Automated detection of major depressive disorder with EEG signals: a time series classification using deep learning

Major depressive disorder (MDD) is considered a severe and common ailment affecting functional frailty, while its manifestations remain elusive. Hence, the manual detection of MDD is a challenging and subjective task. Although electroencephalogram (EEG) signals have shown promise in aiding diagnosis, further enhancement is required to improve accuracy, clinical utility, and efficiency. This study focuses on the automated detection of MDD using EEG data and deep neural network architecture. For this aim, first, a customized InceptionTime model is developed to detect MDD individuals via 19-channel raw EEG signals. A channel-selection strategy, consisting of three steps, is then applied to eliminate redundant channels. The proposed method achieved 91.67% accuracy using the full set of channels and 87.5% after channel reduction. Our analysis shows that i) only the first minute of EEG recording is sufficient for MDD detection, ii) models based on EEG recorded in eyes-closed resting-state outperform eyes-open conditions, and iii) customizing the InceptionTime model can improve its efficiency for different assignments. The proposed method is able to help clinicians as an efficient, straightforward, and intelligent diagnostic tool for the objective detection of MDD.