Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM)

Author

Ming-Hsien Hiesh ; Lam, Yan-Yu Andy ; Chia-Ping Shen ; Wei Chen ; Feng-Shen Lin ; Hsiao-Ya Sung ; Jeng-Wei Lin ; Ming-Jang Chiu ; Feipei Lai

Author_Institution

Dept. of Psychiatry, Nat. Taiwan Univ. Hosp., Taipei, Taiwan

fYear

2013

fDate

3-7 July 2013

Firstpage

6047

Lastpage

6050

Abstract

Recently, Event-Related Potential (ERP) has being the most popular method in evaluating brain waves of schizophrenia patients. ERP is one of the electroencephalography (EEG), which is measured the change of brain waves after giving patients certain stimulations instead of resting state. However, with traditional statistical analysis method, both P50 and MMN showed significant difference between controls and patients but not in Gamma band. Gamma band is a 30-50 Hz auditory stimulation which had been suggested may be abnormal in schizophrenia patients. Our data are recruited from 5 schizophrenia patients and 5 controls in National Taiwan University Hospital have been tested with this platform. The results showed that detection rate is 88.24% and we also analyzed the importance of features, including Standard Deviation (SD) and Total Variation (TotalVar) in different stage of wavelet transform. Therefore, this proposed methodology could serve as a valuable clinical decision support for physiologists in evaluating schizophrenia.

Keywords

auditory evoked potentials; electroencephalography; feature extraction; genetic algorithms; medical disorders; medical signal detection; medical signal processing; neurophysiology; signal classification; statistical analysis; support vector machines; wavelet transforms; MMN method; National Taiwan University Hospital; P50 method; auditory stimulation; brain wave evaluation; clinical decision support; detection rate; electroencephalography; event-related potential; feature extraction; frequency 30 Hz to 50 Hz; gamma band; genetic algorithm; physiologist; schizophrenia patient classification; standard deviation; statistical analysis method; support vector machine; total variation; wavelet transform; Accuracy; Electroencephalography; Feature extraction; Genetic algorithms; Standards; Support vector machines; Synchronization;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6610931

Filename

6610931