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.
Architecture
Hybrid
Self-supervised multi-encoder autoencoder that performs blind source separation on PPG signals, isolating a heartbeat-related source channel from noise/motion-artifact-related channels
Framework
PyTorch
Added to catalog
2026-08-14
Daily-Activity Noisy PPG Dataset (MEAE evaluation)
Surgical PPG Dataset (MEAE evaluation)
Heartbeat-related source signal isolated from noisy PPG, used to improve heart rate detection accuracy