GPT-style decoder-only Transformer pretrained via next-token prediction on tokenized PPG time series, the companion model to ECG-PT (HeartGPT) in the same repository. Individual attention heads respond to physiologically meaningful waveform features such as the dicrotic notch, and the model can be fine-tuned for wearable-based cardiac screening tasks. Developed at Imperial College London.

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.