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Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians
The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monito...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4744257/ https://www.ncbi.nlm.nih.gov/pubmed/25804608 http://dx.doi.org/10.1007/s10877-015-9673-z |
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author | Mort, Alasdair J. Fitzpatrick, David Wilson, Philip M. J. Mellish, Chris Schneider, Anne |
author_facet | Mort, Alasdair J. Fitzpatrick, David Wilson, Philip M. J. Mellish, Chris Schneider, Anne |
author_sort | Mort, Alasdair J. |
collection | PubMed |
description | The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth(®) Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO(2) trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1–50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO(2) error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error. |
format | Online Article Text |
id | pubmed-4744257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-47442572016-02-16 Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians Mort, Alasdair J. Fitzpatrick, David Wilson, Philip M. J. Mellish, Chris Schneider, Anne J Clin Monit Comput Original Research The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth(®) Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO(2) trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1–50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO(2) error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error. Springer Netherlands 2015-03-25 2016 /pmc/articles/PMC4744257/ /pubmed/25804608 http://dx.doi.org/10.1007/s10877-015-9673-z Text en © The Author(s) 2015 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Original Research Mort, Alasdair J. Fitzpatrick, David Wilson, Philip M. J. Mellish, Chris Schneider, Anne Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
title | Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
title_full | Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
title_fullStr | Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
title_full_unstemmed | Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
title_short | Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
title_sort | lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4744257/ https://www.ncbi.nlm.nih.gov/pubmed/25804608 http://dx.doi.org/10.1007/s10877-015-9673-z |
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