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Rhythm Disorders ñ Heart Beat Classification of an Elec-trocardiogram Signal

by Faiza Iftikhar, Ayesha Shams, Arfa Dilawari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 11
Year of Publication: 2012
Authors: Faiza Iftikhar, Ayesha Shams, Arfa Dilawari
10.5120/4867-7292

Faiza Iftikhar, Ayesha Shams, Arfa Dilawari . Rhythm Disorders ñ Heart Beat Classification of an Elec-trocardiogram Signal. International Journal of Computer Applications. 39, 11 ( February 2012), 38-44. DOI=10.5120/4867-7292

@article{ 10.5120/4867-7292,
author = { Faiza Iftikhar, Ayesha Shams, Arfa Dilawari },
title = { Rhythm Disorders ñ Heart Beat Classification of an Elec-trocardiogram Signal },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 11 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number11/4867-7292/ },
doi = { 10.5120/4867-7292 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:12.626519+05:30
%A Faiza Iftikhar
%A Ayesha Shams
%A Arfa Dilawari
%T Rhythm Disorders ñ Heart Beat Classification of an Elec-trocardiogram Signal
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 11
%P 38-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Arrhythmia disorders play a vital role in heart diseases pro-gression. Detection and treatment of arrhythmia disorders can help indirectly in controlling the heart disease. In hospitals, physicians classify the beats after examining the electrocardi-ogram (ECG) report. Sometimes, physicians are not that expert to diagnose the arrhythmias correctly and accurately. In these circumstances, there is a need for automatic and accurate heart beat classifier which takes the ECG signal as an input and classify it into different rhythm disorders. In this paper, an arrhythmia disorder classifier is designed and developed using Feedforward Backpropagation neural network. The supervised network is trained based on the features extracted from the ECG databases of MIT-BIH. The trained network will classify the beats into premature atrial/ventricular contraction (PAC/PVC), left/right bundle branch block (LBBB/RBBB), paced beat and normal beat. This automatic system will make the treatment faster even in the absence of expert physicians.

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Index Terms

Computer Science
Information Sciences

Keywords

QRS Complex Mean Power Frequency Power Spectral Den-sity Fiducial Point LBBB RBBB PVC APC Paced purelin logsig

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