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2016 | OriginalPaper | Buchkapitel

Robust Epileptic Seizure Classification

verfasst von : Farrikh Alzami, Daxing Wang, Zhiwen Yu, Jane You, Hau-San Wong, Guoqiang Han

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

A lot of feature vectors and sub-band signals are considered for Epileptic seizure classification. Unfortunately, not all the feature vectors and sub-band signals contribute to the final result. In view of this limitation, we propose a modified Differential Evolution Feature Selection algorithm (MDEFS), which searches the best feature vector subset and the sub-band signals to distinguish three groups of subjects (healthy, ictal and interictal). From the experiment results, it is observed that the bagging method based on the optimal feature subset (the standard deviation attribute in the delta sub-band signal, the time-lag attribute in the delta sub-band signal, fractal dimension in the alpha sub-band signal, the correlation dimension attribute in the alpha sub-band signal and the standard deviation attribute in the beta sub-band signal) selected by MDEFS results in highest classification accuracy of 98.67 %.

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Titel: Robust Epileptic Seizure Classification
verfasst von: Farrikh Alzami
Daxing Wang
Zhiwen Yu
Jane You
Hau-San Wong
Guoqiang Han
Verlag: Springer International Publishing
Buch: Intelligent Computing Theories and Application
Print ISBN: 978-3-319-42293-0

Electronic ISBN: 978-3-319-42294-7

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-42294-7_32

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