Polysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.

中文翻译：

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使用无线、可穿戴传感器自动进行睡眠阶段分类。

多导睡眠图 (PSG) 是当前高分辨率睡眠监测的黄金标准；然而，这种方法麻烦、昂贵且耗时。相反，市场上出售的手腕监测器（如 ActiWatch）可以监测多天的睡眠并且成本低廉，但往往会高估睡眠并且无法区分睡眠阶段，例如快速眼动 (REM) 和非 REM。无线可穿戴传感器是一种很有前途的替代方案，因为它们具有便携性和访问高分辨率数据以进行自定义分析的能力。我们提出了一种多模式传感器系统，可测量手部加速度、心电图和远端皮肤温度，其性能优于 ActiWatch，检测唤醒和睡眠的召回率分别为 74.4% 和 90.0%，以及唤醒、非 REM 和 REM 召回率分别为 73.3%、59.0% 和 56.0%。