Predicting Respiratory Depression in Neonates Using Deep Learning Neural Networks

Aleksandar Jeremic, Dejan Nikolic

2025

Abstract

Respiratory problems are one of the most common reasons for neonatal intensive care unit (NICU) admission of newborns. It has been estimated that as much as 29% of late preterm infants develop high respiratory morbidity. To this purpose invasive ventilation is often necessary for their treatment in NICU. These patients usually have underdeveloped respiratory system with deficiencies such as small airway caliber, few collateral airways, compliant chest wall, poor airway stability, and low functional residual capacity. Consequently ventilation control has been subject of considerable research interest. In this paper we propose an algorithm for detection of respiratory depression by predicting the onset of pO2 depressions using physiological measurements. We train deep neural network using previously obtained data set from NICU, McMaster University Hospital with intra-arterial pressure measurements and evaluate its performance. Preliminary results indicate that adequate performance can be achieved if sufficient number of measurements is available.

Paper Citation

in Harvard Style

Jeremic A. and Nikolic D. (2025). Predicting Respiratory Depression in Neonates Using Deep Learning Neural Networks. 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 1054-1057. DOI: 10.5220/0013385400003911

in Bibtex Style

@conference{biosignals25,
author={Aleksandar Jeremic and Dejan Nikolic},
title={Predicting Respiratory Depression in Neonates Using Deep Learning Neural Networks},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={1054-1057},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013385400003911},
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 - Predicting Respiratory Depression in Neonates Using Deep Learning Neural Networks
SN - 978-989-758-731-3
AU - Jeremic A.
AU - Nikolic D.
PY - 2025
SP - 1054
EP - 1057
DO - 10.5220/0013385400003911
PB - SciTePress