Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images

Larbi Boubchir, Somaya Al-Maadeed, Ahmed Bouridane, Arab Ali Cherif

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Citations (Scopus)

Abstract

This paper presents novel time-frequency (t-f) feature extraction approach for the classification of EEG signals for Epileptic seizure activities detection. The proposed features are based on Local Binary Patterns (LBP) descriptor extracted from t-f representation of EEG signals processed as a textured image. Compared to most previous t-f approaches were based only on features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands, the proposed t-f features are capable to describe visually the epileptic seizure activity patterns observed in t-f image of EEG signals. The results obtained on real EEG data show that the use of t-f LBP descriptor-based features achieve an overall classification accuracy up to 99% for 150 EEG signals using 2-class SVM classifier. This is confirmed by ROC curve analysis.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3758-3762
ISBN (Print)978-1-4799-8339-1
DOIs
Publication statusPublished - 27 Sep 2015

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