Kavli Affiliate: Wei Gao
| First 5 Authors: Hui Ji, Hui Ji, , ,
| Summary:
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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