arXiv：cs.AI· Jiawei Wang, Liang Xu, Shuntian Zheng, Yu Guan, Kaichen Wang, Ziqing Zhang, Chen Chen, Laurence T. Yang, Sai Gu, M. Jamal Deen·· 23 小时前
结合 scEEG 与 PPG 实现可靠睡眠分期
Combining scEEG and PPG for reliable sleep staging
AI 导读
研究探索在 30 秒至 30 分钟短窗口约束下融合单通道脑电（scEEG）与光电容积脉搏波（PPG）进行 4 类睡眠分期，提出 score-level、cross-attention 与 Mamba-enhanced 三种融合策略。
正文
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-level interactions, and Mamba-enhanced fusion incorporating temporal context modeling. Third, we train and evaluate on the Multi-Ethnic Study of Atherosclerosis (MESA) dataset and perform cross-dataset validation on the Cleveland Family Study (CFS) and the Apnea, Bariatric surgery, and CPAP (ABC) datasets. The Mamba-enhanced fusion achieves the best performance on MESA (Cohen's Kappa $\kappa$ = 0.798, Acc = 86.9%), with particularly notable improvement in light sleep classification (F1-score: 85.63% vs. 77.76%, recall: 82.85% vs. 69.95% for scEEG alone), and generalizes well to CFS and ABC datasets with different populations. These findings suggest that scEEG-PPG fusion is a promising approach for lightweight wearable based sleep monitoring, offering a pathway toward more accessible sleep health assessment. Source code of this project can be found at: this https URL
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.15042 [eess.SP] |
| (or arXiv:2602.15042v2 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2602.15042 arXiv-issued DOI via DataCite |
Submission history
From: Jiawei Wang [view email]
[v1]
Wed, 4 Feb 2026 13:00:35 UTC (2,396 KB)
[v2]
Fri, 9 Oct 2026 21:00:07 UTC (2,929 KB)
来源：arXiv：cs.AI · arxiv.org