Prediction of Treatment Outcome in Major Depressive Disorder Using Ensemble of Hybrid Transfer Learning and Long Short-Term Memory Based on EEG Signal

Major depressive disorder (MDD) is a widespread global mental disease. The effectiveness of selective serotonin reuptake inhibitors (SSRIs) antidepressants prescribed for MDD patients is limited and pretreatment assessment of treatment outcome is a vital task. In this study, a hybrid model based on transfer learning (TL) of powerful pretrained deep convolutional neural networks (CNNs) empowered with bidirectional long short-term memory (BLSTM) cells and attention mechanism were developed to classify responders (R) and nonresponders (NR) to SSRI antidepressants, using raw data images of pretreatment electro-encephalogram (EEG) signal obtained from 30 MDD patients. TL-LSTM-Attention models based on VGG16, Xception, and Densenet121 models were created for the classification of R/NR. Results show that the highest performance of hybrid TL-LSTM-Attention models was achieved by VGG16-LSTM-Attention with an accuracy of 98.21%, sensitivity of 96.22%, and specificity of 99.67%. An ensemble model based on weighted majority voting among hybrid models is constructed to surpass the performance of each single hybrid model. Our proposed ensemble model gained accuracy, sensitivity, and specificity of 98.84%, 97.80%, and 99.60%, respectively. Hence, the proposed model which is an ensemble of TL-LSTM-Attention models fed by raw data images of EEG signal leads to high confidence in the prediction of antidepressants treatment outcome.