Title :
Classifying depression patients and normal subjects using machine learning techniques
Author :
Hosseinifard, Behshad ; Moradi, Mohammad Hassan ; Rostami, Reza
Author_Institution :
Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
Depression is one of the most common mental disorder that at its worst can lead to suicide. Diagnosing depression in the early curable stage is very important. In this paper we study performance of different classification techniques for classifying depression patients from normal subjects. For this aim, power spectrum of three frequency band (alpha, beta, theta) and the whole bands of EEG are used as features. We have shown that Support Vector Machine (SVM) classifier using Genetic algorithm for feature selection can achieve accuracy of 88.6% on classifying depression patients.
Keywords :
diseases; electroencephalography; feature extraction; genetic algorithms; learning (artificial intelligence); medical signal processing; neurophysiology; psychology; signal classification; support vector machines; EEG; SVM classifier; alpha frequency power spectrum; beta frequency power spectrum; depressed patients; depression classification; depression diagnosis; feature selection; genetic algorithm; machine learning; mental disorder; support vector machine; theta frequency power spectrum; Accuracy; Band pass filters; Electroencephalography; Genetic algorithms; Logistics; Support vector machines; Training data; Depression; EEG; Linear discriminant analysis; Power Spectrum; Support Vector Machine; genetic algorithm;
Conference_Titel :
Electrical Engineering (ICEE), 2011 19th Iranian Conference on
Conference_Location :
Tehran
Print_ISBN :
978-1-4577-0730-8
Electronic_ISBN :
978-964-463-428-4
