An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification
Min Xia, Yangsong Zhang, Yihan Wu, Xiuzhu Wang
Abstract
Major depressive disorder (MDD) is a prevalent mental illness associated with abnormalities in structural and functional brain connectivity, and it has become a global public health problem. Early diagnosis is important and challenging for the treatment of MDD. Previous studies proposed the classification methods for MDD based on brain connectivity features through functional connectivity (FC) or effective connectivity (EC) measures. However, it requires prior knowledge and experience to manually select a algorithm to calculate the brain connectivity features. Given that the representation learning capabilities of deep learning (DL) models and the ability to capture correlations between data of self-attention mechanism, we proposed an end-to-end integrated DL model for classifying MDD patients and healthy controls (HCs) based on resting-state electroencephalography (EEG) data. This model first automatically learned the potential connectivity relationships among EEG channels through a multi-head self-attention mechanism, and then extracted higher-level features through a parallel two-branch convolution neural network (CNN) module, and finally completed the classification through a fully connected layer. A public resting-state EEG dataset was utilized to evaluate the validity of the proposed model. The experimental results indicated that the proposed model achieved 91.06% average accuracy that was better than those of comparison methods using the leave-one-subject-out cross-validation (LOSOCV) method. This study may provide a novel approach for brain connectivity modeling of MDD detection.