Title page for etd-0713115-154648


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URN etd-0713115-154648
Author Chih-Liang Wei
Author's Email Address No Public.
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Department Mechanical and Electro-Mechanical Engineering
Year 2015
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Developing algorithms to distinguish insomnia patients from normal people via the single channel forehead signal
Date of Defense 2015-06-29
Page Count 127
Keyword
  • Continuous Wavelet Transform
  • Insomnia
  • Single Channel EEG
  • Multiscale Entropy
  • Zero Crossing points
  • Machine Learning
  • Abstract Polysomnography (PSG) is considered the gold standard in the diagnosis of sleep disordered breathing. However, compared with the single channel EEG sensor, PSG is difficult to operate, expensive and not suitable for home use. However, with fewer number of signals, the single channel senor provides much less information than PSG. Hence, extracting sufficient information from a single channel signal is a very important task.
    Considering the fact that insomnia is the most prevalent sleep disorder, this thesis is aimed to develop algorithms to distinguish insomnia patients form normal people via single channel forehead signal. In specific, we use three different approaches to analyze the forehead signal during sleep in order to identify features that have significantly different values between insomnia patients and normal people. The first method uses the zero crossing point interval to characterize the speed of the signal. The second method uses the continuous wavelet transform (CWT) to generate a series of features to assess the time-frequency domain properties of the signal. The third method uses the multiscale entropy criterion to quantify the complexity of the signal. In addition to comparing the value of the features, we also used the percentile rank of these features. By using the Wilcoxon's rank-sum test to find out which of these features have significantly different median values in the two groups, 23 features were selected to classify insomnia patients from normal people by using Quadratic Discriminant Analysis (QDA).
    Selected from these 23 features, we tested many pairs of features. The best classification result was obtained by the combination of the zero crossing point interval and a CWT feature. The best classification accuracy is 97.8%. Additionally, we have identified 50 different feature pairs that can achieve classification accuracy of 90% or higher.
    According to these promising classification results, our algorithm has the potential to effectively distinguish insomnia patients from normal people. With the simplicity of single channel measurement requirement, the proposed approach is ideal for home use. As a result, the proposed method has the potential to become a home-based insomnia screening tool.
    Advisory Committee
  • Pei-Chung Chen - chair
  • Jiann-Der Lee - co-chair
  • Chen-Wen Yen - advisor
  • Files
  • etd-0713115-154648.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2015-08-13

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