An explainable artificial intelligence framework for trustworthy arrhythmia detection from heart rhythm data

Deep learning has significantly propelled the performance of Electrocardiogram (ECG) arrhythmia classification, yet its clinical adoption remains hindered by challenges in interpretability and deployment on resource-constrained edge devices. To bridge this gap, we propose EXGnet, an innovative and reliable ECG arrhythmia classification network tailored for single-lead signals, specifically designed to balance high accuracy, explainability, and edge compatibility. EXGnet integrates explainable artificial intelligence (XAI) supervision during training via a normalized cross-correlation based loss, directing the model’s attention to clinically relevant ECG regions, similar to a cardiologist’s focus. This supervision is driven by automatically generated ground truth, derived through an innovative heart rate variability-based approach, without the need for manual annotation. To enhance classification accuracy without compromising deployment simplicity, we incorporate quantitative ECG features during training. These enrich the model with multi-domain knowledge but are excluded during inference, keeping the model lightweight for edge deployment. Additionally, we introduce an innovative multiresolution block to efficiently capture both short- and long-term signal features while maintaining computational efficiency. Rigorous evaluation on the Chapman and Ningbo benchmark datasets validates the supremacy of EXGnet, which achieves average five-fold accuracies of 98.762% and 96.932%, and F1-scores of 97.910% and 95.527%, respectively. Comprehensive ablation studies and both quantitative and qualitative interpretability assessment confirm that the XAI guidance is pivotal, demonstrably enhancing the model’s focus and trustworthiness. Overall, EXGnet sets a new benchmark by combining high-performance arrhythmia classification with interpretability, paving the way for more trustworthy and accessible portable ECG based health monitoring systems. • Integrate interpretability into ECG classification through supervised training with attention-based loss. • Automate relevance region identification using heart rate variability without manual annotations. • Incorporate diagnostic features during training to enrich the learning without affecting deployment. • Capture essential heart signal patterns across time with multiresolution signal processing. • Deliver consistent, interpretable classification results validated on clinical benchmark datasets.