P2Es: Physiology-Conditioned Diffusion for 12-Lead ECG Generation from PPG

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 lack of explicit inter-lead constraints and insufficient modeling of spatial-temporal dependencies across leads. To bridge this gap, we introduce P2Es, a physiology-conditioned diffusion framework that integrates several synergistic innovations. First, to resolve the physiological information gap, we propose GroupFinder, a contrastive alignment module that retrieves optimal affinity matrices by aligning the PPG latent space with ECG-derived clusters. Furthermore, to mimic real-world distortions, the forward process applies frequency blurring and temporal noise, while the reverse process employs a hierarchical multi-scale generation module with frequency deblurring to recover fine-grained diagnostic details. Finally, we implement a resource-efficient edge architecture for rapid on-device inference. Extensive experiments demonstrate that P2Es significantly outperforms state-of-the-art baselines. This provides a scalable solution for longitudinal cardiac screening and pre-clinical risk stratification.