One Dimensional Median Local Binary Pattern Based Feature Extraction For Classifying Epileptic EEG Signals
Journal Title: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji - Year 2017, Vol 5, Issue 3
Abstract
Electroencephalogram is an important data source that widely used in detecting epilepsy. In this study, EEG records consisting of five markers A, B, C, D, E that obtained from the database of Epilogy of Bonn University Epileptology Department was used. The feature vectors that obtained by applying the one dimension median local binary pattern (1D-MLBP) method were classified by using k nearest neighbor (kNN) algorithm The classification performance related to 1D-MLBP method developed was evaluated as an attribute. For this classification, the performance criteria was evaluated by calculating the confusion matrix. In this study,the classification performance for the A-E data sets was found to be 100.0%, 99.00% for the A-D data sets, 98.00% for the D-E data sets, 99.50% for the E-CD data sets and 96.00% for the A-D-E data sets. It has been seen that 1D-MLBP method used in the study gives better results than many methods used in the literature.
Authors and Affiliations
Ömer Türk, Mehmet Siraç Özerdem
Keywords
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