Feature extraction based on sparse representation with application to epileptic EEG classification

Epilepsy seizure detection in electroencephalogram (EEG) is a major issue in the diagnosis of epilepsy, and it can be considered as a classification problem. Considering the particular property of EEG, which is sparse in Garbor dictionary, a feature extraction method based on sparse representation has been applied to epilepsy detection. To improve classification accuracy, in this article, a novel feature vector is developed, which not only can reflect the main structure, but also can give expression to the relation between main structure and residual information. Classification accuracy, efficiency, and robustness to noise of the new feature are explored and analyzed with publicly available data set. It is demonstrated by experiments that the classification accuracy and the efficiency are simultaneously enhanced with this new feature extraction method, and that the novel classification feature proposed in this work greatly improves the classification performance of epilepsy detection. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 104113, 2013Engineering, Electrical & ElectronicOpticsImaging Science & Photographic TechnologySCI(E)EI0ARTICLE2104-1132