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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Normalizing Electrocardiograms of Both Healthy Persons and Cardiovascular Disease Patients for Biometric Authentication
|
|---|---|
| Published in |
PLOS ONE, August 2013
|
| DOI | 10.1371/journal.pone.0071523 |
| Pubmed ID | |
| Authors |
Meixue Yang, Bin Liu, Miaomiao Zhao, Fan Li, Guoqing Wang, Fengfeng Zhou |
| Abstract |
Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, which are comparable between any two ECGs, no matter the sampling rates or health status. The overall accuracies reach 100% and 90.11% for healthy participants and cardiovascular disease (CVD) patients, respectively. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 28% |
| Student > Bachelor | 4 | 16% |
| Researcher | 3 | 12% |
| Student > Doctoral Student | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 8 | 32% |
| Computer Science | 5 | 20% |
| Medicine and Dentistry | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 5 | 20% |
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 02 March 2016.
All research outputs
#6,927,901
of 22,716,996 outputs
Outputs from PLOS ONE
#81,689
of 193,928 outputs
Outputs of similar age
#60,083
of 198,610 outputs
Outputs of similar age from PLOS ONE
#1,750
of 4,726 outputs
Altmetric has tracked 22,716,996 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 193,928 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has gotten more attention than average, scoring higher than 56% 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 198,610 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 68% of its contemporaries.
We're also able to compare this research output to 4,726 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.