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|Title:||Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals||Authors:||Jahmunah, V.
Ng, Eddie Yin Kwee
San, Tan Ru
Acharya, U. Rajendra
|Keywords:||Engineering::Mechanical engineering||Issue Date:||2021||Source:||Jahmunah, V., Ng, E. Y. K., San, T. R. & Acharya, U. R. (2021). Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Computers in Biology and Medicine, 134, 104457-. https://dx.doi.org/10.1016/j.compbiomed.2021.104457||Journal:||Computers in Biology and Medicine||Abstract:||Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals.||URI:||https://hdl.handle.net/10356/156998||ISSN:||0010-4825||DOI:||10.1016/j.compbiomed.2021.104457||Schools:||School of Mechanical and Aerospace Engineering||Rights:||© 2021 Elsevier Ltd. All rights reserved. This paper was published in Computers in Biology and Medicine and is made available with permission of Elsevier Ltd.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Journal Articles|
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