title: Approximate entropy and support vector machines for electroencephalogram signal classification.
abstract: The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index-approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
authors.affiliation: Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Department of Neurology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China.
authors.forename: Ziyi
authors.initials: Z
authors.lastname: Chen
authors.affiliation: College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China.
authors.forename: Xianghua
authors.initials: X
authors.lastname: Tian
authors.affiliation: College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China.
authors.forename: Shouhong
authors.initials: S
authors.lastname: Du
authors.affiliation: Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China.
authors.forename: Ruimei
authors.initials: R
authors.lastname: Huang
keywords: approximate entropy; automatic real-time detection; brain injury; classification; electroencephalogram; epilepsy; generalization; grants-supported paper; neural regeneration; neuroregeneration; nonlinear dynamics; support vector machine