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|Title:||On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress.|
Lovell, Nigel H
|Affiliation:||Electrical and Computer Engineering in Medicine Group, University of British Columbia and BC Children's Hospital, Vancouver, BC V6H 3N1, Canada. firstname.lastname@example.org.. Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. email@example.com..|
Media Lab, Massachusetts Institute of Technology, Boston, MA 02139, USA. firstname.lastname@example.org..
National Critical Care and Trauma Response Centre, Darwin, NT 0810, Australia. email@example.com..
National Critical Care and Trauma Response Centre, Darwin, NT 0810, Australia. firstname.lastname@example.org..
School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia. email@example.com..
Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia. firstname.lastname@example.org..
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. email@example.com..
|Citation:||Sensors (Basel, Switzerland) 2015-09-25; 15(10): 24716-34|
|Abstract:||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 Sensors 2015, 15 24717 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%.|
Heat Stress Disorders
Signal Processing, Computer-Assisted
|Appears in Collections:||NT Health digital library|
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