Deep Learning for ECG-Derived Respiration Using the Fantasia Dataset

Lana Dominković, Biljana Mileva Boshkoska, Biljana Mileva Boshkoska, Aleksandra Rashkovska, Aleksandra Rashkovska

2025

Abstract

In this paper, we explore a deep learning approach for extracting respiratory signals from electrocardiogram (ECG) data using the Fantasia dataset. We implemented a fully convolutional neural network model, inspired by the U-Net architecture, and designed to estimate respiratory signals from ECG data. The model incorporates convolutional layers, ReLU activations, batch normalization, max pooling, and up-sampling layers. Our deep learning model achieved an average correlation coefficient (CC) of 0.51 and Mean Squared Error (MSE) of 0.046, outperforming four out of six baseline signal processing algorithms based on the CC metric, and outperforming all signal processing algorithms based on the MSE metric. These findings demonstrate the effectiveness of deep learning in improving the accuracy and robustness of ECG-derived respiration (EDR). The research highlights the potential of advanced machine learning models for non-invasive respiratory monitoring and paves the way for future studies focused on exploring more complex architectures and broader datasets to further enhance performance and generalizability.

Paper Citation

in Harvard Style

Dominković L., Boshkoska B. and Rashkovska A. (2025). Deep Learning for ECG-Derived Respiration Using the Fantasia Dataset. 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 564-570. DOI: 10.5220/0013165300003911

in Bibtex Style

@conference{healthinf25,
author={Lana Dominković and Biljana Boshkoska and Aleksandra Rashkovska},
title={Deep Learning for ECG-Derived Respiration Using the Fantasia Dataset},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={564-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013165300003911},
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 - Deep Learning for ECG-Derived Respiration Using the Fantasia Dataset
SN - 978-989-758-731-3
AU - Dominković L.
AU - Boshkoska B.
AU - Rashkovska A.
PY - 2025
SP - 564
EP - 570
DO - 10.5220/0013165300003911
PB - SciTePress