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

Journal: Journal of affective disorders

Published Date: Sep 1, 2019

Abstract

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 non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients.

Authors

Fatemeh Hasanzadeh

Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Maryam Mohebbi

Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran. Electronic address: m.mohebbi@kntu.ac.ir.
Reza Rostami

School of Psychology and Education, University of Tehran, Tehran, Iran.

Keywords

Adult Algorithms Depressive Disorder, Major Electroencephalography Female Humans Machine Learning Male Middle Aged Prognosis Sensitivity and Specificity Transcranial Magnetic Stimulation Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (31176185)

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

Abstract

Authors

Keywords

External Resources

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