Interpretable Hybrid Multichannel Deep Learning Model for Heart Disease Classification Using 12-Lead ECG Signal

An electrocardiogram (ECG) is a non-invasive and cost-effective method for diagnosing heart disease. However, physicians often face challenges in interpreting ECG. As a result, deep learning (DL) models have been proposed to assist with interpretation. However, the development of a robust, interpretable model that performs well across diverse ECG datasets remains a focus. Hence, this study presents a robust interpretable DL-based system. The model utilizes a multi-channel hybrid architecture. It integrates 12 blocks of one-dimensional convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) networks, followed by an attention mechanism and a two-dimensional CNN, and finally fully connected (FC) layers for classification. The model separately trained and tested on three 12-lead ECG datasets: PTB-XL, CODE-15%, and the reduced seven and merged four classes of Chapman Arrhythmia datasets, achieved average test accuracy rates of 89.84%, 97.82%, 98.55%, and 98.80%, respectively.The result indicates the model's effectiveness across different ECG datasets. Besides, the classification output is analyzed using two post-hoc model interpretability techniques: Gradient-weighted Class Activation Mapping Plus Plus (Grad-CAM++) and SHapley Additive exPlanations (SHAP). These techniques are applied to the trained model to visualize influential segments of the ECG signal, both at the instance level for specific samples and at the test set level to assess the contributions of individual ECG leads among the 12 leads that influence the model predictions. The model's performance and its output interpretation techniques makes it a practicable tool in ECG-based heart disease diagnosis.