Title page for etd-0718108-170721


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URN etd-0718108-170721
Author Yen-shi Wang
Author's Email Address No Public.
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Department Mechanical and Electro-Mechanical Engineering
Year 2007
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title The detection of REM and Wake sleep stages by using EOG signals
Date of Defense 2008-07-04
Page Count 98
Keyword
  • wake sleep
  • REM sleep
  • Abstract To detect REM and wake stages in sleep, this study generates feature variables from the correlation of two-channel EOG signals and the amplitude of LEOG signal. By using the VQ method to quantize these signals into different codewords and by calculating the number of appearances of these codewords, we are able to establish a feature vector for every epoch of the recorded EOG signals. Via a three-stage process, the personalized classification accuracy for REM and wake sleep stages are about 95% and 86%, respectively. By combining these personalized classifiers to perform REM and wake stages detection for other unseen individuals, the classification accuracy for REM and wake sleep stages, the classification accuracy become 85% and 92%. However, the sensitivity for the wake stage detection is merely 52%.
    Advisory Committee
  • Jiann-der Lee - chair
  • Liang-wen Hang - co-chair
  • Chen-wen Yen - advisor
  • Files
  • etd-0718108-170721.pdf
  • indicate access worldwide
    Date of Submission 2008-07-18

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