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

Detection of Bundle Branch Block using Higher

Order Statistics and Temporal Features

Yasin Kaya

 Department of Computer Engineering, Adana Alparslan Türkeş Science and Technology University, Turkey

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.

Keywords: ECG, arrhythmia detection, bundle branch block, genetic algorithms, neural networks, k-nearest neighbors, support vector machines, backward elimination, forward selection.

Received August 20, 2019; accepted October 5, 2020

https://doi.org/10.34028/iajit/18/3/3
Read 902 times Last modified on Monday, 26 April 2021 03:08
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