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Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identifi...

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Autores principales: Villarroel, Mauricio, Chaichulee, Sitthichok, Jorge, João, Davis, Sara, Green, Gabrielle, Arteta, Carlos, Zisserman, Andrew, McCormick, Kenny, Watkinson, Peter, Tarassenko, Lionel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6908711/
https://www.ncbi.nlm.nih.gov/pubmed/31872068
http://dx.doi.org/10.1038/s41746-019-0199-5
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author Villarroel, Mauricio
Chaichulee, Sitthichok
Jorge, João
Davis, Sara
Green, Gabrielle
Arteta, Carlos
Zisserman, Andrew
McCormick, Kenny
Watkinson, Peter
Tarassenko, Lionel
author_facet Villarroel, Mauricio
Chaichulee, Sitthichok
Jorge, João
Davis, Sara
Green, Gabrielle
Arteta, Carlos
Zisserman, Andrew
McCormick, Kenny
Watkinson, Peter
Tarassenko, Lionel
author_sort Villarroel, Mauricio
collection PubMed
description The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.
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spelling pubmed-69087112019-12-23 Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit Villarroel, Mauricio Chaichulee, Sitthichok Jorge, João Davis, Sara Green, Gabrielle Arteta, Carlos Zisserman, Andrew McCormick, Kenny Watkinson, Peter Tarassenko, Lionel NPJ Digit Med Article The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed. Nature Publishing Group UK 2019-12-12 /pmc/articles/PMC6908711/ /pubmed/31872068 http://dx.doi.org/10.1038/s41746-019-0199-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Villarroel, Mauricio
Chaichulee, Sitthichok
Jorge, João
Davis, Sara
Green, Gabrielle
Arteta, Carlos
Zisserman, Andrew
McCormick, Kenny
Watkinson, Peter
Tarassenko, Lionel
Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
title Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
title_full Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
title_fullStr Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
title_full_unstemmed Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
title_short Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
title_sort non-contact physiological monitoring of preterm infants in the neonatal intensive care unit
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6908711/
https://www.ncbi.nlm.nih.gov/pubmed/31872068
http://dx.doi.org/10.1038/s41746-019-0199-5
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