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Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance.

Erguzel TT, Ozekes S, Gultekin S, Tarhan N, Hizli Sayar G, Bayram A - Psychiatry Investig (2015)

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey.

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.

No MeSH data available.


Related in: MedlinePlus

Receiver Operating Characteristic curves of 6, 8, and 10 fold cross validation.
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Figure 1: Receiver Operating Characteristic curves of 6, 8, and 10 fold cross validation.

Mentions: ROC curves of n-fold each are given in Figure 1. AUC values for various k-values are also given in Table 1. The AUCs for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.


Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance.

Erguzel TT, Ozekes S, Gultekin S, Tarhan N, Hizli Sayar G, Bayram A - Psychiatry Investig (2015)

Receiver Operating Characteristic curves of 6, 8, and 10 fold cross validation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4310922&req=5

Figure 1: Receiver Operating Characteristic curves of 6, 8, and 10 fold cross validation.
Mentions: ROC curves of n-fold each are given in Figure 1. AUC values for various k-values are also given in Table 1. The AUCs for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey.

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.

No MeSH data available.


Related in: MedlinePlus