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

Larbi Boubchir, Somaya Al-Maadeed, Ahmed Bouridane

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

17 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2014 26th International Conference on Microelectronics (ICM)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages32-35
ISBN (Print)978-1-4799-8153-3
DOIs
Publication statusPublished - Dec 2014
Event26th International Conference on Microelectronics - Doha
Duration: 1 Dec 2014 → …

Conference

Conference26th International Conference on Microelectronics
Period1/12/14 → …

Fingerprint Dive into the research topics of 'Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data'. Together they form a unique fingerprint.

Cite this