Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity

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dc.contributor.authorAkdemir, Bayram
dc.contributor.authorÖkkesim, Şükrü
dc.contributor.authorKara, Sadık
dc.contributor.authorGüneş, Salih
dc.date.accessioned2020-03-26T17:38:09Z
dc.date.available2020-03-26T17:38:09Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg-Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8-13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum-maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent p erformance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R-2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.en_US
dc.description.sponsorshipTilrkiye Bilimsel ve Teknolojik Arastirma KurumuTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [106E144]en_US
dc.description.sponsorshipThe authors are grateful for the grant support provided by Tilrkiye Bilimsel ve Teknolojik Arastirma Kurumu under Contract 106E144.; The authors would like to thank Dr Tancan Uysal, who has been working at the Department of Orthodontics, School of Dentistry, Erciyes University, for his technical assistance.en_US
dc.identifier.doi10.1243/09544119JEIM619en_US
dc.identifier.endpage1001en_US
dc.identifier.issn0954-4119en_US
dc.identifier.issn2041-3033en_US
dc.identifier.issueH8en_US
dc.identifier.pmid20092096en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage991en_US
dc.identifier.urihttps://dx.doi.org/10.1243/09544119JEIM619
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23386
dc.identifier.volume223en_US
dc.identifier.wosWOS:000272478200006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSAGE PUBLICATIONS LTDen_US
dc.relation.ispartofPROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectcovariance-supported normalization methoden_US
dc.subjectelectromyographyen_US
dc.subjectorthodontic trainer treatmenten_US
dc.subjectartificial neural networken_US
dc.titleCorrelation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activityen_US
dc.typeArticleen_US

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