Automated Major Depressive Disorder Classification using Deep Convolutional Neural Networks and Choquet Fuzzy Integral Fusion

Major depressive disorder (MDD) is a common and severe ailment impacting functional frailty, while its concrete manifestations have been shrouded in mystery. Hence, manual diagnosis of MDD is an arduous and subjective task. Despite the aid of electroencephalogram (EEG) signals in the detection, developing intelligent systems are required to improve clinical utility, performance, and efficiency. In this study, we focus on the automated detection of MDD via raw EEG data using convolutional neural networks (CNN). For this objective, we first extracted the short-time Fourier transform (STFT) of EEG records for five distinct band powers and created an image representing the frequency oscillation of every channel during a resting state. Afterward, we applied three approaches to determine whether a subject is MDD or a Healthy individual. In the first approach, a 2D-CNN model was developed for each band power to detect MDD separately. Second, the outcomes of the developed models were used to establish a Choquet fuzzy integral fusion to classify subjects using all of the previous models. The third approach was dedicated to introducing a 3D-CNN architecture. This model received three-dimensional data by putting different band powers' images together. The two last approaches achieved a 95.65% accuracy and 100% sensitivity to detect MDD. The proposed approaches can help clinicians as straightforward, efficient, and intelligent diagnostic tools for detecting MDD.