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.

中文翻译：

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脑电信号的时频纹理描述符，用于有效检测癫痫发作。

脑电图（EEG）信号中癫痫性癫痫发作的检测是一项艰巨的任务，需要熟练的神经生理学家。因此，计算机辅助检测有助于神经生理学家解释脑电图。在本文中，提出了基于时频（tf）图像的癫痫发作检测的纹理表示。更具体地说，我们提出了基于纹理描述符的特征，以区分tf域中的正常发作和癫痫发作。为此，采用了三种流行的纹理描述符，即灰度共生矩阵（GLCM），纹理特征编码方法（TFCM）和局部二进制模式（LBP）。在GLCM上获得的特征是对比度，相关性，能量和同质性。此外，在TFCM方法中，计算了几个统计特征。此外，对于LBP，直方图用作特征。在分类阶段，采用支持向量机分类器。我们通过广泛的实验来评估我们的建议。根据评估的术语，我们的方法产生了成功的结果。使用LIBLINEAR可获得100％的精度。我们还将我们的方法与其他已发表的方法进行了比较，结果表明了我们提出的方法的优越性。