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DOI: 10.14569/IJACSA.2015.060706
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Classification of Premature Ventricular Contraction in ECG

Author 1: Yasin Kaya
Author 2: Hüseyin Pehlivan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 7, 2015.

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Abstract: Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular, on the performance evaluation of time series approaches to the classification of PVC abnormality. Moreover, the performance effects of several dimension reduction approaches were also tested. Experiments were carried out using well-known machine learning methods, including neural networks, k-nearest neighbour, decision trees, and support vector machines. Findings were expressed in terms of accuracy, sensitivity, specificity, and running time for the MIT-BIH Arrhythmia Database. Among the different classification algorithms, the k-NN algorithm achieved the best classification rate. The results demonstrated that the proposed model exhibited higher accuracy rates than those of other works on this topic. According to the experimental results, the proposed approach achieved classification accuracy, sensitivity, and specificity rates of 99.63%, 99.29% and 99.89%, respectively.

Keywords: ECG; arrhythmia; classification; k-NN; PVC

Yasin Kaya and Hüseyin Pehlivan, “Classification of Premature Ventricular Contraction in ECG” International Journal of Advanced Computer Science and Applications(IJACSA), 6(7), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060706

@article{Kaya2015,
title = {Classification of Premature Ventricular Contraction in ECG},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060706},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060706},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {7},
author = {Yasin Kaya and Hüseyin Pehlivan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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