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Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm

by G. Subramanya Nayak, Dayananda Nayak
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 6
Year of Publication: 2012
Authors: G. Subramanya Nayak, Dayananda Nayak
10.5120/8570-2294

G. Subramanya Nayak, Dayananda Nayak . Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm. International Journal of Computer Applications. 54, 6 ( September 2012), 20-23. DOI=10.5120/8570-2294

@article{ 10.5120/8570-2294,
author = { G. Subramanya Nayak, Dayananda Nayak },
title = { Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 6 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number6/8570-2294/ },
doi = { 10.5120/8570-2294 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:55:00.113138+05:30
%A G. Subramanya Nayak
%A Dayananda Nayak
%T Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 6
%P 20-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrocardiogram is one important physiological signal, which is used in assessing cardiac health. The extraction of features used for identification of the state of ECG is discussed in this paper. Using MAT LAB programs/tools, different statistical features are extracted from both normal and arrhythmia spectra. These features include arithmetic mean, median, variance, residuals on curve fitting etc. The values of the feature vector reveal information regarding cardiac health state. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Electrocardiogram Back propagation algorithm Neural Network

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