On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress

There are a limited number of studies on heat stress dynamics during exercise using the photoplethysmogram (PPG) and its second derivative (APG). However, we investigate the most suitable index from short PPG signal recordings for heat stress assessment. The APG waveform consists of a, b, c and d wa...

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Main Authors: Mohamed Elgendi, Rich Fletcher, Ian Norton, Matt Brearley, Derek Abbott, Nigel H. Lovell, Dale Schuurmans
Format: Article
Language:English
Published: MDPI AG 2015-09-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/15/10/24716
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spelling doaj-4f686178965441bf9a8f6cb4e799baba2020-11-24T23:55:28ZengMDPI AGSensors1424-82202015-09-011510247162473410.3390/s151024716s151024716On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat StressMohamed Elgendi0Rich Fletcher1Ian Norton2Matt Brearley3Derek Abbott4Nigel H. Lovell5Dale Schuurmans6Electrical and Computer Engineering in Medicine Group, University of British Columbia and BC Children's Hospital, Vancouver, BC V6H 3N1, CanadaMedia Lab, Massachusetts Institute of Technology, Boston, MA 02139, USANational Critical Care and Trauma Response Centre, Darwin, NT 0810, AustraliaNational Critical Care and Trauma Response Centre, Darwin, NT 0810, AustraliaSchool of Electrical and Electronic Engineering, University of Adelaide, Adelaide, SA 5005, AustraliaGraduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, AustraliaDepartment of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, CanadaThere are a limited number of studies on heat stress dynamics during exercise using the photoplethysmogram (PPG) and its second derivative (APG). However, we investigate the most suitable index from short PPG signal recordings for heat stress assessment. The APG waveform consists of a, b, c and d waves in systole and an e wave in diastole. Our preliminary results indicate that the use of the energy of aa area, derived from PPG signals measured from emergency responders in tropical conditions, is promising in determining the heat stress level using 20-s recordings. After examining 14 time domain features using leave-one-out cross-validation, we found that the aa energy extracted from PPG signals is the most informative feature for classifying heat-stressed subjects, with an overall accuracy of 79%. Moreover, the combination of the aa energy with the traditional heart rate variability index of heat stress (i.e., the square root of the mean of the squares of the successive aa intervals) improved the heat stress detection to an overall accuracy of 83%.http://www.mdpi.com/1424-8220/15/10/24716global warmingaffordable healthcarethermal stress
collection DOAJ
language English
format Article
sources DOAJ
author Mohamed Elgendi
Rich Fletcher
Ian Norton
Matt Brearley
Derek Abbott
Nigel H. Lovell
Dale Schuurmans
spellingShingle Mohamed Elgendi
Rich Fletcher
Ian Norton
Matt Brearley
Derek Abbott
Nigel H. Lovell
Dale Schuurmans
On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
Sensors
global warming
affordable healthcare
thermal stress
author_facet Mohamed Elgendi
Rich Fletcher
Ian Norton
Matt Brearley
Derek Abbott
Nigel H. Lovell
Dale Schuurmans
author_sort Mohamed Elgendi
title On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
title_short On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
title_full On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
title_fullStr On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
title_full_unstemmed On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
title_sort on time domain analysis of photoplethysmogram signals for monitoring heat stress
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2015-09-01
description There are a limited number of studies on heat stress dynamics during exercise using the photoplethysmogram (PPG) and its second derivative (APG). However, we investigate the most suitable index from short PPG signal recordings for heat stress assessment. The APG waveform consists of a, b, c and d waves in systole and an e wave in diastole. Our preliminary results indicate that the use of the energy of aa area, derived from PPG signals measured from emergency responders in tropical conditions, is promising in determining the heat stress level using 20-s recordings. After examining 14 time domain features using leave-one-out cross-validation, we found that the aa energy extracted from PPG signals is the most informative feature for classifying heat-stressed subjects, with an overall accuracy of 79%. Moreover, the combination of the aa energy with the traditional heart rate variability index of heat stress (i.e., the square root of the mean of the squares of the successive aa intervals) improved the heat stress detection to an overall accuracy of 83%.
topic global warming
affordable healthcare
thermal stress
url http://www.mdpi.com/1424-8220/15/10/24716
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