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

• 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

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...