Discriminative features for interictal epileptic discharges in intracerebral EEG signals

Cheechian Cheng, Yang Bai, Jie Cheng, Hamid Soltanian-Zadeh, Qiang Cheng

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

1 Scopus citations

Abstract

This paper extracts features and selects the most discriminate feature subset for classifying interictal epileptic discharge periods (IED) from non-IED periods in intracerebral EEG (iEEG) signals. Generalized autoregressive conditional heteroscedasticity (GARCH) model based on the student t-distribution is used to describe the wavelet coefficients of the iEEG signals. A variety of features are extracted from the coefficients of GARCH models. The Markov random field (MRF) based feature subset selection method is used to select the most discriminative features. Experimental results on real patients' data validate the effectiveness of the selected features.

Original languageEnglish
Title of host publication2012 5th International Congress on Image and Signal Processing, CISP 2012
Pages1791-1795
Number of pages5
DOIs
StatePublished - 2012
Event2012 5th International Congress on Image and Signal Processing, CISP 2012 - Chongqing, China
Duration: Oct 16 2012Oct 18 2012

Publication series

Name2012 5th International Congress on Image and Signal Processing, CISP 2012

Conference

Conference2012 5th International Congress on Image and Signal Processing, CISP 2012
Country/TerritoryChina
CityChongqing
Period10/16/1210/18/12

Keywords

  • EEG
  • Feature selection
  • IED
  • MRF
  • classification
  • student t-distribution based GARCH model

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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