Classification of seizure and seizure-free EEG signals using local binary patterns

Abstract

Local binary pattern (LBP) is a texture descriptor that has been proven to be quite effective for various image analysis tasks in image processing. In this paper one-dimensional local binary pattern (1D-LBP) based features are used for classification of seizure and seizure-free electroencephalogram (EEG) signals. The proposed method employs a bank of Gabor filters for processing the EEG signals. The processed EEG signal is divided into smaller segments and histograms of 1D-LBPs of these segments are computed. Nearest neighbor classifier utilizes the histogram matching scores to determine whether the acquired EEG signal belongs to seizure or seizure-free category. Experimental results on publicly available database suggest that the proposed features effectively characterize local variations and are useful for classification of seizure and seizure-free EEG signals with a classification accuracy of 98.33%. This result demonstrates the superiority of our approach for classification of seizure and seizure-free EEG signals over recently proposed approaches in the literature.