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
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2012.10.008

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 participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients.