A novel explainable ECG classification with spatio-temporal transformers and hybrid loss optimization

Abstract

This paper presents a novel deep learning framework for ECG arrhythmias detection, integrating Spatio-Temporal Adaptive Embedding (STAE) Transformers and Variational Autoencoders (VAEs) to improve classification accuracy and address class imbalance. Traditional ECG classification models struggle to capture long-range temporal dependencies and handle imbalanced datasets, leading to poor sensitivity for rare arrhythmias. The proposed system employs STAE Transformers to model intricate temporal and spatial relationships within ECG signals to overcome these challenges. At the same time, VAEs generate diverse and realistic ECG samples to enhance model generalization, particularly for underrepresented arrhythmias. Additionally, combining Focal Loss and Dice Loss, a Hybrid Loss Function further optimizes performance by focusing on hard-to-classify arrhythmias. The model is evaluated on the MIT-BIH Arrhythmias Database and PTB Diagnostic ECG Database using 5-fold cross-validation, achieving an accuracy of 99.56% and a macro F1-score of 95.40%, outperforming existing state-of-the-art methods, with a 3.5% improvement in sensitivity for rare arrhythmias. To ensure interpretability, SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) are utilized, highlighting the QRS complex and RR intervals as the most critical features and confirming that the model focuses on clinically relevant waveform regions. These results demonstrate the effectiveness of our approach in developing an accurate, interpretable, and robust deep learning system for ECG arrhythmias detection, paving the way for more reliable clinical decision support systems.