Time-frequency texture descriptors of EEG signals for efficient detection of epileptic seizure

Detection of epileptic seizure in electroencephalogram (EEG) signals is a challenging task and requires highly skilled neurophysiologists. Therefore, computer-aided detection helps neurophysiologist in interpreting the EEG. In this paper, texture representation of the time-frequency (t-f) image-based epileptic seizure detection is proposed. More specifically, we propose texture descriptor-based features to discriminate normal and epileptic seizure in t-f domain. To this end, three popular texture descriptors are employed, namely gray-level co-occurrence matrix (GLCM), texture feature coding method (TFCM), and local binary pattern (LBP). The features that are obtained on the GLCM are contrast, correlation, energy, and homogeneity. Moreover, in the TFCM method, several statistical features are calculated. In addition, for the LBP, the histogram is used as a feature. In the classification stage, a support vector machine classifier is employed. We evaluate our proposal with extensive experiments. According to the evaluated terms, our method produces successful results. 100 % accuracy is obtained with LIBLINEAR. We also compare our method with other published methods and the results show the superiority of our proposed method.