多导睡眠图
可穿戴计算机
睡眠(系统调用)
医学
睡眠呼吸暂停
计算机科学
可穿戴技术
软件可移植性
可用性
阻塞性睡眠呼吸暂停
睡眠阶段
安眠药
呼吸暂停
嵌入式系统
睡眠障碍
人机交互
认知
内科学
精神科
程序设计语言
操作系统
作者
Shinjae Kwon,Hyeon Seok Kim,Kangkyu Kwon,Hodam Kim,Yun‐Soung Kim,Sung Hoon Lee,Young‐Tae Kwon,Jae‐Woong Jeong,Lynn Marie Trotti,Audrey Duarte,Woon‐Hong Yeo
出处
期刊:Science Advances
[American Association for the Advancement of Science]
日期:2023-05-24
卷期号:9 (21)
被引量:66
标识
DOI:10.1126/sciadv.adg9671
摘要
Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.
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