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Detection of epileptic seizures using chaotic and statistical features in the EMD domain

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

28 Scopus citations

Abstract

An artificial neural network (ANN)-based method, using a combination of statistical and chaotic features, is proposed to discriminate electroencephalogram (EEG) signals for seizure detection. The EEG signals are subjected to empirical mode decomposition, generating intrinsic mode functions. Statistical and chaotic features such as skewness, kurtosis, variance, and largest Lyapunov exponent, correlation dimension and approximate entropy are extracted from these modes and fed to the ANN to classify the EEG signals. It is shown that the proposed method can achieve up to 100% accuracy as compared to several state-of-the-art techniques in discriminating the seizure signals from the non-seizure ones.

Original languageEnglish
Title of host publicationProceedings - 2011 Annual IEEE India Conference
Subtitle of host publicationEngineering Sustainable Solutions, INDICON-2011
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011 - Hyderabad, India
Duration: Dec 16 2011Dec 18 2011

Publication series

NameProceedings - 2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011

Conference

Conference2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011
Country/TerritoryIndia
CityHyderabad
Period12/16/1112/18/11

Keywords

  • Electro-encephalogram (EEG)
  • chaotic analysis
  • empirical mode decomposition (EMD)
  • epileptic seizures
  • statistical analysis

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