대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제36권5호
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  • Pages.204-210
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  • 2015
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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피부전기활동을 이용한 실시간 깊은 수면 검출 알고리즘의 개발

Real-time Detection of Deep Sleep using Electrodermal Activity

  • 정다운 (서울대학교 공과대학 협동과정 바이오엔지니어링전공) ;
  • 최상호 (서울대학교 공과대학 협동과정 바이오엔지니어링전공) ;
  • 주광민 (서울대학교 공과대학 협동과정 바이오엔지니어링전공) ;
  • 이유진 (서울대학교병원 수면의학센터) ;
  • 정도언 (서울대학교병원 수면의학센터) ;
  • 박광석 (서울대학교 의과대학 의공학교실)
  • Jung, Da Woon (Interdisciplinary Program for Biomedical Engineering, Seoul National University Graduate School) ;
  • Choi, Sang Ho (Interdisciplinary Program for Biomedical Engineering, Seoul National University Graduate School) ;
  • Joo, Kwang Min (Interdisciplinary Program for Biomedical Engineering, Seoul National University Graduate School) ;
  • Lee, Yu Jin (Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine and Center for Sleep and Chronobiology, Seoul National University Hospital) ;
  • Jeong, Do-Un (Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine and Center for Sleep and Chronobiology, Seoul National University Hospital) ;
  • Park, Kwang Suk (Department of Biomedical Engineering, Seoul National University College of Medicine)
  • 투고 : 2015.09.01
  • 심사 : 2015.10.13
  • 발행 : 2015.10.31
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초록

Although many studies have analyzed the relationship between electrodermal activity (EDA) and sleep stages, a practical method for detecting sleep stage using EDA has not been suggested. The aim of this study was to develop an algorithm for real-time automatic detection of deep sleep using the EDA signal. Simultaneously with overnight polysomnography (PSG), continuous measurement of skin conductance on the fingers was performed for ten subjects. The morphometric characteristics in the fluctuations of EDA signal were employed to establish the quantitative criteria for determining deep sleep. The 30-sec epoch-by-epoch comparison between the deep sleep detected by our method and that reported from PSG exhibited an average sensitivity of 74.6%, an average specificity of 98.0%, and an average accuracy of 96.1%. This study may address the growing need for a reliable and simple measure for identifying sleep stage without a PSG.

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