At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

• Type of Material: Article
• Authors: Shinjae Kwon, Georgia Institute of Technology; Hyeon S Kim, Georgia Institute of Technology; Kangkyu Kwon, Georgia Institute of Technology; Hodam Kim, Georgia Institute of Technology; Yun Soung Kim, Icahn School of Medicine at Mount Sinai; Sung Hoon Lee, Georgia Institute of Technology; Jae-Woong Jeong, Korea Advanced Institute of Science and Technology; Lynn Trotti, Emory University; Audrey Duarte, University of Texas Austin; Woon-Hong Yeo, Georgia Institute of Technology
• Language: English
• Date: 2023-05-26
• Publisher: AMER ASSOC ADVANCEMENT SCIENCE
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1126/sciadv.adg9671
• Title of Journal or Parent Work: SCIENCE ADVANCES
• Volume: 9
• Issue: 21
• Start Page: eadg9671
• End Page: eadg9671
• Grant/Funding Information: We acknowledge the support of the Alzheimer’s Association (2019-AARGD-NTF-643460) and the National Institutes of Health (R21AG064309). This work was also supported by the IEN Center Grant from the Georgia Tech Institute for Electronics and Nanotechnology. Electronic devices in this work were fabricated at the Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the NSF (grant ECCS-2025462). J.-W.J. acknowledges the support of the National Research Foundation of Korea (NRF-2020M3C1B8A01111568 and NRF-2022M3E5E9017759).
• Keywords: SOFT; Science & Technology; Science & Technology - Other Topics; HEALTH; MORTALITY; Multidisciplinary Sciences; INTERFACES
• Research Categories: Engineering, Biomedical; Health Sciences, Radiology

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.