Automatic depression discrimination on FNIRS by using general linear model and SVM

Author

Hong Song ; Weilong Du ; Xin Yu ; Wentian Dong ; Wenxiang Quan ; Weimin Dang ; Huijun Zhang ; Ju Tian ; Tianhang Zhou

Author_Institution

Sch. of software, Beijing Inst. of Technol., Beijing, China

fYear

2014

fDate

14-16 Oct. 2014

Firstpage

278

Lastpage

282

Abstract

A method is proposed to distinguish patients with depression from healthy persons using data measured by Functional Near Infrared Spectroscopy (FNIRS) during a cognitive task. Firstly, General Linear Model (GLM) is used to extract features from 52-channel FNIRS data of patients with depression and normal healthy persons. Then a Support Vector Machine (SVM) classifier is designed for classification. The results of experiment show that the method can achieve a satisfactory classification with the accuracy 89.71% for total and 92.59% for patients. Also, the results suggest that FNIRS is a promising clinical technique in the diagnosis and therapy of depression.

Keywords

cognition; diseases; feature extraction; infrared spectra; support vector machines; automatic depression discrimination; cognitive task; feature extraction; fifty two-channel FNIRS data; functional near infrared spectroscopy; general linear model; support vector machine classifier; Accuracy; Band-pass filters; Educational institutions; Feature extraction; Support vector machines; Testing; Training; Depression Discrimination; FNIRS; GLM; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4799-5837-5

Type

conf

DOI

10.1109/BMEI.2014.7002785

Filename

7002785