Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Classification of Sleep Stages Using EOG, EEG, EMG Signal Analysis

안전도, 뇌파도, 근전도 분석을 통한 수면 단계 분류

  • Kim, HyoungWook (Dept. of Information and Communications Engineering, Changwon National University) ;
  • Lee, YoungRok (Dept. of Eco-friendly Offshore FEED Engineering, Changwon National University) ;
  • Park, DongGyu (Dept. of Information and Communications Engineering, Changwon National University)
  • Received : 2019.08.27
  • Accepted : 2019.11.30
  • Published : 2019.12.31
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Abstract

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

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References

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