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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Robust features for the automatic identification of autism spectrum disorder in children
|
|---|---|
| Published in |
Journal of Neurodevelopmental Disorders, May 2014
|
| DOI | 10.1186/1866-1955-6-12 |
| Pubmed ID | |
| Authors |
Justin Eldridge, Alison E Lane, Mikhail Belkin, Simon Dennis |
| Abstract |
It is commonly reported that children with autism spectrum disorder (ASD) exhibit hyper-reactivity or hypo-reactivity to sensory stimuli. Electroencephalography (EEG) is commonly used to study neural sensory reactivity, suggesting that statistical analysis of EEG recordings is a potential means of automatic classification of the disorder. EEG recordings taken from children, however, are frequently contaminated with large amounts of noise, making analysis difficult. In this paper, we present a method for the automatic extraction of noise-robust EEG features, which serve to quantify neural sensory reactivity. We show the efficacy of a system for the classification of ASD using these features. |
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 | 4 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Israel | 1 | 1% |
| United States | 1 | 1% |
| Egypt | 1 | 1% |
| Unknown | 75 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 17% |
| Researcher | 12 | 15% |
| Student > Ph. D. Student | 10 | 13% |
| Student > Doctoral Student | 6 | 8% |
| Student > Postgraduate | 6 | 8% |
| Other | 15 | 19% |
| Unknown | 16 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 13 | 17% |
| Medicine and Dentistry | 9 | 12% |
| Neuroscience | 7 | 9% |
| Computer Science | 6 | 8% |
| Agricultural and Biological Sciences | 5 | 6% |
| Other | 17 | 22% |
| Unknown | 21 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 18 June 2014.
All research outputs
#7,953,379
of 25,654,806 outputs
Outputs from Journal of Neurodevelopmental Disorders
#282
of 514 outputs
Outputs of similar age
#71,471
of 240,543 outputs
Outputs of similar age from Journal of Neurodevelopmental Disorders
#4
of 8 outputs
Altmetric has tracked 25,654,806 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 514 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.0. This one is in the 44th percentile – i.e., 44% 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 240,543 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.