Deep learning approaches for ECG-based detection and diagnosis of coronary artery disease

Coronary artery disease (CAD) is among the most prevalent and life-threatening cardiovascular conditions worldwide. Early detection is essential for improving patient outcomes and improving the efficiency of healthcare systems. Electrocardiography (ECG) is widely used for assessing cardiac function, yet manual interpretation of ECG signals can be inconsistent and prone to error. Developing reliable automated methods is therefore of great importance for enabling earlier and more consistent CAD detection. Although recent advances in deep learning have achieved strong performance in ECG analysis, many existing methods remain limited in practice. Current state-of-the-art models are often computationally complex and thus unsuitable for deployment on resource-constrained platforms. To address the limited attention given to CAD in current ECG research, various deep learning models were developed in this thesis. First, a one-dimensional convolutional neural network (1D-CNN) was proposed to detect CAD directly from raw ECG signals without manual feature extraction. The model achieved 97.3% accuracy and demonstrated strong generalisability when using 250-point ECG segments. Then, a feature engineering approach was applied to select high-quality signal segments using sample entropy and normalisation techniques, further improving both accuracy and robustness. Next, a lightweight neural network architecture (CADNet) was developed, outperforming existing lightweight models by offering lower complexity and smaller size without compromising accuracy. The fourth study focused on 12-lead ECG, introducing a depthwise, squeeze-and-excitation-based model that captured both lead-specific and inter-lead patterns, and achieved efficient deployment on an STM32 microcontroller. Finally, an attention-driven model was proposed for the detection of multiple cardiovascular diseases from a single ECG recording, demonstrating high diagnostic capability. Our qualitative evaluations in this study demonstrate that lightweight deep learning models can provide reliable ECG-based CAD diagnosis while remaining suitable for real-time deployment. The proposed approaches are robust to ECG variability and show consistent performance across different experimental and diagnostic scenarios, supporting their practical use in resource-constrained environments. Overall, the results highlight the relevance of lightweight deep learning architectures in enabling ECG-based diagnostic methods to be used as potential pre-screening tools.