On Improving PPG-Based Sleep Staging: A Pilot Study

• URL: http://arxiv.org/abs/2508.02689v1
• Date: Wed, 23 Jul 2025 14:11:41 GMT
• Title: On Improving PPG-Based Sleep Staging: A Pilot Study
• Authors: Jiawei Wang, Yu Guan, Chen Chen, Ligang Zhou, Laurence T. Yang, Sai Gu,
• Abstract summary: Photoplethys(mography) sensors are widely adopted in consumer devices, but consistently reliable sleep staging using PPG alone remains a non-trivial challenge.<n>We compare conventional single-stream model with dual-stream cross-attention strategies, based on which complementary information can be learned via PPG and PPG-derived modalities.<n>We found that substantial performance gain can be achieved by combining PPG and its auxiliary information under the dual-stream cross-attention architecture.
• Score: 21.462014806247353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sleep monitoring through accessible wearable technology is crucial to improving well-being in ubiquitous computing. Although photoplethysmography(PPG) sensors are widely adopted in consumer devices, achieving consistently reliable sleep staging using PPG alone remains a non-trivial challenge. In this work, we explore multiple strategies to enhance the performance of PPG-based sleep staging. Specifically, we compare conventional single-stream model with dual-stream cross-attention strategies, based on which complementary information can be learned via PPG and PPG-derived modalities such as augmented PPG or synthetic ECG. To study the effectiveness of the aforementioned approaches in four-stage sleep monitoring task, we conducted experiments on the world's largest sleep staging dataset, i.e., the Multi-Ethnic Study of Atherosclerosis(MESA). We found that substantial performance gain can be achieved by combining PPG and its auxiliary information under the dual-stream cross-attention architecture. Source code of this project can be found at https://github.com/DavyWJW/sleep-staging-models

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