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Individual identification via electrocardiogram analysis

Overview of attention for article published in BioMedical Engineering OnLine, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#15 of 813)
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
3 tweeters
patent
2 patents

Citations

dimensions_citation
88 Dimensions

Readers on

mendeley
142 Mendeley
citeulike
1 CiteULike
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Title
Individual identification via electrocardiogram analysis
Published in
BioMedical Engineering OnLine, August 2015
DOI 10.1186/s12938-015-0072-y
Pubmed ID
Authors

Antonio Fratini, Mario Sansone, Paolo Bifulco, Mario Cesarelli

Abstract

During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Korea, Republic of 1 <1%
Romania 1 <1%
Unknown 139 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 32 23%
Student > Ph. D. Student 18 13%
Student > Bachelor 18 13%
Researcher 12 8%
Professor > Associate Professor 7 5%
Other 25 18%
Unknown 30 21%
Readers by discipline Count As %
Engineering 41 29%
Computer Science 32 23%
Medicine and Dentistry 12 8%
Agricultural and Biological Sciences 3 2%
Nursing and Health Professions 3 2%
Other 15 11%
Unknown 36 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 28 July 2022.
All research outputs
#1,168,683
of 22,343,111 outputs
Outputs from BioMedical Engineering OnLine
#15
of 813 outputs
Outputs of similar age
#16,185
of 251,581 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 3 outputs
Altmetric has tracked 22,343,111 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 813 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 98% 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 251,581 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 93% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them