한빛사논문
- 조회486
Shinjae Kwon1,2†, Hyeon Seok Kim1,2†, Kangkyu Kwon1,3, Hodam Kim1,2, Yun Soung Kim4, Sung Hoon Lee1,3, Young-Tae Kwon5, Jae-Woong Jeong6, Lynn Marie Trotti7, Audrey Duarte8, Woon-Hong Yeo1,2,9,10*
1IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
2George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
3School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
4Department of Radiology, Icahn School of Medicine at Mount Sinai, BioMedical Engineering and Imaging Institute, New York, NY 10029, USA.
5Metal Powder Department, Korea Institute of Materials Science, Changwon 51508, Republic of Korea.
6School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
7Emory Sleep Center and Department of Neurology, Emory University School of Medicine, Atlanta, GA 30329, USA.
8Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA.
9Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA 30332, USA.
10Parker H. Petit Institute for Bioengineering and Biosciences, Institute for Materials, Neural Engineering Center, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA.
†These authors contributed equally to this work.
*Corresponding author : Woon-Hong Yeo
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.
논문정보
- Research article
- 2023년 05월 (BRIC 등록일 2023-06-22)
- 국내+국외 연구진
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