用可解释深度学习精准定位心律失常的异常通路，准确率超95%
Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
- 基于虚拟心脏模型生成大规模合成心电图，训练深度学习模型
- 定位准确率超95%，敏感度94.32%，特异度99.78%
- 融合可解释AI技术，揭示关键导联（如V2）对诊断的重要性
Wolff-Parkinson-White (WPW) 综合征是由旁路（AP）导致的心脏电生理异常，引发快速心室激动并成为房室折返性心动过速的基质。准确定位旁路对导管消融手术至关重要。传统诊断树与近期机器学习方法在解剖定位精度、可解释性及数据集规模方面存在局限。本研究提出一种深度学习（DL）模型，用于在24个心脏区域中定位单发显性旁路，训练数据来自基于个性化虚拟心脏模型生成的大规模生理真实合成心电图（ECG）。同时，集成可解释人工智能（XAI）方法：引导反向传播、Grad-CAM 和 Guided Grad-CAM，实现对深度学习决策过程的可视化，解决临床应用中模型不透明的核心障碍。模型定位准确率超过95%，敏感度为94.32%，特异度达99.78%。XAI输出经已知除极模式验证，并引入新指标识别对旁路定位最具信息量的导联，结果显示导联V2最重要，其次为aVF、V1和aVL。该工作展示了结合心脏数字孪生与可解释深度学习，在非侵入性、高精度、透明化旁路定位中的潜力。
原文摘要 · Abstract (English)
Wolff-Parkinson-White (WPW) syndrome is a cardiac electrophysiology (EP) disorder caused by the presence of an accessory pathway (AP) that bypasses the atrioventricular node, faster ventricular activation rate, and provides a substrate for atrio-ventricular reentrant tachycardia (AVRT). Accurate localization of the AP is critical for planning and guiding catheter ablation procedures. While traditional diagnostic tree (DT) methods and more recent machine learning (ML) approaches have been proposed to predict AP location from surface electrocardiogram (ECG), they are often constrained by limited anatomical localization resolution, poor interpretability, and the use of small clinical datasets. In this study, we present a Deep Learning (DL) model for the localization of single manifest APs across 24 cardiac regions, trained on a large, physiologically realistic database of synthetic ECGs generated using a personalized virtual heart model. We also integrate eXplainable Artificial Intelligence (XAI) methods, Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, into the pipeline. This enables interpretation of DL decision-making and addresses one of the main barriers to clinical adoption: lack of transparency in ML predictions. Our model achieves localization accuracy above 95%, with a sensitivity of 94.32% and specificity of 99.78%. XAI outputs are physiologically validated against known depolarization patterns, and a novel index is introduced to identify the most informative ECG leads for AP localization. Results highlight lead V2 as the most critical, followed by aVF, V1, and aVL. This work demonstrates the potential of combining cardiac digital twins with explainable DL to enable accurate, transparent, and non-invasive AP localization.
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