Comput Methods Programs Biomed - Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
{ studi(1410) differ(1259) use(1210) }
{ method(2212) result(1239) propos(1039) }
{ treatment(1704) effect(941) patient(846) }
{ model(2341) predict(2261) use(1141) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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.

Resumo Limpo

diagnos depress earli curabl stage import may even save life patient paper studi nonlinear analysi eeg signal discrimin depress patient normal control fortyf unmed depress patient normal subject particip studi power four eeg band four nonlinear featur includ detrend fluctuat analysi dfa higuchi fractal correl dimens lyapunov expon extract eeg signal discrimin two group knearest neighbor linear discrimin analysi logist regress classifi use highest classif accuraci obtain correl dimens lr classifi among nonlinear featur improv nonlinear featur combin appli classifi classif accuraci achiev nonlinear featur lr classifi experi genet algorithm employ select import featur propos techniqu compar contrast report method demonstr combin nonlinear featur perform enhanc studi show nonlinear analysi eeg can use method discrimin depress patient normal subject suggest analysi may complementari tool help psychiatrist diagnos depress patient

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