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Mendeley readers
Attention Score in Context
| Title |
On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress
|
|---|---|
| Published in |
Sensors, September 2015
|
| DOI | 10.3390/s151024716 |
| Pubmed ID | |
| Authors |
Mohamed Elgendi, Rich Fletcher, Ian Norton, Matt Brearley, Derek Abbott, Nigel H. Lovell, Dale Schuurmans |
| 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%. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 107 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 20 | 19% |
| Student > Master | 18 | 17% |
| Student > Ph. D. Student | 14 | 13% |
| Student > Doctoral Student | 6 | 6% |
| Student > Bachelor | 6 | 6% |
| Other | 23 | 21% |
| Unknown | 20 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 35 | 33% |
| Medicine and Dentistry | 11 | 10% |
| Computer Science | 9 | 8% |
| Nursing and Health Professions | 6 | 6% |
| Psychology | 5 | 5% |
| Other | 14 | 13% |
| Unknown | 27 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 September 2015.
All research outputs
#17,285,036
of 25,373,627 outputs
Outputs from Sensors
#10,796
of 24,303 outputs
Outputs of similar age
#171,312
of 286,220 outputs
Outputs of similar age from Sensors
#121
of 175 outputs
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