caa a22 4500 44529485X CHVBK 20180317142538.0 cr unu---uuuuu 170323e20100901xx s 000 0 eng 10.1007/s11571-010-9120-2 doi (NATIONALLICENCE)springer-10.1007/s11571-010-9120-2 Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn [Elektronische Daten] [Chunmei Wang, Junzhong Zou, Jian Zhang, Min Wang, Rubin Wang] 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. Springer Science+Business Media B.V., 2010 EEG nationallicence Principal component analysis nationallicence Factor analysis nationallicence Epileptiform activity nationallicence Discrete wavelet transform nationallicence Approximate entropy nationallicence Wang Chunmei Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut Zou Junzhong Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut Zhang Jian Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut Wang Min Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut Wang Rubin Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut Cognitive Neurodynamics Springer Netherlands 4/3(2010-09-01), 233-240 1871-4080 4:3<233 2010 4 11571 https://doi.org/10.1007/s11571-010-9120-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11571-010-9120-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Chunmei Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut NATIONALLICENCE 700 1- Zou Junzhong Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut NATIONALLICENCE 700 1- Zhang Jian Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut NATIONALLICENCE 700 1- Wang Min Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut NATIONALLICENCE 700 1- Wang Rubin Institute for Neurodynamics, School of Information Science and Engineering, School of Science, East China University of Science and Technology, Shanghai, China aut NATIONALLICENCE 773 0- Cognitive Neurodynamics Springer Netherlands 4/3(2010-09-01), 233-240 1871-4080 4:3<233 2010 4 11571 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer