title: 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.forename: Ali
authors.initials: A
authors.lastname: Khaleghi
authors.forename: Ali
authors.initials: A
authors.lastname: Sheikhani
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Mohammadi
authors.forename: Ali Moti
authors.initials: AM
authors.lastname: Nasrabadi
authors.forename: Safa Rafiei
authors.initials: SR
authors.lastname: Vand
authors.forename: Hadi
authors.initials: H
authors.lastname: Zarafshan
authors.forename: Mahdi
authors.initials: M
authors.lastname: Moeini
mesh_terms: D000293:Adolescent; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted