会议专题

Application of Eigenvector Estimation and SVM for EEG Signals Classification

Objective: To realize the automatic classification between melancholic and healthy persons by extracting the disease features from the melancholics EEG signals. Methods: 1. Extracting the features from the EEG signals of melancholic and healthy persons; 2. Obtaining the characteristic parameters such as the maximum, minimum, mean and standard deviation of EEG power spectrum amplitude: 3. Training the classifier and realizing the classification based on Support Vector Machines; 4. Test and validation. Results: The present classifier, which uses power spectrum characteristic parameters extracted by eigenvector methods as classification features, has better classification accuracy comparing with the one which uses frequency feature parameters extracted by wavelet methods as classification features. It achieves the classification accuracy of 95.6%. Conclusion: This paper presented a new method for melancholia diagnose.

Eigenvector Estimation Classification SVM.

Enping Lou Sheng Zhang Shini Qiao

College of Mathematics,Physics and Information Engineering,Zhejiang Normal University,Jinhua China,321004

国际会议

2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

北京

英文

3410-3413

2009-08-16(万方平台首次上网日期,不代表论文的发表时间)