融合脑电与心率信号，用短时数据实现精准睡眠分期。
Combining scEEG and PPG for reliable sleep staging using lightweight wearables
- 用30秒到30分钟短窗口数据，融合scEEG与PPG信号进行睡眠分期。
- Mamba增强融合方法在MESA数据集上达到Kappa=0.798，轻度睡眠识别率提升显著。
- 适合开发轻量可穿戴设备，支持即时睡眠干预与跨人群通用性评估。
可靠睡眠分期对单通道脑电（scEEG）或光电容积脉搏波（PPG）等轻量可穿戴设备仍具挑战。scEEG直接测量皮层活动，是睡眠分期基础，但对轻度睡眠阶段表现有限；PPG提供低成本互补，捕捉自主神经信号，有效检测轻度睡眠。然而，现有基于PPG的方法依赖整夜8–10小时输入上下文，难以实现及时反馈。本文研究在短窗（30秒–30分钟）约束下，scEEG-PPG融合用于四类睡眠分期。首先评估各模态所需的时间上下文；其次探索三种融合策略：评分级融合、支持特征级交互的交叉注意力融合，以及结合时序建模能力的Mamba增强融合。在多族裔动脉粥样硬化研究（MESA）数据集上训练并验证，同时在克利夫兰家庭研究（CFS）和睡眠呼吸暂停、减肥手术与CPAP（ABC）数据集上进行跨数据集测试。Mamba增强融合在MESA上表现最佳（Cohen's Kappa $κ$ = 0.798，准确率86.9%），尤其在轻度睡眠分类上显著提升（F1-score: 85.63% vs. 77.76%，召回率82.85% vs. 69.95% for scEEG alone），且在不同人群数据集上具有良好泛化能力。结果表明，scEEG-PPG融合是轻量可穿戴睡眠监测的有前景方案，为更可及的睡眠健康评估开辟路径。
原文摘要 · Abstract (English)
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 $κ$ = 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: https://github.com/DavyWJW/scEEG-PPGFusion
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