Translation from Wearable PPG to 12-Lead ECG

• URL: http://arxiv.org/abs/2509.25480v1
• Date: Mon, 29 Sep 2025 20:36:24 GMT
• Title: Translation from Wearable PPG to 12-Lead ECG
• Authors: Hui Ji, Wei Gao, Pengfei Zhou,
• Abstract summary: The 12-lead electrocardiogram (ECG) is the gold standard for cardiovascular monitoring.<n>Existing 12-lead ECG systems rely on cumbersome multi-electrode setups, limiting sustained monitoring in ambulatory settings.<n>We introduce P2Es, an innovative demographic-aware framework designed to generate clinically valid 12-lead ECG from PPG signals.
• Score: 35.793551592077016
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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