Machine learning approaches to predict repetitive transcranial magnetic stimulation treatment response in major depressive disorder

摘要: Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment that is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The efficacy of rTMS treatment can be predicted using pre-treatment cordance, a quantitative electroencephalography (QEEG) method extracting information from absolute and relative power of EEG spectra, that will prevent trial-and error treatment sequences, subject suffering and increase in health-care costs. In this study, pre-treatment QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7 and F8) in slow bands (delta and theta) for 147 MDD subjects. In order to classify MDD subjects as responder or non-responder, four different machine learning techniques, which are Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Tree (DT), were used and their performances …