用深度学习快速替代心脏电活动计算，精度接近物理模型。
Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models
- 采用注意力机制的时序序列模型，从心电传播图预测体表心电图。
- 在多种组织条件下实现均方相关系数0.99的高精度预测。
- 适合需要实时计算的临床与数字孪生应用场景。
心脏电生理正问题（即从心肌电活动计算体表电位）传统上依赖双向或单向域方程等物理模型。尽管精确，但这些方法计算成本高昂，限制了其在实时和大规模临床应用中的使用。本文提出一种深度学习框架作为前向求解器的高效替代方案。该模型采用时间依赖的注意力序列到序列架构，从心肌电压传播图预测心电图信号。引入结合Huber损失与频谱熵项的混合损失函数，以保持时域和频域的保真度。基于包含健康、纤维化及间隙连接重构条件的二维组织模拟数据，模型实现了高精度（均值$R^2 = 0.99 \pm 0.01$）。消融实验验证了卷积编码器、时序感知注意力和频谱熵损失的有效性。结果表明，深度学习可作为可扩展、低成本的物理模型替代方案，具有临床与数字孪生应用潜力。
原文摘要 · Abstract (English)
The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using physics-based models such as the bidomain or monodomain equations. While accurate, these approaches are computationally expensive, limiting their use in real-time and large-scale clinical applications. We propose a proof-of-concept deep learning (DL) framework as an efficient surrogate for forward solvers. The model adopts a time-dependent, attention-based sequence-to-sequence architecture to predict electrocardiogram (ECG) signals from cardiac voltage propagation maps. A hybrid loss combining Huber loss with a spectral entropy term was introduced to preserve both temporal and frequency-domain fidelity. Using 2D tissue simulations incorporating healthy, fibrotic, and gap junction-remodelled conditions, the model achieved high accuracy (mean $R^2 = 0.99 \pm 0.01$). Ablation studies confirmed the contributions of convolutional encoders, time-aware attention, and spectral entropy loss. These findings highlight DL as a scalable, cost-effective alternative to physics-based solvers, with potential for clinical and digital twin applications.
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