Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BMEI.2014.7002785 | BMEI |
Keywords | Field | DocType |
cognition,fnirs,diseases,functional near infrared spectroscopy,automatic depression discrimination,svm,general linear model,support vector machine classifier,fifty two-channel fnirs data,infrared spectra,cognitive task,feature extraction,depression discrimination,glm,support vector machines,testing,band pass filters,accuracy | Pattern recognition,General linear model,Computer science,Support vector machine,Feature extraction,Speech recognition,Functional near-infrared spectroscopy,Artificial intelligence,Classifier (linguistics),Cognition | Conference |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Song | 1 | 8 | 8.34 |
| Weilong Du | 2 | 1 | 0.36 |
| Xin Yu | 3 | 1 | 0.36 |
| Wentian Dong | 4 | 2 | 1.08 |
| Wenxiang Quan | 5 | 2 | 1.08 |
| Weimin Dang | 6 | 2 | 1.08 |
| Huijun Zhang | 7 | 5 | 4.12 |
| Ju Tian | 8 | 1 | 0.36 |
| Tianhang Zhou | 9 | 1 | 0.36 |