文献类型：期刊文献

英文题名：Classifying detection of epileptic EEG based on approximate entropy in wavelet domain

作者：Wang, Chun-Mei[1,2]; Zou, Jun-Zhong[1]; Zhang, Jian[1]; Zhang, Zhi-Suo[3]; Zhang, Chong-Ming[2]

机构：[1] Department of Automation, East China University of Science and Technology, Shanghai, China; [2] Department of Electronic Engineering, Shanghai Normal University, Shanghai, China; [3] Changhai Hospital, Second Military Medical University, Shanghai, China

年份：2009

外文期刊名：Proceedings of the 2009 2nd International Conference on Biomedical Engineering and Informatics, BMEI 2009

收录：EI(收录号：20100312643840)

语种：英文

外文关键词：Entropy - Discrete wavelet transforms - Complex networks - Signal reconstruction

摘要：In the analysis of epileptic EEG data, the typical presence of epileptic activity includes spike wave, sharp wave, spike-and-slow complex wave and sharp-and-slow complex wave. Each of these epileptic EEG has different time-frequency characteristics. If they are detected by identical detection rule, it is impossible to obtain optimal detection result. In this paper, we present a classifying detection method to automatically detect different kinds of epileptic EEG data using the discrete wavelet transform (DWT) combined with approximate entropy (ApEn). Spike wave, spike-and-slow complex wave and sharp-and-slow complex wave are detected by this method and the optimal detection rules are achieved. And it assures a higher detection rate with a lower false detection rate. ?2009 IEEE.