用可解释AI提升心电图心律失常检测的可信度，医生更喜欢可视化波形热点。
Explainable AI (XAI) for Arrhythmia detection from electrocardiograms
- 采用SHAP等可解释技术分析心电图模型决策依据。
- 模型在MIT-BIH数据集上准确率达98.3%，但跨数据集性能下降。
- 梯度和DeepLIFT方法生成的热力图更符合临床判断逻辑。
深度学习在心电图（ECG）心律失常检测中已实现高精度，但可解释性不足限制了临床应用。本研究针对时间序列ECG分析，探索适用于该场景的可解释AI（XAI）技术。基于MIT-BIH心律失常数据集，使用卷积神经网络进行分类，并通过Pan-Tompkins算法进行R波峰值分割。为扩充数据量并缓解类别不平衡，引入了12导联心电图数据集。通过用户调研发现，医疗从业者更偏好基于显著性图的解释，因其与现有心电图判读流程契合度更高。对比了四种SHAP方法：置换重要性、KernelSHAP、梯度法及DeepLIFT。模型在MIT-BIH验证集上达到98.3%准确率，但在融合数据集上性能下降，凸显数据差异挑战。置换重要性和KernelSHAP输出杂乱，而梯度法与DeepLIFT能突出与临床推理一致的波形区域，但样本间存在差异。结果强调需为心电图分析定制化设计可解释技术，且显著性映射更具临床适用性。
原文摘要 · Abstract (English)
Advancements in deep learning have enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption. This study investigates the application of Explainable AI (XAI) techniques specifically adapted for time-series ECG analysis. Using the MIT-BIH arrhythmia dataset, a convolutional neural network-based model was developed for arrhythmia classification, with R-peak-based segmentation via the Pan-Tompkins algorithm. To increase the dataset size and to reduce class imbalance, an additional 12-lead ECG dataset was incorporated. A user needs assessment was carried out to identify what kind of explanation would be preferred by medical professionals. Medical professionals indicated a preference for saliency map-based explanations over counterfactual visualisations, citing clearer correspondence with ECG interpretation workflows. Four SHapley Additive exPlanations (SHAP)-based approaches: permutation importance, KernelSHAP, gradient-based methods, and Deep Learning Important FeaTures (DeepLIFT), were implemented and compared. The model achieved 98.3% validation accuracy on MIT-BIH but showed performance degradation on the combined dataset, underscoring dataset variability challenges. Permutation importance and KernelSHAP produced cluttered visual outputs, while gradient-based and DeepLIFT methods highlighted waveform regions consistent with clinical reasoning, but with variability across samples. Findings emphasize the need for domain-specific XAI adaptations in ECG analysis and highlight saliency mapping as a more clinically intuitive approach
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