EEG signal analysis of patients with obstructive sleep apnea syndrome (OSAS) using power spectrum and fuzzy entropy

Szu Yu Lin, Yu Te Wu, Wei Chung Mao, Po Shan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Sleep is important for the restoration and renewal of the human body. Obstructive sleep apnea syndrome (OSAS), which is caused by repetitive episodes of partial or complete upper airway obstruction during sleep, is the most common type of sleep apnea. The sleep electroencephalogram (EEG) analysis has been an important tool to investigate brain activity. In this study, we used the spectral analysis and fuzzy entropy to analyze the EEG signals collected from the OSAS patients and normal control. Results obtained from the EEG power spectrum and fuzzy entropy with and without principal component analysis (PCA) process were used as the features and fed into four different classifiers, namely, linear Support Vector Machines (SVM), Liner Discriminant Analysis (LDA), subspace k-nearest neighbor (k-NN) and subspace discriminant analysis, to differentiate these two groups. Our results demonstrated that the feature resulted from power spectrum with PCA process and subspace discriminate method using 5-fold cross-validation produces the superior classification rate which is 89 ± 3.74%.

Original languageEnglish
Title of host publicationICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
EditorsLiang Zhao, Lipo Wang, Guoyong Cai, Kenli Li, Yong Liu, Guoqing Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages740-744
Number of pages5
ISBN (Electronic)9781538621653
DOIs
StatePublished - 21 Jun 2018
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China
Duration: 29 Jul 201731 Jul 2017

Publication series

NameICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Conference

Conference13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Country/TerritoryChina
CityGuilin, Guangxi
Period29/07/1731/07/17

Keywords

  • electroencephalogram
  • fuzzy entropy
  • Machine learning
  • Obstructive sleep apnea syndrome
  • principal component analysis
  • Sleep
  • spectral analysis

Fingerprint

Dive into the research topics of 'EEG signal analysis of patients with obstructive sleep apnea syndrome (OSAS) using power spectrum and fuzzy entropy'. Together they form a unique fingerprint.

Cite this