Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals
Article
Jahmunah, V., Ng, E.Y.K., Tan, Tan Ru and Acharya, U. Rajendra. 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. https://doi.org/10.1016/j.compbiomed.2021.104457
| Article Title | Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Jahmunah, V., Ng, E.Y.K., Tan, Tan Ru and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 134 |
| Article Number | 104457 |
| Number of Pages | 11 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2021.104457 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482521002511 |
| 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. |
| Keywords | Cardiovascular disease; Convolutional neural network ; Gabor filter ; Gabor convolutional neural network ; Ten-fold validation ; Deep learning ; Multi-class classification |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | Nanyang Technological University, Singapore |
| National Heart Centre, Singapore | |
| School of Mathematics, Physics and Computing | |
| Ngee Ann Polytechnic, Singapore | |
| Kumamoto University, Japan | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
Permalink -
https://research.usq.edu.au/item/z1vq3/automated-detection-of-coronary-artery-disease-myocardial-infarction-and-congestive-heart-failure-using-gaborcnn-model-with-ecg-signals
Download files
Accepted Version
54
total views276
total downloads0
views this month10
downloads this month