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Statistical parameters in the dual tree complex wavelet transform domain for the detection of epilepsy and seizure

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

8 Scopus citations

Abstract

In this paper, a comprehensive statistical analysis of electroencephalogram (EEG) signals is carried out in the dual tree complex wavelet transform domain using a publicly available EEG database. It is shown that variance and kurtosis can be effective in distinguishing EEG signals at sub-band levels. It is further shown that the parameters of a normal inverse Gaussian probability density function can equally discriminate the EEG signals at sub-band levels. Thus, these statistical quantities may be used to characterize EEG signals and help the researchers in developing improved classifiers for the detection of epilepsy and seizure and building a better understanding of the diverse process of EEG signals.

Original languageEnglish
Title of host publication2013 International Conference on Electrical Information and Communication Technology, EICT 2013
PublisherIEEE Computer Society
ISBN (Print)9781479922994
DOIs
StatePublished - 2014
Externally publishedYes
Event2013 International Conference on Electrical Information and Communication Technology, EICT 2013 - Khulna, Bangladesh
Duration: Feb 13 2014Feb 15 2014

Publication series

Name2013 International Conference on Electrical Information and Communication Technology, EICT 2013

Conference

Conference2013 International Conference on Electrical Information and Communication Technology, EICT 2013
Country/TerritoryBangladesh
CityKhulna
Period02/13/1402/15/14

Keywords

  • Dual Tree Complex Wavelet Transform(DT-CWT)
  • Electroencephalogram(EEG)
  • Normal Inverse Gaus-sian(NIG)
  • Seizure

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