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.

Self-supervised foundation model for photoplethysmography (PPG) that explicitly targets the low-signal-quality problem endemic to real-world wearable and bedside PPG, rather than assuming clean input. A Siamese ResNet (SimSiam-style) encoder is pretrained to produce similar representations for temporally-adjacent high- and low-quality PPG segments presumed to share the same underlying physiological state, with curriculum learning that gradually increases the artifact-level gap between paired segments. Pretrained on over 36 million 30-second PPG pairs drawn from more than 24,100 UCSF ICU patients, then fine-tuned and evaluated on six downstream cardiovascular-monitoring datasets/tasks (heart rate, respiratory rate, blood pressure, and atrial-fibrillation detection), where it exceeds prior state-of-the-art by roughly 75% on respiratory-rate estimation and 5% on AF detection, with performance scaling with backbone depth (ResNet152 best). Code and the pretraining dataset are not public; the pretraining data is available upon request under a UCSF-Emory data use agreement.

Multi-task model that jointly assesses signal quality and detects atrial fibrillation from wrist-worn wearable photoplethysmography (PPG), pretrained on roughly one million simulated unlabeled signals before fine-tuning on labeled wearable data. Uses a 1D CNN with separate output heads for signal quality and arrhythmia detection. Developed by Stanford's Ashley Lab.

Translates a raw PPG waveform into a full continuous arterial blood-pressure waveform, from which systolic, diastolic, and mean arterial pressure are derived. Uses a two-stage cascaded 1D convolutional network in a U-Net style, with a coarse approximation stage followed by a refinement stage. Meets BHS Grade A and AAMI accuracy standards for diastolic and mean arterial pressure. Developed at BUET.

One of the first open foundation models for PPG signals, pretrained on over 57,000 hours (20 million segments) of publicly available data using a morphology-aware self-supervised objective. Evaluated across 20 tasks from 10 datasets spanning cardiovascular health, sleep disorders, pregnancy monitoring, and general wellbeing. Developed by Nokia Bell Labs and published at ICLR 2025.

Open-source PPG foundation model pretrained directly on real-world, field-collected wearable data rather than clean clinical signals alone, aiming for better generalization to the noise of free-living conditions. Uses a ResNet-based encoder trained with a relative contrastive (RelCon) self-supervised objective, and is directly benchmarked against PaPaGei. Developed at the University of Illinois Urbana-Champaign and published at UbiComp 2025.