用于从心电图检测心律失常的可解释人工智能（XAI）
Explainable AI (XAI) for Arrhythmia detection from electrocardiograms
AI总结：
针对深度学习心律失常检测模型临床应用中可解释性不足的问题，本研究适配XAI技术于ECG时间序列分析，结合数据集扩展与用户需求评估，比较SHAP方法，为临床可解释心律失常检测提供方向。
摘要：
深度学习的进步使从心电图（ECG）信号中检测心律失常的准确率大幅提升，但有限的可解释性仍是临床应用的障碍。本研究探讨了专为时间序列心电图分析适配的可解释人工智能（XAI）技术的应用。基于MIT-BIH心律失常数据集，开发了一种基于卷积神经网络的心律失常分类模型，通过Pan-Tompkins算法进行基于R峰的分割。为扩大数据集规模并减少类别不平衡，纳入了额外的12导联心电图数据集。开展了用户需求评估，以确定医疗专业人员偏好何种解释类型。医疗专业人员表示，与反事实可视化相比，更偏好基于显著性图的解释，称其与心电图解读工作流程的对应关系更清晰。实施并比较了四种基于SHapley加法解释（SHAP）的方法：排列重要性、KernelSHAP、基于梯度的方法和深度学习重要特征（DeepLIFT）。该模型在MIT-BIH数据集上达到98.3%的验证准确率，但在合并数据集上表现下降，突显了数据集变异性的挑战。排列重要性和KernelSHAP产生了杂乱的视觉输出，而基于梯度的方法和DeepLIFT突出了与临床推理一致的波形区域，但样本间存在变异性。研究结果强调了心电图分析中特定领域XAI适配的必要性，并指出显著性映射是更具临床直观性的方法。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
