Computer methods and programs in biomedicine · 2026
Clinically-aligned explainable AI for atrial fibrillation detection: A U-Net inspired multi-lead ECG analysis framework.
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Taleban Amirsajjad, Sparapani Rodney, Zlochiver Sharone, Lu Qiang, Widlansky Michael E, Luo Jake
Аннотация
BACKGROUND AND OBJECTIVE: Deep learning offers high diagnostic accuracy but often lacks interpretability, hindering clinical adoption for Electrocardiogram (ECG) analysis. This study aims to develop a clinically interpretable, U-Net-inspired deep learning framework for Atrial Fibrillation (AFib) detection that elucidates model decision-making through morphological and rhythmic feature extraction.
METHODS: We utilized a U-Net-inspired encoder-decoder architecture to analyze 12-lead ECGs as spatially structured inputs, capturing inter-lead relationships using the PTB-XL database. Model interpretability was assessed using layer-wise Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize hierarchical feature focus. Additionally, we implemented RR interval variability assessment with heart rate stratification and group-specific threshold optimization to account for physiological variations.
RESULTS: On PTB-XL-Improved labels, the model achieved an Area Under the Curve (AUC) of 99.15%, Sensitivity of 97.81%, Specificity of 95.63%, and Precision (PPV) of 95.72%. Performance on the original PTB-XL annotations reached an AUC of 99.78%. Grad-CAM analysis confirmed hierarchical feature extraction, correctly localizing QRS complexes in over 97.5% of cases and P-wave and rhythm irregularities in over 93.1% of cases. Independent RR-interval variability analysis achieved AUCs of 95.5% (slow heart rate), 93.2% (normal), and 91.3% (fast) across three clinically stratified heart rate groups.
CONCLUSIONS: This framework successfully combines high diagnostic performance with transparent, clinically aligned visual explanations. The principal contribution is not accuracy alone, but the demonstration that layer-wise explanations can be aligned with the clinical ECG reading sequence, from QRS localization to P-wave morphology. External validation remains necessary before broader clinical generalization can be claimed.
METHODS: We utilized a U-Net-inspired encoder-decoder architecture to analyze 12-lead ECGs as spatially structured inputs, capturing inter-lead relationships using the PTB-XL database. Model interpretability was assessed using layer-wise Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize hierarchical feature focus. Additionally, we implemented RR interval variability assessment with heart rate stratification and group-specific threshold optimization to account for physiological variations.
RESULTS: On PTB-XL-Improved labels, the model achieved an Area Under the Curve (AUC) of 99.15%, Sensitivity of 97.81%, Specificity of 95.63%, and Precision (PPV) of 95.72%. Performance on the original PTB-XL annotations reached an AUC of 99.78%. Grad-CAM analysis confirmed hierarchical feature extraction, correctly localizing QRS complexes in over 97.5% of cases and P-wave and rhythm irregularities in over 93.1% of cases. Independent RR-interval variability analysis achieved AUCs of 95.5% (slow heart rate), 93.2% (normal), and 91.3% (fast) across three clinically stratified heart rate groups.
CONCLUSIONS: This framework successfully combines high diagnostic performance with transparent, clinically aligned visual explanations. The principal contribution is not accuracy alone, but the demonstration that layer-wise explanations can be aligned with the clinical ECG reading sequence, from QRS localization to P-wave morphology. External validation remains necessary before broader clinical generalization can be claimed.