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Detection of Bundle Branch Block using Higher Order Statistics and Temporal Features

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dc.contributor.author Kaya, Yasin
dc.date.accessioned 2022-12-29T07:44:09Z
dc.date.available 2022-12-29T07:44:09Z
dc.date.issued 2021-05
dc.identifier.citation Kaya, Y. (2021). Detection of Bundle Branch Block using Higher Order Statistics and Temporal Features. The International Arab Journal of Information Technology, 18(3). https://doi.org/10.34028/iajit/18/3/3 tr_TR
dc.identifier.issn 1683-3198
dc.identifier.uri http://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/4080
dc.identifier.uri http://dx.doi.org/10.34028/iajit/18/3/3
dc.description WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection. tr_TR
dc.description.abstract Bundle Branch Block (BBB) beats are the most common Electrocardiogram (ECG) arrhythmias and can be indicators of significant heart disease. This study aimed to provide an effective machine-learning method for the detection of BBB beats. To this purpose, statistical and temporal features were calculated and the more valuable ones searched using feature selection algorithms. Forward search, backward elimination and genetic algorithms were used for feature selection. Three different classifiers, K-Nearest Neighbors (KNN), neural networks, and support vector machines, were used comparatively in this study. Accuracy, specificity, and sensitivity performance metrics were calculated in order to compare the results. Normal sinus rhythm (N), Right Bundle Branch Block (RBBB), and Left Bundle Branch Block (LBBB) ECG beat types were used in the study. All beats containing these three beat types in the MIT-BIH arrhythmia database were used in the experiments. All of the feature sets were obtained at a promising classification accuracy for BBB classification. The KNN classifier using backward elimination-selected features achieved the highest classification accuracy results in the study with 99.82%. The results showed the proposed approach to be successful in the detection of BBB beats. tr_TR
dc.language.iso en tr_TR
dc.relation.ispartofseries 2021;Volume: 18 Issue: 3
dc.subject ECG tr_TR
dc.subject arrhythmia detection tr_TR
dc.subject bundle branch block tr_TR
dc.subject genetic algorithms tr_TR
dc.subject neural networks tr_TR
dc.subject k-nearest neighbors tr_TR
dc.subject support vector machines tr_TR
dc.subject backward elimination tr_TR
dc.subject forward selection tr_TR
dc.title Detection of Bundle Branch Block using Higher Order Statistics and Temporal Features tr_TR
dc.type Article tr_TR

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