Explainable AI model for ECG signal assessment - Low, Stefanie Jing Ting (Nanyang Technological University, 2023)
The use of artificial intelligence (AI) systems can automate the onerous task of manually interpreting ECG parameters to detect cardiac diseases. However, to instill confidence in such systems, it is crucial to ensure performance, transparency and interpretability in these systems. This report presents a novel hybrid approach that leverages on the combination of Residual Network (ResNet) and Discrete Wavelet Transformation (DWT) to achieve accurate predictions of cardiac diseases based on ECG signals. The proposed methodology is evaluated on the PTX-XL dataset, the largest publicly accessible 12-lead ECG dataset. The hybrid model, which incorporated features extracted from DWT, achieved higher true positive rates for cardiac diseases compared to the end-to-end ResNet. This is especially crucial as misidentifying a positive patient as negative could delay the treatment process and have high costs. Our findings demonstrate the efficacy of the hybrid approach in enhancing performance, especially in accurately predicting ECG samples of patients with cardiac diseases. This can ultimately have significant clinical implications in terms of early detection and treatment of these conditions.