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Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/1588
Title: An improved cardiac arrhythmia classification using an RR interval-based approach
Authors: Bohat, Vijay Kumar
Keywords: ECG
Cardiac arrhythmia
Classifier
RR interval
PVC
PAC
Issue Date: Apr-2021
Publisher: Elsevier
Abstract: Accurate and early detection of cardiac arrhythmia present in an electrocardiogram (ECG) can prevent many premature deaths. Cardiac arrhythmia arises due to the improper conduction of electrical impulses throughout the heart. In this paper, we propose an improved RR interval-based cardiac arrhythmia classification approach. The Discrete Wavelet Transform (DWT) and median filters were used to remove high-frequency noise and baseline wander from the raw ECG. Next, the processed ECG was segmented after the determination of the QRS region. We extracted the primary feature RR interval and other statistical features from the beats to classify the Normal, Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC). The K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF) classifier were utilised for classification. Overall performance of SVM with Gaussian kernel achieved Se % = 99.28, Sp % = 99.63, +P % = 99.28, and Acc % = 99.51, which is better than the other classifiers used in this method. The obtained results of the proposed method are significantly better and more accurate.
URI: https://doi.org/10.1016/j.bbe.2021.04.004
http://lrcdrs.bennett.edu.in:80/handle/123456789/1588
ISSN: 0208-5216
Appears in Collections:Journal Articles_SCSET

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