title: Detrended fluctuation analysis for major depressive disorder.
abstract: Clinical utility of Electroencephalography (EEG) based diagnostic studies is less clear for major depressive disorder (MDD). In this paper, a novel machine learning (ML) scheme was presented to discriminate the MDD patients and healthy controls. The proposed method inherently involved feature extraction, selection, classification and validation. The EEG data acquisition involved eyes closed (EC) and eyes open (EO) conditions. At feature extraction stage, the de-trended fluctuation analysis (DFA) was performed, based on the EEG data, to achieve scaling exponents. The DFA was performed to analyzes the presence or absence of long-range temporal correlations (LRTC) in the recorded EEG data. The scaling exponents were used as input features to our proposed system. At feature selection stage, 3 different techniques were used for comparison purposes. Logistic regression (LR) classifier was employed. The method was validated by a 10-fold cross-validation. As results, we have observed that the effect of 3 different reference montages on the computed features. The proposed method employed 3 different types of feature selection techniques for comparison purposes as well. The results show that the DFA analysis performed better in LE data compared with the IR and AR data. In addition, during Wilcoxon ranking, the AR performed better than LE and IR. Based on the results, it was concluded that the DFA provided useful information to discriminate the MDD patients and with further validation can be employed in clinics for diagnosis of MDD.
authors.forename: Wajid
authors.initials: W
authors.lastname: Mumtaz
authors.forename: Aamir Saeed
authors.initials: AS
authors.lastname: Malik
authors.forename: Syed Saad Azhar
authors.initials: SS
authors.lastname: Ali
authors.forename: Mohd Azhar Mohd
authors.initials: MA
authors.lastname: Yasin
authors.forename: Hafeezullah
authors.initials: H
authors.lastname: Amin
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D005123:Eye; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008875:Middle Aged; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted