Application of Eigenvector Estimation and SVM for EEG Signals Classification

摘要：

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.

关键词： Eigenvector Estimation Classification SVM.

作者: Enping Lou Sheng Zhang Shini Qiao

作者单位: College of Mathematics,Physics and Information Engineering,Zhejiang Normal University,Jinhua China,321004

会议类型: 国际会议

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

会议地点: 北京

会议语种:英文

页码: 3410-3413

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）