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Anxiety and Depression Detection using Statistical Features

عنوان مقاله: Anxiety and Depression Detection using Statistical Features
شناسه ملی مقاله: CSCG02_154
منتشر شده در دومین کنفرانس ملی محاسبات نرم در سال 1396
مشخصات نویسندگان مقاله:

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

خلاصه مقاله:
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

کلمات کلیدی:
Anxiety; Depression; EEG; Wavelet coefficients; Statistical features; SVM

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/696783/