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Published on: July 29, 2011
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Multi-Level Explainable AI for ECG-Based Atrial Fibrillation Detection: Exploring LIME, SHAP, and Grad-CAM for
Jake Luo1, Amirsajjad Taleban2, Patrick Noffke3
1Health Informatics Program & Computer Science Department, University of Wisconsin-Milwaukee, Milwaukee, USA.
Proceedings of the 2025 9Th International Conference on Medical and Health Informatics. International Conference on Medical and Health Informatics (9Th : 2025 : Kyoto, Japan)
|April 29, 2026
Summary
This study introduces an explainable AI framework for detecting atrial fibrillation (AFib) from ECGs, enhancing clinical trust. The multi-level XAI approach provides clinically relevant interpretations, improving AI adoption in cardiology.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Automated electrocardiogram (ECG) analysis using deep learning shows potential for atrial fibrillation (AFib) detection.
- The "black box" nature of deep learning models hinders their clinical integration and interpretability.
- Explainable AI (XAI) methods are crucial for bridging the gap between AI performance and clinical trust.
Purpose of the Study:
- To develop and evaluate a multi-level XAI framework for AFib detection using ECG data.
- To enhance the clinical interpretability of deep learning models for AFib diagnosis.
- To align AI-driven insights with established clinical diagnostic patterns for AFib.
Main Methods:
- A ResNet-based deep learning model was developed for AFib detection using the PhysioNet AFib Dataset (5,830 ECG samples).
- A multi-level XAI framework combining LIME, SHAP, and Grad-CAM was implemented.
- A novel RR-interval aggregation method was introduced to improve the clinical relevance of XAI outputs.
Main Results:
- The ResNet model achieved high accuracy: 91.3% precision and 88.7% recall for AFib detection.
- Normal rhythm classification achieved superior performance with 98.4% precision and 98.8% recall.
- Grad-CAM successfully identified key morphological features: P-wave absence, pronounced T-waves, and irregular RR-intervals, aligning with clinical AFib indicators.
Conclusions:
- The proposed multi-level XAI framework provides clinically meaningful interpretations for AFib detection, enhancing model transparency.
- Integrating XAI methods, particularly with RR-interval aggregation, improves the alignment of AI predictions with clinical diagnostic criteria.
- This approach facilitates the adoption of AI in clinical cardiology by offering interpretable and accurate AFib detection.
