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http://dx.doi.org/10.9717/kmms.2019.22.12.1491

Classification of Sleep Stages Using EOG, EEG, EMG Signal Analysis  

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)
Publication Information
Journal of Korea Multimedia Society / v.22, no.12, 2019 , pp. 1491-1499 More about this Journal
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.
Keywords
Sleep Stage Classification; CNN Algorithm; DNN Algorithm; EEG; EOG; EMG; Polysomnography;
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Times Cited By KSCI : 2  (Citation Analysis)
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