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

• URL: http://arxiv.org/abs/2506.12404v2
• Date: Wed, 23 Jul 2025 06:58:51 GMT
• Title: EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification
• Authors: Tushar Talukder Showrav, Soyabul Islam Lincoln, Md. Kamrul Hasan,
• Abstract summary: We propose EXGnet, a novel ECG arrhythmia classification network tailored for single-lead signals.<n>XAI supervision during training directs the model's attention to clinically relevant ECG regions.<n>We introduce an innovative multiresolution block to efficiently capture both short and long-term signal features.
• Score: 1.5162243843944596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has significantly propelled the performance of 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, a novel and reliable ECG arrhythmia classification network tailored for single-lead signals, specifically designed to balance high accuracy, explainability, and edge compatibility. EXGnet integrates 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.

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