Phoemsuk, Atitaya and Abolghasemi, Vahid (2025) CADNet: A lightweight Neural Network for Coronary Artery Disease Classification Using Electrocardiogram Signals. IEEE Journal of Biomedical and Health Informatics, PP. pp. 1-13. DOI https://doi.org/10.1109/jbhi.2025.3582872 (In Press)
Phoemsuk, Atitaya and Abolghasemi, Vahid (2025) CADNet: A lightweight Neural Network for Coronary Artery Disease Classification Using Electrocardiogram Signals. IEEE Journal of Biomedical and Health Informatics, PP. pp. 1-13. DOI https://doi.org/10.1109/jbhi.2025.3582872 (In Press)
Phoemsuk, Atitaya and Abolghasemi, Vahid (2025) CADNet: A lightweight Neural Network for Coronary Artery Disease Classification Using Electrocardiogram Signals. IEEE Journal of Biomedical and Health Informatics, PP. pp. 1-13. DOI https://doi.org/10.1109/jbhi.2025.3582872 (In Press)
Abstract
Coronary Artery Disease (CAD) is characterised by a diminished capacity of the coronary arteries to supply sufficient blood, oxygen and nutrients to the heart. It primarily develops due to the presence of fat deposits and arterial plaques, and it is a leading cause of global mortality. Given the limited accessibility, high cost, and inconvenience of invasive diagnostic tools, we propose a lightweight one-dimensional convolutional neural network for CAD classification using non-invasive electrocardiography (ECG) signals. The proposed model, CADNet, consists of two key components: Feature Encoding and Compact Pooling. The feature encoding block extracts key temporal characteristics from ECG data using a convolutional layer, while the compact pooling block reduces temporal resolution, preserving essential ECG features for CAD diagnosis. CADNet comes with a novel data purification process to optimise computational efficiency and maintain high diagnostic accuracy. This approach aids convergence, significantly reduces the model parameters, and improves the model’s ability to detect CAD patterns. Our extensive experiments with four diverse datasets show that CADNet achieves an average 99.3% accuracy, with 2,586 trainable parameters, surpassing state-of-the-art models performance.
Item Type: | Article |
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Uncontrolled Keywords: | Cardiovascular diseases, Coronary Artery Disease, Convolutional Neural Network, Electrocardiogram |
Subjects: | Z Bibliography. Library Science. Information Resources > ZR Rights Retention |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 24 Jun 2025 16:02 |
Last Modified: | 04 Jul 2025 23:20 |
URI: | http://repository.essex.ac.uk/id/eprint/41153 |