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AuthorBoubchir, Larbi
AuthorAl-Maadeed, Somaya
AuthorBouridane, Ahmed
Available date2016-04-21T14:44:36Z
Publication Date2014
Publication NameProceedings of the International Conference on Microelectronics, ICM
CitationL. Boubchir, S. Al-Maadeed and A. Bouridane, "Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data," 2014 26th International Conference on Microelectronics (ICM), Doha, 2014, pp. 32-35.
ISSN2159-1660
URIhttp://dx.doi.org/10.1109/ICM.2014.7071799
URIhttp://hdl.handle.net/10576/4424
AbstractThis 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.
SponsorNational Priority Research Program (NPRP No.09-864-1-128) grant.
Languageen
PublisherIEEE
Subjectclassification
SubjectEEG
Subjectepileptic seizure detection
Subjectfeature extraction
SubjectHaralick features
Subjecttime-frequency image
TitleHaralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data
TypeConference Paper
Pagination32-35


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