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dc.contributor.authorErguzel, Turker Tekin
dc.contributor.authorGultekin, Selahattin
dc.contributor.authorTarhan, Nevzat
dc.contributor.authorBayram, Ali
dc.contributor.authorOzekes, Serhat
dc.contributor.authorHizli Sayar, Gokben
dc.date.accessioned2015-02-20T11:13:01Z
dc.date.available2015-02-20T11:13:01Z
dc.date.issued2015-01-12
dc.identifier.citationErgüzel TT., Ozekes S, Gultekin S, Tarhan N.,HizliSayar G., Bayram A.,Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance, Psychiatry Investigation,(2015),http://dx.doi.org/10.4306/pi.2015.12.1.62tr_TR
dc.identifier.urihttp://dx.doi.org/10.4306/pi.2015.12.1.61
dc.identifier.urihttp://earsiv.uskudar.edu.tr/xmlui/handle/123456789/457
dc.description.abstractObjectiveaaThe 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). MethodsaaThe 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. ResultsaaThe 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. ConclusionaaPotential 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. Psychiatry Investig 2015;12(1):62-66 Key Wordsaa Major depressive disorder, Transcranial magnetic stimulation, Electroencephalography, Neural network.tr_TR
dc.language.isoengtr_TR
dc.relation.ispartofseriesSCI-E;
dc.relation.isversionof10.4306/pi.2015.12.1.62tr_TR
dc.subjectMajor depressive disordertr_TR
dc.subjectTranscranial magnetic stimulationtr_TR
dc.subjectElectroencephalographytr_TR
dc.subjectNeural networktr_TR
dc.titleNeural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordancetr_TR
dc.typeArticletr_TR
dc.relation.journalPsychiatry Investigationtr_TR
dc.contributor.departmentÜsküdar Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliğitr_TR
dc.contributor.authorIDTR19915tr_TR


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