Optimizing Electrocardiogram Denoising for Enhanced Cardiovascular Disease Detection: A Metaheuristic Approach

Authors

  • Javier Galvis-Chacón Universidad de Guadalajara
  • Oscar Ramos-Soto Universidad de Guadalajara
  • Diego Oliva Universidad de Guadalajara
  • Arturo Valdivia Universidad de Guadalajara
  • Horacio Rostro-González Universitat Ramon Llull
  • Saúl Zapotecas-Martínez Instituto Nacional de Astrofísica, Óptica y Electrónica
  • Marco Perez-Cisneros Universidad de Guadalajara

DOI:

https://doi.org/10.13053/cys-29-1-5532

Keywords:

ECG signal, extreme gradient boosting (XGBoost), metaheuristic algorithms (MAs)

Abstract

Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting formore deaths than any other known cause. Hence, early detection followed by timely treatment of these diseases is crucial to preventing premature deaths. In this scenario, the electrocardiogram (ECG) emerges as a key diagnostic tool, providing critical insightin to the heart’s electrical activity and allowing early identification of potentially lethal conditions such asarrhythmias and heart attacks. The automated analysis of ECGs represents a potential tool for the timely detection of different heart conditions. Nevertheless, noise is always present due to the signal acquisition process, and the degree of removal highly impacts the ECG classification accuracy. This paper presentsan approach to determining the best ECG degree of noise removal effectively. It comprises the iterative analysis of the wavelet-based denoising method and the Extreme Gradient Boosting (XGBoost) classification, whose best noise removal parameter configuration is obtained through an optimization based on metaheuristic algorithms (MAs). Different MAs are tested to evaluate their performance in classification accuracy enhancement. This proposal is trained and tested on the MIT BIH public ECG dataset to demonstrate its effectiveness across different signal acquisitions. This method is intended to be a preprocessing stage to improve the accuracy of predictive models based on neural networks and the future development of morerobust ECG classifier systems, which will improve the detection of CVD.

Downloads

Published

2025-03-24

Issue

Vol. 29 No. 1 (2025): 29(1) 2025

Section

Articles of the Thematic Section (2)

License 

Hereby I transfer exclusively to the Journal "Computación y 
Sistemas", published by the Computing Research Center (CIC-IPN),
the Copyright of the aforementioned paper. I also accept that these 
rights will not be transferred to any other publication, in any other 
format, language or other existing means of developing.
I certify that the paper has not been previously disclosed or simultaneo
usly submitted to any other publication, and that it does not contain 
material whose publication would violate the Copyright or other 
proprietary rights of any person, company or institution. I certify that 
I have the permission from the institution or company where I work or 
study to publish this work.
The representative author accepts the responsibility for the publication
of this paper on behalf of each and every one of the authors. 
This transfer is subject to the following conditions:
  • The authors retain all ownership rights (such as patent rights) of this work, except for the publishing rights transferred to the CIC, through this document.
  • Authors retain the right to publish the work in whole or in part in any book they are the authors or publishers. They can also make use of this work in conferences, courses, personal web pages, and so on.
  • Authors may include working as part of his thesis, for non-profit distribution only.