At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch

• Type of Material: Article
• Authors: Nathan Zavanelli, Georgia Institute of Technology; Hojoong Kim, Georgia Institute of Technology; Jongsu Kim, Georgia Institute of Technology; Robert Herbert, Georgia Institute of Technology; Musa Mahmood, Georgia Institute of Technology; Yun-Soung Kim, Georgia Institute of Technology; Shinjae Kwon, Georgia Institute of Technology; Nicholas B Bolus, Huxley Med Inc; Brennan F Torstrick, Huxley Med Inc; Christopher SD Lee, Huxley Med Inc; Woon-Hong Yeo, Emory University
• Language: English
• Date: 2021-12-01
• Publisher: AMER ASSOC ADVANCEMENT SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2021 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 License 4.0 (CC BY).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1126/sciadv.abl4146
• Title of Journal or Parent Work: SCIENCE ADVANCES
• Volume: 7
• Issue: 52
• Start Page: eabl4146
• End Page: eabl4146
• Grant/Funding Information: This work was supported by the Georgia Tech IEN Center for Human-Centric Interfaces and Engineering and the Huxley Medical. 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).
• Keywords: CONSEQUENCES; Science & Technology - Other Topics; Science & Technology; Multidisciplinary Sciences
• Research Categories: Engineering, Biomedical

Abstract

Obstructive sleep apnea (OSA) affects more than 900 million adults globally and can create serious health complications when untreated; however, 80% of cases remain undiagnosed. Critically, current diagnostic techniques are fundamentally limited by low throughputs and high failure rates. Here, we report a wireless, fully integrated, soft patch with skin-like mechanics optimized through analytical and computational studies to capture seismocardiograms, electrocardiograms, and photoplethysmograms from the sternum, allowing clinicians to investigate the cardiovascular response to OSA during home sleep tests. In preliminary trials with symptomatic and control subjects, the soft device demonstrated excellent ability to detect blood-oxygen saturation, respiratory effort, respiration rate, heart rate, cardiac pre-ejection period and ejection timing, aortic opening mechanics, heart rate variability, and sleep staging. Last, machine learning is used to autodetect apneas and hypopneas with 100% sensitivity and 95% precision in preliminary at-home trials with symptomatic patients, compared to data scored by professionally certified sleep clinicians.