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

  • ,
  • Hojoong Kim
    ,
  • Jongsu Kim
    ,
  • Robert Herbert
    ,
  • Musa Mahmood
    ,
  • Yun Soung Kim
*Corresponding author for this work
  • Georgia Institute of Technology
    ,
  • IEN Center for Human-Centric Interfaces and Engineering
    ,
  • Huxley Medical Inc.
    ,
  • Wallace H. Coulter Department of Biomedical Engineering
Research Output:
Contribution to journal
Article
Peer-review

Open access

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.

Bibliographic Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Article number

eabl4146

Journal (Volume, Issue Number)

Science Advances (Volume 7, Issue 52)

Publication milestones

  • Published - 12/2021

Publication status

Published - 12/2021

Publication IDs

  • Scopus: 85122040007
  • PubMed: 34936438