Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance.
- Author:
Turker Tekin ERGUZEL
1
;
Serhat OZEKES
;
Selahattin GULTEKIN
;
Nevzat TARHAN
;
Gokben HIZLI SAYAR
;
Ali BAYRAM
Author Information
- Publication Type:Original Article
- Keywords: Major depressive disorder; Transcranial magnetic stimulation; Electroencephalography; Neural network
- MeSH: Artificial Intelligence; Brain; Classification; Depressive Disorder, Major*; Electroencephalography; Humans; ROC Curve; Transcranial Magnetic Stimulation
- From:Psychiatry Investigation 2015;12(1):61-65
- CountryRepublic of Korea
- Language:English
- 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.
