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On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress

Overview of attention for article published in Sensors, September 2015
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Mentioned by

twitter
1 tweeter

Citations

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13 Dimensions

Readers on

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100 Mendeley
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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 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%.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 18%
Student > Master 18 18%
Student > Ph. D. Student 13 13%
Student > Postgraduate 6 6%
Student > Doctoral Student 6 6%
Other 23 23%
Unknown 16 16%
Readers by discipline Count As %
Engineering 33 33%
Medicine and Dentistry 10 10%
Computer Science 9 9%
Nursing and Health Professions 6 6%
Psychology 5 5%
Other 15 15%
Unknown 22 22%

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
#13,709,317
of 20,568,640 outputs
Outputs from Sensors
#7,226
of 14,937 outputs
Outputs of similar age
#149,992
of 263,102 outputs
Outputs of similar age from Sensors
#66
of 123 outputs
Altmetric has tracked 20,568,640 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,937 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,102 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.