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Prediction of Cardiac Arrhythmia using Artificial Neural Network

by J. P. Kelwade, S. S. Salankar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 20
Year of Publication: 2015
Authors: J. P. Kelwade, S. S. Salankar
10.5120/20270-2679

J. P. Kelwade, S. S. Salankar . Prediction of Cardiac Arrhythmia using Artificial Neural Network. International Journal of Computer Applications. 115, 20 ( April 2015), 30-35. DOI=10.5120/20270-2679

@article{ 10.5120/20270-2679,
author = { J. P. Kelwade, S. S. Salankar },
title = { Prediction of Cardiac Arrhythmia using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number20/20270-2679/ },
doi = { 10.5120/20270-2679 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:25.826620+05:30
%A J. P. Kelwade
%A S. S. Salankar
%T Prediction of Cardiac Arrhythmia using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 20
%P 30-35
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Heart Rate Variability (HRV) is a non-invasive way of analysis for estimating the autonomic nervous system and based on measurement of the variability of heart rate signals. HRV signal is mostly noted for cardiac arrhythmia detection and classification. In this paper, artificial neural network (ANN) is used as a classifier to predict cardiac arrhythmias into five classes. The HRV data, RR interval time series is obtained using the Electrocardiogram (ECG) data from the MIT-BIH Arrhythmia Database. The linear and nonlinear parameters are extracted from RR intervals are used to train Multi-Layer Perceptron (MLP) neural network. In our study of neural network for time series i. e. RR interval time series, the prediction of Normal Sinus Rhythm (NSR), Premature Ventricular Contraction (PVC), Atrial fibrillation (AFIB), Left bundle branch block (LBBB) and Second degree block (BII) can be done using proposed algorithm. The 70% of the datasets are used to train MLP neural network. The proposed algorithm is predicted with next 30% of the datasets and satisfactory results obtained with prediction overall accuracy of 97%.

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Index Terms

Computer Science
Information Sciences

Keywords

Heart Rate Variability Electrocardiogram artificial neural network arrhythmia prediction

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