Comparative performance analysis of hybrid deep learning architectures for automated classification of 50 cardiac arrhythmia classes

Arrhythmia is a major cardiac abnormality associated with cardiovascular disease (CVD) and contributes substantially to morbidity and mortality worldwide. Automated analysis of electrocardiogram (ECG) signals is therefore essential for supporting the early detection of cardiac arrhythmia and clinical decision-making. This work demonstrates a comparative analysis of hybrid deep learning architectures, including CNN, CNN+LSTM, CNN+GRU, and CNN+Transformer for automated multi-class ECG arrhythmia classification using single-lead ECG signals. In the proposed architectures, one-dimensional convolutional neural networks (1D-CNNs) are employed to extract local morphological features, while LSTM, GRU, and transformer encoder networks are used to model temporal dependencies in ECG signals. Furthermore, gradient-weighted class activation mapping (Grad-CAM) is incorporated to enhance model interpretability by highlighting the important ECG regions that contribute most to the classification decisions. The developed models are evaluated on two publicly available benchmark datasets: CPSC2018, comprising nine arrhythmia classes, and the Georgia dataset, containing 67 SNOMED-CT diagnostic categories, of which 50 classes with sufficient heartbeat samples are considered for evaluation. On the CPSC2018 dataset, the CNN+GRU model achieved the best overall performance, with test accuracy of 90.31% and macro-averaged accuracy, AUC, precision, sensitivity, and F1-score of 97.85%, 98.89%, 90.26%, 89.92%, and 90.07%, respectively. On the Georgia dataset, the CNN+LSTM model achieved the best overall performance, with test accuracy of 85.27% and macro-averaged accuracy, AUC, sensitivity, and F1-score of 99.41%, 99.11%, 87.15%, and 86.98%, respectively. In addition, comprehensive ablation studies, computational complexity analysis, and comparisons with recent state-of-the-art methods demonstrate the effectiveness of the developed hybrid architectures. These results indicate that combining convolutional feature extraction with recurrent or transformer-based temporal modeling enables reliable multi-class ECG arrhythmia classification across diverse clinical datasets.