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Abstract

Apnoea, a pause in respiration, is almost ubiquitous in preterm infants born before completing 30 weeks gestation. Apnoea often begets hypoxemia and/or bradycardia, and has the potential to result in adverse neurodevelopmental consequences. Our current inability to predict apnoeic events in preterm infants requires apnoea to first be detected by monitoring device/s in order to trigger an intervention by bedside (medical or nursing) staff. Such a reactive management approach is laborious, and makes the consequences of apnoeic events inevitable. Recent technological advances and improved signal processing have allowed the possibility of developing prediction models for apnoeic events in preterm infants. However, the development of such models has numerous challenges and is only starting to show potential. This paper identifies requisite components and current gaps in developing prediction models for apnoeic events, and reviews previous studies on predicting apnoeic events in preterm infants.

Item Type:	Article
Authors/Creators:	Lim, K and Jiang, H and Marshall, AP and Salmon, B and Gale, TJ and Dargaville, PA
Keywords:	apnoea of prematurity, machine learning, neonatal intensive care, prediction, preterm infants
Journal or Publication Title:	Frontiers in Pediatrics
Publisher:	Frontiers Research Foundation
ISSN:	2296-2360
DOI / ID Number:	https://doi.org/10.3389/fped.2020.00570
Copyright Information:	Copyright 2020 Lim, Jiang, Marshall, Salmon, Gale and Dargaville. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Related URLs:	Google Scholar: Salmon, B UTAS Profile: Salmon, B Google Scholar: Gale, TJ UTAS Profile: Gale, TJ UTAS Profile: Dargaville, PA
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