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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Veröffentlicht in:Computer methods and programs in biomedicine Jg. 109; H. 3; S. 339 - 345
Hauptverfasser: Hosseinifard, Behshad, Moradi, Mohammad Hassan, Rostami, Reza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ireland Ltd 01.03.2013
Elsevier
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2012.10.008