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

• Published in: Computers in biology and medicine Vol. 94; pp. 19 - 26
• Main Authors: Tan, Jen Hong, Hagiwara, Yuki, Pang, Winnie, Lim, Ivy, Oh, Shu Lih, Adam, Muhammad, Tan, Ru San, Chen, Ming, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2018; Elsevier Limited
• Subjects: 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

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...