用可穿戴PPG生成12导联心电图，突破信号重建瓶颈
Translation from Wearable PPG to 12-Lead ECG
- 基于扩散模型，融合频域模糊与时序噪声模拟真实信号畸变
- 逆过程采用多尺度时序生成与频域去模糊，提升重构精度
- 结合KNN聚类与对比学习，实现基于人群特征的个性化心电生成
12导联心电图（ECG）是心血管监测的金标准，诊断精度和特异性优于光电容积脉搏波（PPG）。然而现有12导联系统依赖繁琐的多电极配置，难以实现长期可移动监测；而当前基于PPG的方法因缺乏导联间约束且对导联间时空依赖建模不足，无法重建多导联心电图。为弥合此差距，我们提出P2Es——一种面向人口统计学特征的扩散框架，通过三个关键创新，实现从PPG信号生成临床有效的12导联心电图。具体而言，在前向过程中引入频域模糊后叠加时序噪声，以模拟真实信号畸变；在反向过程中设计时序多尺度生成模块，并配合频域去模糊；特别地，利用KNN聚类结合对比学习，为反向过程构建亲和矩阵，实现人群特征相关的心电图转换。大量实验表明，P2Es在12导联心电图重建任务上优于基线模型。
原文摘要 · Abstract (English)
The 12-lead electrocardiogram (ECG) is the gold standard for cardiovascular monitoring, offering superior diagnostic granularity and specificity compared to photoplethysmography (PPG). However, existing 12-lead ECG systems rely on cumbersome multi-electrode setups, limiting sustained monitoring in ambulatory settings, while current PPG-based methods fail to reconstruct multi-lead ECG due to the absence of inter-lead constraints and insufficient modeling of spatial-temporal dependencies across leads. To bridge this gap, we introduce P2Es, an innovative demographic-aware diffusion framework designed to generate clinically valid 12-lead ECG from PPG signals via three key innovations. Specifically, in the forward process, we introduce frequency-domain blurring followed by temporal noise interference to simulate real-world signal distortions. In the reverse process, we design a temporal multi-scale generation module followed by frequency deblurring. In particular, we leverage KNN-based clustering combined with contrastive learning to assign affinity matrices for the reverse process, enabling demographic-specific ECG translation. Extensive experimental results show that P2Es outperforms baseline models in 12-lead ECG reconstruction.
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