Assessing Signal Noise Effects on Machine Learning Models for ECG-Based Cardiac Diagnosis

Emanuela Guglielmi, Davide Donato Russo, Pasquale Trinchese, Gennaro Laudato, Simone Scalabrino, Gianluca Testa, Rocco Oliveto

2025

Abstract

The Internet of Medical Things (IoMT) plays a vital role in healthcare by enhancing preventive careand chronic disease management through continuous monitoring using smart sensors and wearable devices. However, the reliability of IoMT systems can be compromised by noise in the acquired vital signals, which can negatively impact the accuracy of Machine Learning (ML) models used for anomaly detection. This study evaluates the impact of various disturbances on the performance of ML models in predicting cardiac conditions, with a focus on assessing the reliability and effectiveness of these systems in real-world applications. We investigated the effects of three types of noise—baseline wander, muscle artifact noise, and electrode motion artifact—on the performance of two advanced ML models designed to predict cardiac conditions, specifically atrial fibrillation (AF) and ventricular tachycardia (VT). Our analysis centered on how different noise intensities (i.e., the “loudness” of the noise) and durations (i.e., the length of time the noise persists) impacted the classification performance of these models. The VT detection model showed robust performance, with minimal impact even under intense and prolonged noise conditions. In contrast, AF detection was affected by all types of noise, with classification accuracy decreasing by up to ∼59% in the most challenging scenarios.

Paper Citation

in Harvard Style

Guglielmi E., Russo D., Trinchese P., Laudato G., Scalabrino S., Testa G. and Oliveto R. (2025). Assessing Signal Noise Effects on Machine Learning Models for ECG-Based Cardiac Diagnosis. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 458-466. DOI: 10.5220/0013126800003911

in Bibtex Style

@conference{healthinf25,
author={Emanuela Guglielmi and Davide Russo and Pasquale Trinchese and Gennaro Laudato and Simone Scalabrino and Gianluca Testa and Rocco Oliveto},
title={Assessing Signal Noise Effects on Machine Learning Models for ECG-Based Cardiac Diagnosis},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={458-466},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013126800003911},
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 2: HEALTHINF
TI - Assessing Signal Noise Effects on Machine Learning Models for ECG-Based Cardiac Diagnosis
SN - 978-989-758-731-3
AU - Guglielmi E.
AU - Russo D.
AU - Trinchese P.
AU - Laudato G.
AU - Scalabrino S.
AU - Testa G.
AU - Oliveto R.
PY - 2025
SP - 458
EP - 466
DO - 10.5220/0013126800003911
PB - SciTePress