%0 Journal Article
%@ 01400118
%A Mumtaz, W.
%A Ali, S.S.A.
%A Yasin, M.A.M.
%A Malik, A.S.
%D 2018
%F scholars:10551
%I Springer Verlag
%J Medical and Biological Engineering and Computing
%K Artificial intelligence; Computer aided diagnosis; Computer aided instruction; Diagnosis; Diseases; Learning systems; Support vector machines, Classification accuracy; Computer Aided Diagnosis(CAD); Functional connectivity; Functional disabilities; Machine learning techniques; Major depressive disorder; Major depressive disorder (MDD); Synchronization likelihoods, Education, adult; Article; Bayesian learning; clinical article; comparative study; controlled study; diagnostic accuracy; diagnostic test accuracy study; electroencephalography; female; functional connectivity; human; machine learning; major depression; male; priority journal; sensitivity and specificity; support vector machine
%N 2
%P 233-246
%R 10.1007/s11517-017-1685-z
%T A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
%U https://khub.utp.edu.my/scholars/10551/
%V 56
%X Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98, sensitivity = 99.9, specificity = 95 and f-measure = 0.97; LR classification accuracy = 91.7, sensitivity = 86.66, specificity = 96.6 and f-measure = 0.90; NB classification accuracy = 93.6, sensitivity = 100, specificity = 87.9 and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes. © 2017, International Federation for Medical and Biological Engineering.
%Z cited By 94