Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal

摘要

Background: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and nonresponders (NR) to rTMS treatment for major depression disorder (MDD) patients.