Anxiety and Depression Detection using Statistical Features
Publish place: 2rd International Conference on Soft Computing
Publish Year: 1396
Type: Conference paper
Language: English
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Document National Code:
CSCG02_154
Index date: 26 February 2018
Anxiety and Depression Detection using Statistical Features abstract
Human action is caused by the neuron activities. The distributed signals from throughout the scalp, due by these activities,can be recorded and analyzed subsequently. Concerning, receiving and recording brain signals can be performed by Electroencephalogram (EEG) recorder. The objective of the present study is to detect the anxiety and depression disorders using EEG signals. In order to get this purpose, some statistical features are extracted using wavelet coefficients in timefrequency domain. Experimental results using 50 subjects is achieved by 96 percent of accuracy to detect disorder
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Anxiety and Depression Detection using Statistical Features authors
Tahereh Najafi
Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan
Babak Abad Fomani
Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan
Asadollah Shahbahrami
Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan