On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress

• Published in: Sensors (Basel, Switzerland) Vol. 15; no. 10; pp. 24716 - 24734
• Main Authors: Elgendi, Mohamed, Fletcher, Rich, Norton, Ian, Brearley, Matt, Abbott, Derek, Lovell, Nigel H, Schuurmans, Dale
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 25.09.2015; MDPI AG
• Subjects: Adult; affordable healthcare; Assessments; Derivatives; Energy measurement; Energy use; Exercise - physiology; Feature extraction; Female; Fingers; Global Warming; Heart rate; Heart Rate - physiology; Heat stress; Heat Stress Disorders - diagnosis; Hot Temperature; Humans; Male; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Photoplethysmography - instrumentation; Photoplethysmography - methods; Recording; Signal Processing, Computer-Assisted - instrumentation; thermal stress; global warming; affordable healthcare; thermal stress
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s151024716

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%.