Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images
Author | Boubchir L. |
Author | Al-Maadeed, Somaya |
Author | Bouridane A. |
Author | Cherif A.A. |
Available date | 2022-05-19T10:23:12Z |
Publication Date | 2015 |
Publication Name | Proceedings - International Conference on Image Processing, ICIP |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/ICIP.2015.7351507 |
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. |
Language | en |
Publisher | IEEE Computer Society |
Subject | EEG LBP descriptor seizure detection time-frequency feature extraction Time-frequency image |
Type | Conference |
Pagination | 3758-3762 |
Volume Number | 2015-December |
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Computer Science & Engineering [2427 items ]