Does a 2D-CNN network using EEG-based wavelet coherence accurately diagnose Major Depressive Disorder?
A 2D-CNN network using EEG-based wavelet coherence in the default mode network provides highly accurate automated diagnosis of Major Depressive Disorder.
Major depressive disorder (MDD) contributes the most to human’s functional frailty worldwide. Therefore, its timely diagnosis and treatment is of utmost importance. Conventionally, MDD is diagnosed using subjective evaluation methods, so, it is essential to develop a quantitative biomarker for its automated diagnosis. Accordingly, this study proposes a 2D-CNN network and a new biomarker for automated detection of MDD. The proposed biomarker is developed by estimating wavelet coherence (WCOH) amongst the brain’s default mode network (DMN) regions using EEG signals. This biomarker data from 30 MDD patients and 30 healthy controls (HCs) is randomly divided into training and testing sets for network training and blind testing, respectively. The performance of the network is evaluated via 10-fold cross-validation which is applied to the training data only to avoid learning bias. The blind testing of subjects is performed using two different classification approaches i.e., sample-based and subject-based. The former achieves 98.1% accuracy, 98.0% sensitivity, and 98.2% specificity whereas the latter yields 100% each for accuracy, sensitivity, and specificity. This high classification performance validates that DMN-based WCOH can be used as a potential biomarker and that the proposed 2D-CNN can provide reliable performance assessment for the diagnosis of MDD.
Khan et al. (Fri,) studied this question.