EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning

Abstract

ObjectiveDisrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving the diagnostic accuracy of machine learning classification models. We explored the potential of EEG-based FC during resting state and emotional processing, for diagnosing MDD using machine learning approach. MethodsEEG was recorded during resting state and while watching emotionally contagious happy and sad videos in 24 drug-naïve MDD patients and 25 healthy controls. FC was quantified using the Phase Lag Index. Three Random Forest classifier models were constructed to classify MDD patients and healthy controls, Model-I incorporating FC features from the resting state and Model-II and Model-III incorporating FC features while watching happy and sad videos respectively. ResultsImportant features distinguishing MDD and healthy controls were from all frequency bands and represent functional connectivity between fronto-temporal, fronto-parietal and fronto occipital regions. The cross-validation accuracies for Model-I, Model-II and Model-III were 92.3%, 94.9% and 89.7% and test accuracies were 60%, 80% and 70% respectively. Incorporating emotionally contagious videos improved the classification accuracies. ConclusionFindings support EEG FC patterns during resting state and emotional processing along with machine learning can be used to diagnose MDD. Future research should focus on replicating and validating these results. SignificanceEEG FC pattern combined with machine learning may be used for assisting in diagnosing MDD.