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Detection of bundle branch block using higher order statistics and temporal features
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 3 (31 May. 2021), pp.279-285, 7 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-05-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Economics & Business Administration
Abstract EN
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.
American Psychological Association (APA)
Kaya, Yasin. 2021. Detection of bundle branch block using higher order statistics and temporal features. The International Arab Journal of Information Technology،Vol. 18, no. 3, pp.279-285.
https://search.emarefa.net/detail/BIM-1432135
Modern Language Association (MLA)
Kaya, Yasin. Detection of bundle branch block using higher order statistics and temporal features. The International Arab Journal of Information Technology Vol. 18, no. 3 (May. 2021), pp.279-285.
https://search.emarefa.net/detail/BIM-1432135
American Medical Association (AMA)
Kaya, Yasin. Detection of bundle branch block using higher order statistics and temporal features. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 3, pp.279-285.
https://search.emarefa.net/detail/BIM-1432135
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 284-285
Record ID
BIM-1432135