Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal

Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were part...

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Bibliographic Details
Published in:	Computer methods and programs in biomedicine Vol. 109; no. 3; pp. 339 - 345
Main Authors:	Hosseinifard, Behshad, Moradi, Mohammad Hassan, Rostami, Reza
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ireland Ltd 01.03.2013 Elsevier
Subjects:	Adult Algorithms Artificial Intelligence Biological and medical sciences Brain Mapping - methods Computer Simulation Correlation dimension Depression Depression - diagnosis Depression - physiopathology Detrended fluctuation analysis Discriminant Analysis EEG Electroencephalography - methods Female Fractals Higuchi fractal Humans Internal Medicine Least-Squares Analysis Logistic Models Lyapunov exponent Male Medical sciences Middle Aged Models, Statistical Other Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects) Reproducibility of Results Signal Processing, Computer-Assisted Software Technology. Biomaterials. Equipments. Material. Instrumentation Detrended fluctuation analysis Depression Higuchi fractal Correlation dimension EEG Lyapunov exponent Electroencephalography Biomedical engineering
ISSN:	0169-2607, 1872-7565, 1872-7565
Online Access:	Get full text
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