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Epileptic seizure detection of electroencephalogram based on weighted-permutation entropy

  • Zhenxi Song
  • , Jiang Wang
  • , Lihui Cai
  • , Bin Deng
  • , Yingmei Qin
  • Tianjin University
  • TianJin University of Technology and Education

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

Abstract

Weighted-permutation entropy (WPE) is modified from permutation entropy (PE), which recently has been proposed as a measurement for nonlinear time series. To explore the efficiency of this method and the features of seizure electroencephalogram (EEG) segments, we investigate the application of WPE in the complexity analysis for epileptic seizure detection based on EEG. It is found that the calculated values of WPE are decreased during seizure segments in the contrast with seizure-free. Moreover, WPE provides a highly distinguishable feature for classifier to obtain more accurate results of automatic classification compared with PE by support vector machine (SVM).

Original languageEnglish
Title of host publicationProceedings of the 2016 12th World Congress on Intelligent Control and Automation, WCICA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2819-2823
Number of pages5
ISBN (Electronic)9781467384148
DOIs
StatePublished - 27 Sep 2016
Externally publishedYes
Event12th World Congress on Intelligent Control and Automation, WCICA 2016 - Guilin, China
Duration: 12 Jun 201615 Jun 2016

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume2016-September

Conference

Conference12th World Congress on Intelligent Control and Automation, WCICA 2016
Country/TerritoryChina
CityGuilin
Period12/06/1615/06/16

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