2023-05-24 ジョージア工科大学
◆従来の方法では、睡眠障害の疑いがある人は医療施設に行かなければならず、脳、目、筋肉の活動を記録するためのワイヤードプローブに接続され、夜間に監視されます。このウェアラブルデバイスは、自宅で使用できるため、睡眠ラボでのより高価な医療処置の代替手段となり得ます。
◆睡眠時無呼吸症候群は心臓病や糖尿病などの既存の疾患を持つ人々にとって健康問題を悪化させる可能性があります。さらに、睡眠時無呼吸症候群は長期間放置されるほど心臓と脳に悪影響を及ぼすため、早期の発見と治療が重要です。
<関連情報>
- https://research.gatech.edu/georgia-tech-researchers-develop-wireless-monitoring-patch-system-detect-sleep-apnea-home
- https://www.science.org/doi/10.1126/sciadv.adg9671
睡眠の質と睡眠時無呼吸症候群の臨床評価のための家庭用無線睡眠モニタリングパッチ At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea
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 and Woon-Hong Yeo
Science Advances Published:24 May 2023
DOI:https://doi.org/10.1126/sciadv.adg9671
Abstract
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.
