Prediction of treatment outcome for repetitive transcranial magnetic stimulation in major depressive disorder using connectivity measures and ensemble of pre-trained deep learning models

Repetitive Transcranial Magnetic Stimulation (rTMS) can be used as an effective treatment for Major Depressive Disorder (MDD) especially when a patient does not respond to multiple antidepressants. However, the prediction of the treatment outcome of rTMS is a vital task to prevent starting an inefficient treatment which may waste several important weeks for the patient and clinic. In the present study, we acquired 19-channel Electro-Encephalogram (EEG) for 34 MDD patients diagnosed with drug-resistant Depression before initiating rTMS treatment. Effective Connectivity matrix was obtained from four frequency bands of EEG signal to create connectivity images which, simultaneously, represent spatial and frequency information of EEG signals. Then, we investigated five powerful pre-trained Convolutional Neural Networks (CNN) named VGG16, Xception, InceptionResNetV2, DenseNet121, and EfficientNetB0 as Transfer Learning (TL) models to predict rTMS treatment outcome by discriminating Responders and NonResponders. VGG16 and DenseNet121 achieved the best performance with an accuracy of 89.22%. Additionally, we developed a new ensemble model based on all pre-trained CNN models using a weighted majority voting approach. The Differential Evolution Optimization (DEO) algorithm is used to find the optimal weights for the ensemble model and the mean accuracy of 92.28% was obtained. The superior performance of the optimized ensemble of these pre-trained CNN models, using effective connectivity images obtained from EEG signals, shows that the proposed method is highly capable of predicting the treatment outcome of rTMS.