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

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dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.contributor.authorKaya, Yasin
dc.date.accessioned2025-01-06T17:37:33Z
dc.date.available2025-01-06T17:37:33Z
dc.date.issued2021
dc.description.abstractBundle 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.
dc.identifier.doi10.34028/iajit/18/3/3
dc.identifier.endpage285
dc.identifier.issn1683-3198
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85106423714
dc.identifier.scopusqualityQ2
dc.identifier.startpage279
dc.identifier.urihttps://doi.org/10.34028/iajit/18/3/3
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2277
dc.identifier.volume18
dc.identifier.wosWOS:000667208600003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherZarka Private Univ
dc.relation.ispartofInternational Arab Journal of Information Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectECG
dc.subjectarrhythmia detection
dc.subjectbundle branch block
dc.subjectgenetic algorithms
dc.subjectneural networks
dc.subjectk-nearest neighbors
dc.subjectsupport vector machines
dc.subjectbackward elimination
dc.subjectforward selection
dc.titleDetection of Bundle Branch Block using Higher Order Statistics and Temporal Features
dc.typeArticle

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