XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias

Arrhythmias stand out for having irregular cardiac rhythms, and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes. Despite the progress in this field, existing research efforts have encountered limitations, necessitating innovative approaches to address diagnostic challenges effectively. The primary objective of this research is to propose an innovative classification methodology for distinguishing five distinct arrhythmia classes: Atrial Premature Beat(A), normal (N), Ventricular Premature Beat(V), Right Bundle Branch Block(R), and Left Bundle Branch Block(L). This approach outperforms existing methods while ensuring high levels of accuracy of 99.16%, specificity of 99.79%, recall of 99.2%, precision of 99.2%, F1-measure of 99.16%, and AUC of 99.92%. The proposed methodology entails the construction of a hybrid model incorporating an attention mechanism, leveraging ECG data from an open-source repository. Additionally, we have incorporated an explainability feature into the model, allowing for the interpretation and explanation of its predictions. This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics. Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance. Findings from this study underscore the superiority of the proposed classification model over existing methodologies. Quantitative analysis demonstrates substantial enhancements in accuracy, specificity, recall, precision, F1-measure, and AUC, providing compelling evidence of the model's efficacy in accurately identifying arrhythmias across diverse classes. This research advances medical diagnostics by integrating advanced machine-learning techniques for improving arrhythmia detection.