Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians

Alasdair J. Mort, David Fitzpatrick, Philip M. J. Wilson, Chris Mellish, Anne Schneider

Research output: Contribution to journalArticle

5 Downloads (Pure)

Abstract

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 SpO2 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 SpO2 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.
Original languageEnglish
Pages (from-to)23-32
Number of pages10
JournalJournal of Clinical Monitoring and Computing
Volume30
Issue number1
Early online date25 Mar 2015
DOIs
Publication statusPublished - Feb 2016

Fingerprint

Ambulances
Respiratory Rate
Heart Rate
Fingers
Oxygen
Incidence

Keywords

  • Physiologic monitoring
  • pre-hospital
  • ambulance clinicians
  • motion artefact

ASJC Scopus subject areas

  • Physiology (medical)

Cite this

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.

In: Journal of Clinical Monitoring and Computing, Vol. 30, No. 1, 02.2016, p. 23-32.

Research output: Contribution to journalArticle

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. In: Journal of Clinical Monitoring and Computing. 2016 ; Vol. 30, No. 1. pp. 23-32.
@article{1fcae62e8b4645a5b2fe235426b9fc86,
title = "Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians",
abstract = "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{\circledR} 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 SpO2 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 SpO2 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.",
keywords = "Physiologic monitoring, pre-hospital, ambulance clinicians, motion artefact",
author = "Mort, {Alasdair J.} and David Fitzpatrick and Wilson, {Philip M. J.} and Chris Mellish and Anne Schneider",
note = "Acknowledgments We would like to extend our thanks to the Scottish Ambulance Service for granting us permission to undertake this research. We would also like to thank the ambulance clinicians who took part, and the patients. We also recognise the non-financial support given to us by the developers of the RESpeck sensor (University of Edinburgh). The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.",
year = "2016",
month = "2",
doi = "10.1007/s10877-015-9673-z",
language = "English",
volume = "30",
pages = "23--32",
journal = "Journal of Clinical Monitoring and Computing",
issn = "1387-1307",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians

AU - Mort, Alasdair J.

AU - Fitzpatrick, David

AU - Wilson, Philip M. J.

AU - Mellish, Chris

AU - Schneider, Anne

N1 - Acknowledgments We would like to extend our thanks to the Scottish Ambulance Service for granting us permission to undertake this research. We would also like to thank the ambulance clinicians who took part, and the patients. We also recognise the non-financial support given to us by the developers of the RESpeck sensor (University of Edinburgh). The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.

PY - 2016/2

Y1 - 2016/2

N2 - 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 SpO2 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 SpO2 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.

AB - 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 SpO2 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 SpO2 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.

KW - Physiologic monitoring

KW - pre-hospital

KW - ambulance clinicians

KW - motion artefact

U2 - 10.1007/s10877-015-9673-z

DO - 10.1007/s10877-015-9673-z

M3 - Article

VL - 30

SP - 23

EP - 32

JO - Journal of Clinical Monitoring and Computing

JF - Journal of Clinical Monitoring and Computing

SN - 1387-1307

IS - 1

ER -