EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification

Background: Deep learning has significantly advanced ECG arrhythmia classification, enabling high accuracy in detecting various cardiac conditions. The use of single-lead ECG systems is crucial for portable devices, as they offer convenience and accessibility for continuous monitoring in diverse settings. However, the interpretability and reliability of deep learning models in clinical applications poses challenges due to their black-box nature. Methods: To address these challenges, we propose EXGnet, a single-lead, trustworthy ECG arrhythmia classification network that integrates multiresolution feature extraction with Explainable Artificial Intelligence (XAI) guidance and train only quantitative features. Results: Trained on two public datasets, including Chapman and Ningbo, EXGnet demonstrates superior performance through key metrics such as Accuracy, F1-score, Sensitivity, and Specificity. The proposed method achieved average five fold accuracy of 98.762%, and 96.932% and average F1-score of 97.910%, and 95.527% on the Chapman and Ningbo datasets, respectively. Conclusions: By employing XAI techniques, specifically Grad-CAM, the model provides visual insights into the relevant ECG segments it analyzes, thereby enhancing clinician trust in its predictions. While quantitative features further improve classification performance, they are not required during testing, making the model suitable for real-world applications. Overall, EXGnet not only achieves better classification accuracy but also addresses the critical need for interpretability in deep learning, facilitating broader adoption in portable ECG monitoring.