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

Boubchir, Larbi, Al-Maadeed, Somaya, Bouridane, Ahmed and Cherif, Arab Ali (2015) Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images. In: 2015 IEEE International Conference on Image Processing (ICIP). IEEE, Piscataway, NJ, pp. 3758-3762. ISBN 978-1-4799-8339-1

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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.

Item Type:	Book Section
Uncontrolled Keywords:	EEG, LBP descriptor, Time-frequency image, seizure detection, time-frequency feature extraction
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	17 Feb 2016 12:17
Last Modified:	12 Oct 2019 22:29
URI:	http://nrl.northumbria.ac.uk/id/eprint/26046

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