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Mendeley readers
Attention Score in Context
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
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 25% |
Mexico | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 50% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Practitioners (doctors, other healthcare professionals) | 1 | 25% |
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
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.