@inproceedings{c843a8402f224cd4bbec5b30f2f1bfff,
title = "Automatic depression discrimination on FNIRS by using fastICA/WPD and SVM",
abstract = "A method is proposed for distinguishing patients with depression from normal controls based on data measured by FNIRS during a cognitive task. First, Fast Independent Component Analysis (FastICA) and Wavelet Package Decomposition (WPD) are used to extract features from 52-channel Functional Near- Infrared Spectroscopy (FNIRS) data of patients with depression and normal healthy persons. Then a classifier based on Support Vector Machine (SVM) is designed for classification. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy 86.7647% for total and 90.74% for patients. Also, the results suggested that FNIRS may be a promising clinical tool in the diagnosis and treatment of psychiatric disorders.",
keywords = "Depression discrimination, FNIRS, FastICA, SVM, WPD",
author = "Hong Song and Weilong Du and Qingjie Zhao",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; Chinese Intelligent Automation Conference, 2015 ; Conference date: 01-01-2015",
year = "2015",
doi = "10.1007/978-3-662-46469-4_27",
language = "English",
isbn = "9783662464687",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "257--265",
editor = "Zhidong Deng and Hongbo Li",
booktitle = "Proceedings of the 2015 Chinese Intelligent Automation Conference - Intelligent Information Processing",
address = "Germany",
}