Approximate entropy and support vector machines for electroencephalogram signal classification*****
Zhen Zhang
Yi Zhou
Ziyi Chen
Xianghua Tian
Shouhong Du
Ruimei Huang
摘要：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 epi-leptic seizures were included in this study. They were al diagnosed with neocortex localized epi-lepsy 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 con-structed with the approximate entropy extracted from one epileptic case, and then electroence-phalogram waves of the other three cases were classified, reaching a 93.33%accuracy rate. Our findings suggest that the use of approximate entropy al ows 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.
机标关键词：signal classificationapproximate entropygeneralization abilitysupport vector machinesnonlinear dynamicsidentification of
资助基金：This study was financial y supported by the National Natural Science Foundation of China, (No.61263011,81000554)Program in Sun Yat-sen University supported by Fundamental Research Funds for the Central Universities, (No.11ykpy07)Natural Science Foundation of Guangdong Province, (No. S2011010005309)Innovation Fund of Xinjiang Medical University, (No. XJC201209)
论文发表日期：2013-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 1844-1852 )
英文信息