Real Time EEG Analysis for Prediction of Antidepressant Responses of Transcranial Magnetic Stimulation in Major Depressive Disorder Based on Machine Learning

• Main Authors: Yi-Chen Li, 李易宸
• Other Authors: Chung-Ping Chen
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/hjgqyk

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 107 === Major depressive disorder (MDD) is increasingly recognized as a chronic, deteriorating illness with high comorbidity. A significant proportion of patients with MDD fail to respond to sequential antidepressants. Such treatment-resistant depression can be treated with noninvasive brain stimulation, such as repetitive transcranial magnetic stimulation (rTMS) and intermittent theta-burst stimulation (iTBS). In this study, we analyzed electroencephalograph (EEG) signals from a total of 90 patients with MDD. Antidepressant responses were predicted by observing the features of patients with MDD receiving rTMS or iTBS treatments. EEG signals were decomposed into 5 bands: delta, theta, alpha, beta, and gamma. Feature extraction, including linear and nonlinear methods, was applied to the EEG signals. First, our study demonstrated that frontal theta could be associated with antidepressant responses to rTMS instead of iTBS and sham treatments. Second, frontal delta and all bands (1–60 Hz) were associated with antidepressant responses to rTMS treatment. Third, antidepressant responses to iTBS treatment might be associated with frontal beta waves. Finally, machine learning and deep learning were used for distinguishing between responders and non-responders, and a 91% validation accuracy rate, an 83.3% true positive rate, and a 5.0% false positive rate were achieved.