文献类型：期刊文献

英文题名：Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

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

机构：[1]E China Univ Sci & Technol, Inst Neurodynam, Sch Informat Sci & Engn, Sch Sci, Shanghai 200237, Peoples R China;[2]Shanghai Normal Univ, Dept Elect Engn, Shanghai, Peoples R China

年份：2010

卷号：4

期号：3

起止页码：233

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000280782500008)】；

基金：Projects (10872068, 10672057) supported by National Natural Science Foundation of China (NSFC).

语种：英文

外文关键词：EEG; Principal component analysis; Factor analysis; Epileptiform activity; Discrete wavelet transform; Approximate entropy

摘要：This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman-Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.