Paperdoi:10.1145/3749978
Semi-supervised Contrastive Learning for Reliable Sleep Staging with Small Labeled Photoplethysmography Data
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)
TL;DR
Addresses scarce labeled wearable PPG by a staging model that jointly consumes sleep-related features and raw PPG, plus FASL semi-supervised contrastive learning that adjusts the loss for reliable multi-stage classification when labels are limited. Targets wearable/home monitoring vs hospital PSG annotation cost.
- Form factor: wrist
Abstract
Sleep monitoring is essential for assessing the quality of the body's rest. In medicine, it involves collecting polysomnography (PSG) data in hospitals, along with expert annotations. For convenient sleep monitoring, some studies on wearable devices train models using labeled photoplethysmography (PPG) data to classify multiple sleep stages. However, the difficulty of collecting labeled PPG data limits the application of these studies. To address the impaired model performance caused by the limited labeled PPG data, we develop a sleep staging model that simultaneously inputs sleep-related features and raw data to leverage their advantages in high task correlation and rich information. Furthermore, we design a semi-supervised contrastive learning method, FASL, which adjusts the loss function based on a soft label confidence coefficient to utilize the majority of unlabeled data and enhance the model's generalization ability. Finally, we apply semi-supervised contrastive learning to the features before the Sequence Encoder to mitigate the impact of individual variations. Compared to the best semi-supervised baseline, our approach demonstrates apparent performance improvements on two publicly available datasets and one self-collected dataset. To our knowledge, this work is the first to investigate a semi-supervised sleep monitoring method using PPG data, providing guidance on how to leverage unlabeled PPG data for sleep staging. The code and self-collected dataset used in this study are publicly available at https://github.com/QiWangXPY/FASL.
Verbatim from the publisher's DOI record (opens in a new tab) · Abstract as deposited by the publisher with Crossref; © the authors/publisher
Results on ChatPPG leaderboards
All leaderboardsNo results from this paper on ChatPPG leaderboards yet. Browse the leaderboards.
Tasks & signals
Tasks
Signals
Keywords
How it works
Cite this paper
APA
Wang, Q., Zhang, Z., Zhu, G., Yao, Y., Wang, Z., Wang, Y., Fu, Y., Song, Y., Zhao, D., & Ma, H. (2025). Semi-supervised Contrastive Learning for Reliable Sleep Staging with Small Labeled Photoplethysmography Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. https://doi.org/10.1145/3749978
BibTeX
@inproceedings{wang2025semisupervised,
title = {{Semi-supervised Contrastive Learning for Reliable Sleep Staging with Small Labeled Photoplethysmography Data}},
author = {Wang, Qi and Zhang, Zhengyuan and Zhu, Guanzhou and Yao, Yuxing and Wang, Zilong and Wang, Yiyang and Fu, Yuxuan and Song, Yixuan and Zhao, Dong and Ma, Huadong},
year = {2025},
booktitle = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
doi = {10.1145/3749978}
}Built from the hub record. Check it against the publisher before submitting.
Added to the ChatPPG research hub
