DocumentCode :
3541228
Title :
Application of eigenvector estimation and SVM for EEG Signals Classification
Author :
Lou, En Ping ; Zhang, Sheng ; Qiao, Shini
Author_Institution :
Coll. of Math., Phys. & Inf. Eng., Zhejiang Normal Univ., Jinhua, China
fYear :
2009
fDate :
16-19 Aug. 2009
Abstract :
Objective: To realize the automatic classification between melancholic and healthy persons by extracting the disease features from the melancholic´s 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.
Keywords :
eigenvalues and eigenfunctions; electroencephalography; feature extraction; medical signal processing; signal classification; support vector machines; EEG power spectrum amplitude; disease feature extraction; eigenvector estimation; electroencephalogram signals classification; power spectrum characteristic; support vector machine; Data mining; Diseases; Electroencephalography; Feature extraction; Frequency; Pattern classification; Spectral analysis; Support vector machine classification; Support vector machines; White noise; Classification; Eigenvector Estimation; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-3863-1
Electronic_ISBN :
978-1-4244-3864-8
Type :
conf
DOI :
10.1109/ICEMI.2009.5274101
Filename :
5274101
Link To Document :
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