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Intelligent ECG Signal Noise Removal using PSONN

by Sara Moein, Rajasvaran Logeswaran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 6
Year of Publication: 2012
Authors: Sara Moein, Rajasvaran Logeswaran
10.5120/6783-9085

Sara Moein, Rajasvaran Logeswaran . Intelligent ECG Signal Noise Removal using PSONN. International Journal of Computer Applications. 45, 6 ( May 2012), 9-17. DOI=10.5120/6783-9085

@article{ 10.5120/6783-9085,
author = { Sara Moein, Rajasvaran Logeswaran },
title = { Intelligent ECG Signal Noise Removal using PSONN },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 6 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 9-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number6/6783-9085/ },
doi = { 10.5120/6783-9085 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:53.091905+05:30
%A Sara Moein
%A Rajasvaran Logeswaran
%T Intelligent ECG Signal Noise Removal using PSONN
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 6
%P 9-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist and automatic heart disorder diagnosis systems. In this paper, the use of Particle Swarm Optimization Neural Network (PSONN) for automatically identifying the cutoff frequency of ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from four classes: normal sinus rhythm, atrial fibrillation, arrhythmia and supraventricular. Baseline wander is removed using the moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the PSONN. The performance of the PSONN with various parameters is investigated. The PSONN-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent PSONN-based system successfully denoised the ECG signals more effectively than the conventional method.

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Index Terms

Computer Science
Information Sciences

Keywords

Cutoff Frequency Particle Swarm Optimization Neural Network (psonn) Low-pass Filtering Finite Impulse Response (fir)

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