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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using n-gram analysis to cluster heartbeat signals
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2012
|
| DOI | 10.1186/1472-6947-12-64 |
| Pubmed ID | |
| Authors |
Yu-Chen Huang, Hanjun Lin, Yeh-Liang Hsu, Jun-Lin Lin |
| Abstract |
Biological signals may carry specific characteristics that reflect basic dynamics of the body. In particular, heart beat signals carry specific signatures that are related to human physiologic mechanisms. In recent years, many researchers have shown that representations which used non-linear symbolic sequences can often reveal much hidden dynamic information. This kind of symbolization proved to be useful for predicting life-threatening cardiac diseases. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 67% |
| United States | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 12% |
| Professor > Associate Professor | 2 | 12% |
| Student > Bachelor | 2 | 12% |
| Student > Master | 2 | 12% |
| Student > Doctoral Student | 1 | 6% |
| Other | 5 | 29% |
| Unknown | 3 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 29% |
| Computer Science | 4 | 24% |
| Nursing and Health Professions | 1 | 6% |
| Mathematics | 1 | 6% |
| Business, Management and Accounting | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 4 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 10 July 2012.
All research outputs
#14,147,011
of 22,669,724 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,101
of 1,978 outputs
Outputs of similar age
#96,973
of 164,716 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#31
of 49 outputs
Altmetric has tracked 22,669,724 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% 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 164,716 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.