文献类型：期刊文献

英文题名：Automatic detection of epileptic Sharp-slow by wavelet and approximate entropy

作者：Wang, Chunmei[1]; Zou, Junzhong[1]; Zhang, Jian[1]; Zhang, Zhisuo[2]

机构：[1] Department of Automation, East China University of Science and Technology, Shanghai 200237, China; [2] Changhai Hospital, Second Military Medical University, Shanghai 200433, China

年份：2009

起止页码：1269

外文期刊名：2009 IEEE International Conference on Information and Automation, ICIA 2009

收录：EI(收录号：20094812499139)

语种：英文

外文关键词：Signal analysis - Optimal detection

摘要：The automatic epileptiform activities detection in EEG is significant in clinical application. Epileptic sharp-slow complex wave is one of typical presence of epileptiform activities, which has different time-frequency property compared with spike and spike-slow complex wave. A new scheme is presented for detecting epileptic sharp-slow wave in 8-channel EEG data from normal subjects and epileptic patients. The scheme is based on the characteristic of a multi-resolution and approximate entropy (ApEn) analysis of EEG signals. The EEG signals on each channel are decomposed into three levels using multi-resolution wavelet analysis, and then ApEn values of the detail coefficients are computed. Distinct differences are found between the ApEn values of the epileptic sharp-slow and the normal EEG. The EEG signals are detected by Neyman-Pearson (NP) criteria. The optimal detection rule of detecting sharp-slow is achieved, and it assures a higher detection rate with a lower false detection rate. ? 2009 IEEE.