MATEC Web of Conferences
Volume 22, 2015International Conference on Engineering Technology and Application (ICETA 2015)
|Number of page(s)||6|
|Section||Chemical and Industrial Technology|
|Published online||09 July 2015|
A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG
1 College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
2 Key lab of Biomedical Engineering of Ministry of Education, Zhejiang University
3 Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effec-tiveness Appraisal Hangzhou, China
* Corresponding author: email@example.com
Sleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG). The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using electrooculogram (EOG) and electromyography (EMG) automatically. We extracted right and left EOG features and EMG feature in time domain, and classified them into strong, weak and none types through calculating self-adaptive threshold. Combination of the time features of EOG and EMG signals, we classified sleep stages into Wake, REM and NREM stages. The time domain features utilized in the method were Integrate Value, variance and energy. The experiment of 30 datasets showed a satisfactory result with the accuracy of 82.93% for Wake, NREM and REM stages classification, and the average accuracy of Wake stage classification was 83.29%, 82.11% for NREM stage and 76.73% for REM stage.
Key words: sleep stage classification / self-adaptive threshold / EOG / EMG
© Owned by the authors, published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.