Identification of Epileptic EEG Signals based on the Tunable Q-factor Wavelet Transform
HE Wangpeng
YANG Lin
WANG Fang
HUANG Shaoping
Abstract：For the problem of identifying and classifying epileptic EEG signals , we proposed an effective technique based on the tunable Q-factor wavelet transform ( TQWT) .Firstly, the TQWT was employed to decompose EEG signal into several wavelet subba-nds.Then, according to the frequency band of epileptic abnormal waves , the EEG signal was reconstructed adaptively via correspond-ing TQWT wavelet subbands .The root mean square value and peak -to-peak value indicators were calculated as feature vector .Fi-nally, the support vector machine (SVM) was introduced for classification.Moreover, the proposed method was applied to analyze real EEG data collected from the Epilepsy Research Center , University of Bonn, Germany.The results demonstrate that the proposed tech-nique can effectively detect epilepsy disease from EEG signals with good classification accuracy of 98%.
Keywords：Epileptic EEGTunable Q-factor wavelet transformSupport vector mahine -fator waveler transformFeature ex-tractionClassification
Publication Date：2017-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：5( 346-350 )