Detrended fluctuation analysis for major depressive disorder

Mumtaz, Wajid and Malik, Aamir Saeed and Azhar Ali, Syed Saad and Mohd Yasin, Mohd Azhar and Amin, Hafeez Ullah (2015) Detrended fluctuation analysis for major depressive disorder. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Departments / MOR / COE: Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Health Analytics
Depositing User: Dr Aamir Saeed Malik
Date Deposited: 07 Oct 2016 01:42
Last Modified: 07 Oct 2016 01:42

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