Explainable hybrid deep learning framework with Grad-CAM for heartbeat-level arrhythmia classification

Abstract

Cardiac arrhythmia, a common sign of cardiovascular disease, is a leading cause of global mortality and morbidity. Timely and accurate detection of electrocardiogram (ECG) cardiac arrhythmia is essential for effective clinical intervention. Here, we present an explainable hybrid deep learning framework for automated ECG cardiac arrhythmia classification. The proposed model integrates one-dimensional convolutional neural network (1D-CNN) for local morphological features, a gated recurrent unit (GRU) to extract temporal dependencies in sequential ECG signals, and the channel attention technique to emphasize clinically important patterns. In addition, a gradient-weighted class activation mapping (Grad-CAM) module is integrated to enhance interpretability by highlighting complex portions of the ECG signals that influence model decision making. The proposed framework is evaluated on three benchmark datasets: Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) Arrhythmia, St. Petersburg INCART, and the MIT-BIH Supraventricular Arrhythmia Database (SVDB). The model achieves classification accuracy values of 99.69%, 99.73%, and 98.77% along with macro-F1 scores of 95.75%, 97.21%, and 94.58% and specificity values of 99.53%, 99.54%, and 98.63%, respectively. The experimental results demonstrated performance improvement of approximately 2%–5% over recent state-of-the-art techniques in terms of key performance metrics. These findings indicate that the proposed framework effectively integrates local morphological feature extraction with temporal modeling, providing a robust and scalable solution for ECG arrhythmia classification. Moreover, its computational efficiency and its use of single-lead ECG signals make it suitable for real-time deployment in wearable devices, remote monitoring systems, and resource-limited clinical settings.