Feature Extraction of Electroencephalogram Signals Applied to Epilepsy

摘要：

　　In this work,we proposed an analysis framework for Electroencephalogram (EEG) signals and their classification.The EEGs considered for this study belong to both normal as well as epileptic subjects.After wavelet packet decomposition of EEG signals,three important statistical features such as standard deviation,energy and entropy were computed at different sub-bands decomposition.The most suitable wavelets were selected for processing EEG signals.Linear discriminant analysis and principal component analysis are used to reduce the dimension of data.Feature vectors were used to model and train the efficient Support Vector Machine (SVM) classifier.In this study,we have attempted to improve the computing efficiency as it selects the statistical features and the dimensionality reduction method that can provide an important assistant to neuro-physicians,thus to make their decision on their patients.

关键词： EEG wavelet coefficients WPT Feature Extraction

作者: A.Bousbia-Salah A.Mesbah H.Bousbia-Salah

作者单位: University of Sciences and Technology Houari Boumediene USTHBBP32, Bab-Ezzouar, El-alia, 16111 Algie Ecole Nationale Polytechnique, Avenue Hassen Badi ,El Harrach,16200,Algiers,Algeria

会议类型: 国际会议

会议名称: 2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)

会议地点: 北京

会议语种:英文

页码: 1624-1628

在线出版日期: 2012-10-21（万方平台首次上网日期，不代表论文的发表时间）