Self-supervised multi-encoder autoencoder (MEAE) that separates heartbeat-related source signals from noisy photoplethysmogram (PPG) via blind source separation, improving heart-rate detection without requiring any pre-processing or manual data selection. Trained entirely on PPG signals from a large open polysomnography database (with no cleaning or curation), the model is then applied to a noisy real-world PPG dataset collected during daily activities of 9 subjects and a surgical dataset of 4,681 patients; the extracted heartbeat-related source signal significantly improves heart-rate detection accuracy compared with using the raw PPG signal directly.

ECG-guided photoplethysmography (PPG) foundation model pretrained on over 100,000 hours of synchronized PPG-ECG recordings from 58,796 subjects across five clinical and wearable sources, using a CLIP-style contrastive alignment framework so the PPG encoder inherits physiologically grounded structure from paired ECG. Achieves state-of-the-art performance on 13 of 15 conventional physiological-analysis tasks across eight datasets, and shows meaningful discriminative capability (AUC >= 0.70) for 307 ICD-10-coded phenotypes across 16 phecode chapters, including many non-cardiovascular conditions.