Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data

Boubchir, Larbi, Al-Maadeed, Somaya and Bouridane, Ahmed (2014) Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data. In: 2014 26th International Conference on Microelectronics (ICM). IEEE, Piscataway, NJ, pp. 32-35. ISBN 978-1-4799-8153-3

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Abstract

This paper presents novel time-frequency (t-f) features based on t-f image descriptors for the automatic detection and classification of epileptic seizure activities in EEG data. Most previous methods were based only on signal-related features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands. The proposed features are extracted from the t-f representation of EEG signals which are processed as a textured image using Haralick's texture descriptors. The proposed descriptors are capable to describe visually the epileptic seizure patterns observed in t-f image of EEG signals. The results obtained on real EEG data show that the use of the proposed features improves significantly the performance of the EEG seizure detection and classification by achieving a total classification accuracy up to 99% for 140 EEG segments using one-againt-one SVM classifier.

Item Type:	Book Section
Uncontrolled Keywords:	EEG, Haralick features, classification, epileptic seizure detection, feature extraction, time-frequency image
Subjects:	B900 Others in Subjects allied to Medicine G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	15 Sep 2015 09:26
Last Modified:	12 Oct 2019 22:29
URI:	http://nrl.northumbria.ac.uk/id/eprint/23750

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