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Attention Score in Context
| Title |
Inclusion of temporal priors for automated neonatal EEG classification
|
|---|---|
| Published in |
Journal of Neural Engineering, June 2012
|
| DOI | 10.1088/1741-2560/9/4/046002 |
| Pubmed ID | |
| Authors |
Andriy Temko, Nathan Stevenson, William Marnane, Geraldine Boylan, Gordon Lightbody |
| Abstract |
The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 h. By exploiting these priors, the receiver operating characteristic area is increased by 23% (relative) reaching 96.74%. The number of false detections per hour is decreased from 0.45 to 0.25, while maintaining the correct detection of seizure burden at 70%. |
X Demographics
The data shown below were collected from the profiles of 5 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 States | 1 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Unknown | 23 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 25% |
| Student > Ph. D. Student | 4 | 17% |
| Student > Doctoral Student | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Other | 3 | 13% |
| Unknown | 5 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 10 | 42% |
| Neuroscience | 3 | 13% |
| Medicine and Dentistry | 3 | 13% |
| Psychology | 1 | 4% |
| Unknown | 7 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 20 June 2012.
All research outputs
#7,414,160
of 22,668,244 outputs
Outputs from Journal of Neural Engineering
#652
of 1,597 outputs
Outputs of similar age
#54,571
of 164,469 outputs
Outputs of similar age from Journal of Neural Engineering
#21
of 38 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,597 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.