EEG classification of adolescents with type I and type II of bipolar disorder.

Abstract:

:Bipolar disorder (BD) is a severe psychiatric disorder and has two common types: type I and type II. Early diagnosis of the subtypes is very challenging particularly in adolescence. In this study, 38 adolescents are participated including 18 patients with BD I and 20 patients with BD II. The electroencephalogram signal is recorded by 19 electrodes in open eyes at resting state. After preprocessing, the state of the art methods from various domains are implemented to provide a good feature set for classifying the two groups. In order to improve the classification accuracy, four different feature selection methods named mutual information maximization (MIM), conditional mutual information maximization (CMIM), fast correlation based filter (FCBF), and double input symmetrical relevance (DISR) are applied to select the most informative features. Multilayer perceptron (MLP) neural network with a hidden layer containing five neurons is used for classification with and without applying the feature selection methods. The accuracy of 82.68, 86.33, 89.67, 84.61, and 91.83 % were observed using entire extracted features and selected features using MIM, CMIM, FCBF, and DISR methods by MLP, respectively. Therefore, the proposed method can be used in clinical setting for more validation.

authors

Khaleghi A,Sheikhani A,Mohammadi MR,Nasrabadi AM,Vand SR,Zarafshan H,Moeini M

doi

10.1007/s13246-015-0375-0

subject

Has Abstract

pub_date

2015-12-01 00:00:00

pages

551-9

issue

4

eissn

0158-9938

issn

1879-5447

pii

10.1007/s13246-015-0375-0

journal_volume

38

pub_type

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