One-dimensional Center Symmetric Local Binary Pattern Based Epilepsy Detection Method

Abstract

The diagnosis of epilepsy from the EEG signals is determined by the visual/manual evaluation performed by the neurologist. This evaluation process is laborious and evaluation results vary according to the experience level of neurologists. Therefore, automated systems that will be created using advanced signal processing techniques are important for diagnosis. In this study, a new feature extraction method is proposed using multiple kernel based one-dimensional center symmetric local binary pattern (1D-CSLBP) to identify epileptic seizures. To strengthen this method, levels have been created and multi-level feature extraction has been carried out. Discrete wavelet transform (DWT) was used to generate the levels and feature extraction was performed using the low pass filter coefficient (L bands) obtained at each level. Neighborhood component analysis (NCA) was used to select the most distinctive features. The obtained features are classified using the nearest neighbors (kNN) algorithm. A high performance method was obtained by using multiple kernel NCA and NCA. The 1D-CSLBP and NCA-based method has reached 100.0% accuracy in A-E, A-D-E, D-E, C-E situations.

Keywords

• Feature extraction
• local feature generation
• feature selection
• classification

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