A comparison of PCA, ICA and LDA in EEG signal classification using SVM

被引：0
|
作者：
Guersoy, M. Ismail [1 ]
Subasi, Abduelhamit [1 ]
机构：
[1] Kahramanmaras Sutcu Imam Univ, Kahramanmaras, Turkey
来源：
2008 IEEE 16TH SIGNAL PROCESSING, COMMUNICATION AND APPLICATIONS CONFERENCE, VOLS 1 AND 2 | 2008年
关键词：
EEG; Epileptic seizure; Autoregressive method (AR); Independent Component Analysis (ICA); Principal Component Analysis (PCA); Linear Discriminant Analysis (LDA); Support Vector Machine (SVM);
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Since EEG is one of the most important sources of information in diagnosis of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a method for classifying epilepsy of full spectrum EEG recordings. In the proposed method, autoregressive (AR) model is used to acquire power spectrum of EEG signals, then dimension of the extracted feature vectors is reduced by using ICA, PCA and LDA, and these vectors used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. It is observed that, SVM classification of EEG signals gives better results and these results can also be used for diagnosis of diseases.
引用
收藏
页码：853 / 856
页数：4
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