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SinusCor: an advanced tool for heart rate variability analysis

Overview of attention for article published in BioMedical Engineering OnLine, September 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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13 X users

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30 Dimensions

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85 Mendeley
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Title
SinusCor: an advanced tool for heart rate variability analysis
Published in
BioMedical Engineering OnLine, September 2017
DOI 10.1186/s12938-017-0401-4
Pubmed ID
Authors

Rhenan Bartels, Leonardo Neumamm, Tiago Peçanha, Alysson Roncally Silva Carvalho

Abstract

Heart rate variability (HRV) is a widespread non-invasive technique to assess cardiac autonomic function. Time and frequency domain analyses have been used in HRV studies, and their interpretations are linked with both clinical prognostic and diagnostic information. Statistical and geometrical parameters, Fast Fourier Transform and Autoregressive based periodograms are commonly used approaches for the assessment of stationary RR intervals (RRi) signals. However, some conditions result in non-stationary HRV behavior such as the "tilt test" and exercise. This study presents the SinusCor, a new free software for HRV analysis that includes the classical time and frequency domain indices and also techniques for non-stationary data analyses in both time (i.e. root mean squared of successive differences; RMSSD calculated with moving segments) and frequency domains (i.e. time-frequency analysis). An example of RRi was acquired from a young male subject and its time and frequency domain indices were calculated. Time-varying and time-frequency analyses were also presented using the RMSSD and total power, respectively. Validation of the present software against a standard software for HRV analysis (Kubios v 3.0.1) was also performed [SinusCor vs. Kubios: RMSSD-93.96 (41.55) vs. 93.96 (41.55) ms; SDNN-101.29 (29.03) vs. 101.29 (29.03) ms; LF-50.42 (19.76) vs. 50.56 (19.56) n.u.; HF-49.57 (19.76) vs. 49.38 (19.56) n.u.; LF/HF-1.38 (1.08) vs. 1.38 (1.07)]. SinusCor might be a useful tool for classical stationary and non-stationary HRV analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 85 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 15%
Student > Master 11 13%
Student > Ph. D. Student 10 12%
Student > Doctoral Student 7 8%
Student > Bachelor 7 8%
Other 18 21%
Unknown 19 22%
Readers by discipline Count As %
Engineering 13 15%
Computer Science 10 12%
Sports and Recreations 8 9%
Medicine and Dentistry 8 9%
Psychology 4 5%
Other 20 24%
Unknown 22 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 03 December 2020.
All research outputs
#4,051,327
of 23,002,898 outputs
Outputs from BioMedical Engineering OnLine
#94
of 824 outputs
Outputs of similar age
#72,183
of 318,311 outputs
Outputs of similar age from BioMedical Engineering OnLine
#5
of 20 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 88% 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 318,311 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.