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1.
Front Pediatr ; 8: 570, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33042915

RESUMO

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.

2.
J Med Syst ; 41(9): 134, 2017 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-28755269

RESUMO

Imaging systems have applications in patient respiratory monitoring but with limited application in neonatal intensive care units (NICU). In this paper we propose an algorithm to automatically detect the torso in an image of a preterm infant during non-invasive respiratory monitoring. The algorithm uses normalised cut to segment each image into clusters, followed by two fuzzy inference systems to detect the nappy and torso. Our dataset comprised overhead images of 16 preterm infants in a NICU, with uncontrolled illumination, and encompassing variations in poses, presence of medical equipment and clutter in the background. The algorithm successfully identified the torso region for 15 of the 16 images, with a high agreement between the detected torso and the torso identified by clinical experts.


Assuntos
Tronco , Algoritmos , Humanos , Recém-Nascido , Recém-Nascido Prematuro , Unidades de Terapia Intensiva Neonatal
3.
Neonatology ; 109(1): 37-43, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26554825

RESUMO

BACKGROUND: Oxygen saturation (SpO2) targeting in the preterm infant may be improved with a better understanding of the SpO2 responses to changes in inspired oxygen (FiO2). OBJECTIVE: We investigated the first-order FiO2-SpO2 relationship, aiming to quantify the parameters governing that relationship, the influences on these parameters and their variability. METHODS: In recordings of FiO2 and SpO2 from preterm infants on continuous positive airway pressure and supplemental oxygen, we identified unique FiO2 adjustments and mapped the subsequent SpO2 responses. For responses identified as first-order, the delay, time constant and gain parameters were determined. Clinical and physiological predictors of these parameters were sought in regression analysis, and intra- and inter-subject variability was evaluated. RESULTS: In 3,788 h of available data from 47 infants at 31 (28-33) post-menstrual weeks [median (interquartile range)], we identified 993 unique FiO2 adjustments followed by a first-order SpO2 response. All response parameters differed between FiO2 increments and decrements, with increments having a shorter delay, longer time constant and higher gain [2.9 (1.7-4.8) vs. 1.3 (0.58-2.6), p < 0.05]. Gain was also higher in less mature infants and in the setting of recent SpO2 instability, and was diminished with increasing severity of lung dysfunction. Intra-subject variability in all parameters was prominent. CONCLUSIONS: First-order SpO2 responses show variable gain, influenced by the direction of FiO2 adjustment and the severity of lung disease, as well as substantial intra-subject parameter variability. These findings should be taken into account in adjustment of FiO2 for SpO2 targeting in preterm infants.


Assuntos
Pressão Positiva Contínua nas Vias Aéreas/métodos , Recém-Nascido Prematuro , Oximetria/métodos , Oxigenoterapia/métodos , Oxigênio/análise , Humanos , Recém-Nascido
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