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Abstract

Sleep is very important for the health. Analyzing the polysomnography (PSG) helps us get valuable information to assess the quality of sleep. In this work, we develop a program to automatically detect the transition from wakefulness to sleep in adults. The accurate detection of the point of sleep onset occurs in the first time is useful for assessing the micro-structure of sleep. The proposed method is analyzed polysomnography of 30 healthy volunteers, using data of one channel Electroencephalography, Electrooculography and chin Electromyography. The algorithm automatically analyzes every second according to American Academy of Sleep Medicine (AASM) standards with the latest version. The results obtained under two levels: identify and list the epoch occurred the transition, and exact the time of the shift occurred. With more than 85% in accuracy, the study shows the feasibility to provide timely warning. This approach opens up developing a system in real-time warning: doze off in student, drowsiness, sleepiness when driving or working. It helps us to examine the brain's response to external stimuli to reduce the time of sleep latency.



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Article Details

Issue: Vol 20 No K3 (2017)
Page No.: 18-24
Published: Jun 30, 2017
Section: Engineering and Technology - Research article
DOI: https://doi.org/10.32508/stdj.v20iK3.1080

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Le, K., Dinh, A., Tran, B., & Huynh, L. (2017). Automatic detection of sleep onset in healthy adults. VNUHCM Journal of Science and Technology Development, 20(K3), 18-24. https://doi.org/https://doi.org/10.32508/stdj.v20iK3.1080

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