用深度学习直接从心内电位预测体表心电图，无需手动设定传导参数。
Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor
- 训练时仅需74例数据，模型直接学习心内电位到体表心电图的映射。
- 在测试集上达到R²=0.949±0.037，逼近物理模型精度。
- 适合临床心律失常无创评估，尤其适用于房颤患者建模。
精确的前向建模对非侵入性心电生理学至关重要，尤其在心房颤动中，电激活高度紊乱。传统基于物理的前向模型需要显式指定心肌细胞内传导张量，而该参数在临床实践中无法直接测量，易引入结构建模误差。本概念验证研究提出一种深度学习方法，可直接从左心房心肌细胞内电位映射至远场体表心电图（ECG），推理阶段无需输入心内传导张量。尽管仅在74名受试者上训练，模型仍实现R² = 0.949 ± 0.037，表明该方法有望降低结构不确定性，提升非侵入性房颤评估能力。
原文摘要 · Abstract (English)
Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is highly disorganised. Conventional physics-based forward models require explicit specification of intracellular conductivity tensors, which are not directly measurable in clinical practice and introduce structural modelling errors. This proof-of-concept study presents a deep learning approach that learns a direct mapping from left atrial intracellular electrical potentials to far-field ECGs without requiring explicit intracellular conductivity inputs at inference time. Despite training only on 74 subjects, the model achieved an R2 of 0.949 \pm 0.037, highlighting potential to reduce structural uncertainty and improve non-invasive AF assessment.
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