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[2602.15042] Combining scEEG and PPG for reliable sleep staging using lightweight wearables
Summary
This article explores the fusion of single-channel EEG (scEEG) and photoplethysmography (PPG) for improved sleep staging in lightweight wearables, highlighting innovative fusion strategies and their effectiveness across different datasets.
Why It Matters
Reliable sleep staging is crucial for health monitoring, especially with the rise of wearable technology. This research presents a significant advancement by combining scEEG and PPG, potentially enhancing sleep health assessments and interventions through accessible technology.
Key Takeaways
- scEEG provides direct cortical activity measurement but struggles with light sleep detection.
- PPG captures autonomic signatures, complementing scEEG for better accuracy.
- Mamba-enhanced fusion outperforms traditional methods, especially in light sleep classification.
- The study demonstrates effective short-window sleep staging, enhancing practical applications for wearables.
- Cross-dataset validation shows the robustness of the proposed method across diverse populations.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.15042 (eess) [Submitted on 4 Feb 2026] Title:Combining scEEG and PPG for reliable sleep staging using lightweight wearables Authors:Jiawei Wang, Liang Xu, Shuntian Zheng, Yu Guan, Kaichen Wang, Ziqing Zhang, Chen Chen, Laurence T. Yang, Sai Gu View a PDF of the paper titled Combining scEEG and PPG for reliable sleep staging using lightweight wearables, by Jiawei Wang and 8 other authors View PDF HTML (experimental) Abstract:Reliable sleep staging remains challenging for lightweight wearable devices such as single-channel electroencephalography (scEEG) or photoplethysmography (PPG). scEEG offers direct measurement of cortical activity and serves as the foundation for sleep staging, yet exhibits limited performance on light sleep stages. PPG provides a low-cost complement that captures autonomic signatures effective for detecting light sleep. However, prior PPG-based methods rely on full night recordings (8 - 10 hours) as input context, which is less practical to provide timely feedback for sleep intervention. In this work, we investigate scEEG-PPG fusion for 4-class sleep staging under short-window (30 s - 30 min) constraints. First, we evaluate the temporal context required for each modality, to better understand the relationship of sleep staging performance with respect to monitoring window. Second, we investigate three fusion strategies: score-level fusion, cross-attention fusion enabling feature-l...
