Assessing Electrocardiogram Quality: A Deep Learning Framework For Noise Detection And Classification

Márcia Monteiro, Mariana Dias, Hugo Gamboa

2025

Abstract

The electrocardiogram (ECG) is an essential tool in the diagnosis of cardiovascular conditions. A common obstacle to readability and reliability is the vulnerability of ECG signals to noise, especially in wearable devices and long-term monitoring. Traditional methods have limited accuracy in noise detection, and, while deep learning (DL) shows promise, current models primarily focus on binary classification, lacking detailed quality analysis. This study proposes a DL model that assesses ECG signal quality, detecting and classifying specific noise types, with random-length noise segments added to clean 10-second signals to simulate real-world scenarios. The model, using gated recurrent units (GRUs), identifies three common noise types: baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), achieving 98.09 % accuracy for BW, 92.62 % for MA, and 90.71 % for EM with F1 scores of 88.89 % for BW, 82.19 % for EM and 64.62 % for MA. It also surpasses existing DL methods, reaching 99.86 % accuracy for binary classification, with high recall and precision.

Paper Citation

in Harvard Style

Monteiro M., Dias M. and Gamboa H. (2025). Assessing Electrocardiogram Quality: A Deep Learning Framework For Noise Detection And Classification. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-731-3, SciTePress, pages 793-804. DOI: 10.5220/0013313800003911

in Bibtex Style

@conference{biosignals25,
author={Márcia Monteiro and Mariana Dias and Hugo Gamboa},
title={Assessing Electrocardiogram Quality: A Deep Learning Framework For Noise Detection And Classification},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={793-804},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013313800003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Assessing Electrocardiogram Quality: A Deep Learning Framework For Noise Detection And Classification
SN - 978-989-758-731-3
AU - Monteiro M.
AU - Dias M.
AU - Gamboa H.
PY - 2025
SP - 793
EP - 804
DO - 10.5220/0013313800003911
PB - SciTePress