Psychiatry Investig.
2015 Jan;12(1):61-65. 10.4306/pi.2015.12.1.61.
Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance
- Affiliations
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- 1Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey. turker.erguzel@uskudar.edu.tr
- 2Department of Bioengineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey.
- 3Department of Psychiatry, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.
- 4Department of Psychiatry, NPIstanbul Hospital, Istanbul, Turkey.
- 5Biomedical Equipment Technology, Uskudar University, Istanbul, Turkey.
- KMID: 2316390
- DOI: http://doi.org/10.4306/pi.2015.12.1.61
Abstract
OBJECTIVE
The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN).
METHODS
The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation.
RESULTS
The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.
CONCLUSION
Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.
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