用多模态信息提升可穿戴设备的睡眠分期准确率
On Improving PPG-Based Sleep Staging: A Pilot Study
- 采用双流交叉注意力架构融合PPG与衍生信号
- 在MESA数据集上四阶段睡眠分期准确率达83.6%
- 适合关注可穿戴睡眠监测的开发者和医疗研究者
通过可及的可穿戴技术进行睡眠监测对提升普适计算中的健康水平至关重要。尽管光电容积脉搏波描记法（PPG）传感器广泛应用于消费类设备，但仅依靠PPG实现稳定可靠的睡眠分期仍具挑战。本文探索多种策略以提升基于PPG的睡眠分期性能。具体而言，比较了传统单流模型与双流交叉注意力策略，后者可通过PPG及其衍生模态（如增强型PPG或合成ECG）学习互补信息。为评估上述方法在四阶段睡眠监测任务中的有效性，我们在全球最大的睡眠分期数据集——多民族动脉粥样硬化研究（MESA）上进行了实验。结果表明，在双流交叉注意力架构下结合PPG及其辅助信息可显著提升性能。项目源代码见https://github.com/DavyWJW/sleep-staging-models。
原文摘要 · Abstract (English)
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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