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Attention Score in Context
| Title |
Windowed Multivariate Autoregressive Model improving Classification of Labor vs. Pregnancy Contractions
|
|---|---|
| Published in |
Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, January 2013
|
| DOI | 10.1109/embc.2013.6611279 |
| Pubmed ID | |
| Authors |
Brynjar Karlsson, Mahmoud Hassan, Catherine Marque |
| Abstract |
Analyzing the propagation of uterine electrical activity is poised to become a powerful tool in labor detection and for the prediction of preterm labor. Several methods have been proposed to investigate the relationship between signals recorded externally from several sites on the pregnant uterus. A promising recent method is the multivariate autoregressive (MVAR) model. In this paper we proposed a windowed (time varying) version of the multivariate autoregressive model, called W-MVAR, to investigate the connectivity between signals while still respecting their non-stationary characteristics. The proposed method was tested on synthetic signals as well as applied to real signals. The comparison between the two methods on synthetic signals showed the superiority of W-MVAR to detect connectivity even if it is non-stationary. The application of W-MVAR on multichannel real uterine signals show that the proposed method is a good tool to distinguish non-labor and labor signals. These results are very promising and can very possibly have important clinical applications in labor detection and preterm labor prediction. |
X Demographics
The data shown below were collected from the profile of 1 X user 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 4% |
| Unknown | 27 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 25% |
| Student > Master | 5 | 18% |
| Student > Doctoral Student | 4 | 14% |
| Researcher | 3 | 11% |
| Professor | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 6 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 10 | 36% |
| Medicine and Dentistry | 6 | 21% |
| Computer Science | 3 | 11% |
| Neuroscience | 1 | 4% |
| Unknown | 8 | 29% |
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 11 October 2013.
All research outputs
#22,764,772
of 25,377,790 outputs
Outputs from Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#3,438
of 4,376 outputs
Outputs of similar age
#258,432
of 289,014 outputs
Outputs of similar age from Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#242
of 281 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,376 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 281 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.