Detection of Bundle Branch Block using Higher Order Statistics and Temporal Features
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Zarka Private Univ
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
ECG, arrhythmia detection, bundle branch block, genetic algorithms, neural networks, k-nearest neighbors, support vector machines, backward elimination, forward selection
Kaynak
International Arab Journal of Information Technology
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
18
Sayı
3
