| Title |
Cardiac Rhythm Device Identification Using Neural Networks
|
|---|---|
| Published in |
JACC: Clinical Electrophysiology, March 2019
|
| DOI | 10.1016/j.jacep.2019.02.003 |
| Pubmed ID | |
| Authors |
James P. Howard, Louis Fisher, Matthew J. Shun-Shin, Daniel Keene, Ahran D. Arnold, Yousif Ahmad, Christopher M. Cook, James C. Moon, Charlotte H. Manisty, Zach I. Whinnett, Graham D. Cole, Daniel Rueckert, Darrel P. Francis |
X Demographics
The data shown below were collected from the profiles of 94 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 | 25 | 27% |
| United Kingdom | 24 | 26% |
| Spain | 4 | 4% |
| Australia | 3 | 3% |
| Canada | 3 | 3% |
| Germany | 2 | 2% |
| India | 2 | 2% |
| Switzerland | 2 | 2% |
| Belgium | 1 | 1% |
| Other | 4 | 4% |
| Unknown | 24 | 26% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 55 | 59% |
| Practitioners (doctors, other healthcare professionals) | 21 | 22% |
| Scientists | 13 | 14% |
| Science communicators (journalists, bloggers, editors) | 5 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 86 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 14% |
| Other | 8 | 9% |
| Researcher | 8 | 9% |
| Student > Master | 8 | 9% |
| Student > Bachelor | 8 | 9% |
| Other | 15 | 17% |
| Unknown | 27 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 31 | 36% |
| Engineering | 10 | 12% |
| Computer Science | 9 | 10% |
| Nursing and Health Professions | 3 | 3% |
| Mathematics | 1 | 1% |
| Other | 4 | 5% |
| Unknown | 28 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 102. 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 29 December 2022.
All research outputs
#422,909
of 25,713,737 outputs
Outputs from JACC: Clinical Electrophysiology
#42
of 1,576 outputs
Outputs of similar age
#9,460
of 365,945 outputs
Outputs of similar age from JACC: Clinical Electrophysiology
#1
of 53 outputs
Altmetric has tracked 25,713,737 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,576 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one has done particularly well, scoring higher than 97% 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 365,945 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 53 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 98% of its contemporaries.