Akdemir, BayramÖkkesim, ŞükrüKara, SadıkGüneş, Salih2020-03-262020-03-2620090954-41192041-3033https://dx.doi.org/10.1243/09544119JEIM619https://hdl.handle.net/20.500.12395/23386In 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.en10.1243/09544119JEIM619info:eu-repo/semantics/closedAccesscovariance-supported normalization methodelectromyographyorthodontic trainer treatmentartificial neural networkArticle223H8991100120092096Q3WOS:000272478200006Q4