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Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

Overview of attention for article published in BioMedical Engineering OnLine, May 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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4 X users
patent
1 patent
facebook
1 Facebook page

Citations

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93 Dimensions

Readers on

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228 Mendeley
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Title
Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst
Published in
BioMedical Engineering OnLine, May 2013
DOI 10.1186/1475-925x-12-44
Pubmed ID
Authors

Jerritta Selvaraj, Murugappan Murugappan, Khairunizam Wan, Sazali Yaacob

Abstract

Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 228 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Indonesia 1 <1%
Austria 1 <1%
Argentina 1 <1%
Qatar 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 221 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 21%
Student > Master 41 18%
Student > Bachelor 27 12%
Researcher 21 9%
Student > Doctoral Student 15 7%
Other 37 16%
Unknown 38 17%
Readers by discipline Count As %
Engineering 63 28%
Computer Science 42 18%
Psychology 26 11%
Neuroscience 12 5%
Medicine and Dentistry 11 5%
Other 28 12%
Unknown 46 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 27 November 2020.
All research outputs
#6,374,015
of 25,374,917 outputs
Outputs from BioMedical Engineering OnLine
#155
of 867 outputs
Outputs of similar age
#50,913
of 207,266 outputs
Outputs of similar age from BioMedical Engineering OnLine
#2
of 20 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 867 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 82% of its peers.
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 207,266 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.