PPG-Distill
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PPG-Distill is a knowledge-distillation framework that compresses PPG foundation teachers such as GPT-PPG-19m and PaPaGei into roughly 1M-parameter students for edge devices. It adds morphology and rhythm distillation on top of prediction and feature KD so heart-rate and atrial-fibrillation performance stay close to the teacher at a fraction of the cost.
- Venue
- NeurIPS 2025 TS4H Workshop (Spotlight)
- Signals
- PPG
- Authors
- Ni, Kataria, Tang, Yang, Hu, Jin (Emory)
- Teachers
- GPT-PPG-19m, PaPaGei → students GPT-PPG-1m / MLP
- Code
- github.com/xiaoyuli0575/PPG-Distill
- Year
- 2025
DaLiA HR MAE 8.34 bpm; StanfordAF F1 0.77 (+21.8% relative)
Up to 7× faster inference and 19× lower memory vs teacher
- Type
- FM adaptation
- Modalities
- PPG
- Form factor
- Wrist edge student
- Parameters
- 1M
- Architecture
- Prediction + feature + patch morphology (InfoNCE) + rhythm (relational) KD; students GPT-PPG-1m / MLP; teachers GPT-PPG-19m / PaPaGei
- Venue
- NeurIPS 2025 Workshop on Learning from Time Series for Health (TS4H Spotlight)
- Year
- 2025
- DOI
- Not reported
- Weights
- Not released
- Licence
- Not reported
- Caveats
- Not reported
- Added
- Oct 25, 2026
- Updated
- Oct 25, 2026

How it works
Datasets linked in the hub; pretraining vs evaluation roles follow the paper.
- Architecture
- Prediction + feature + patch morphology (InfoNCE) + rhythm (relational) KD; students GPT-PPG-1m / MLP; teachers GPT-PPG-19m / PaPaGei
- Inputs
- PPG
- Approach
- PPG · distillation · knowledge distillation · edge · emory · neurips 2025
Devices· 2

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Datasets· 2
Papers· 1
Cite
APA
Ni, J., Kataria, S., Tang, S., Yang, C., Hu, X., & Jin, W. (2025). PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation. NeurIPS 2025 Workshop on Learning from Time Series for Health. https://arxiv.org/abs/2509.19215
BibTeX
@inproceedings{ni2025ppgdistill,
title = {{PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation}},
author = {Ni, Juntong and Kataria, Saurabh and Tang, Shengpu and Yang, Carl and Hu, Xiao and Jin, Wei},
year = {2025},
booktitle = {NeurIPS 2025 Workshop on Learning from Time Series for Health},
eprint = {2509.19215},
archivePrefix = {arXiv}
}Built from the hub record. Check it against the publisher before submitting.
History
Added to the ChatPPG research hub
Connections
Open in the graph explorerPapers
- PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model DistillationPaper on this modelNeurIPS 2025 Workshop on Learning from Tim · 2025
Bridging ECG and PPG: Latent-Space Prediction for Robust Physiological AnalysisAlso uses PPG-DaLiAKDD '26 · 2026
GPT-PPG: a GPT-based foundation model for photoplethysmography signalsAlso uses PPG-DaLiAPhysiological Measurement · 2025
