Automating sleep stage classification using wireless, wearable sensors
Automating sleep stage classification using wireless, wearable sensors
复制标题
利用无线可穿戴传感器自动进行睡眠阶段分类
DOI：
10.1038/s41746-019-0210-1
复制
发表时间：
2019-12-01
影响因子：
15.2
通讯作者：
Jayaraman, Arun
中科院分区：
文献类型：
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作者：
Boe, Alexander J.;Koch, Lori L. McGee;Jayaraman, Arun
多导睡眠图（PSG）是目前高分辨率睡眠监测的黄金标准;然而，这种方法是突兀的，昂贵的，耗时的。相反，市售的手腕监测器，如ActiWatch，可以监测睡眠多天，成本低，但往往高估睡眠，不能区分睡眠阶段，如快速眼动（REM）和非REM。无线可穿戴传感器因其便携性和访问高分辨率数据以进行可定制分析而成为一种有前途的替代方案。我们提出了一种多模式传感器系统，测量手加速度，心电图和远端皮肤温度，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的召回率分别为73.3%，59.0%和56.0%。这种方法将使临床医生和研究人员能够更容易，更准确，更便宜地评估长期睡眠模式，诊断睡眠障碍，并在实验室和家庭环境中监测疾病的风险因素。
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.