disease
Convolutional neural network
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí: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. [Display omitted] •Classification of normal and CAD ECG signals.•Implemented two deep learning approaches.•Subject-specific data classification.•Obtained accuracy of 99.85% using blindfold method.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2017.12.023