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Decomposition of Heart Rate Variability Spectrum into a Power-Law Function and a Residual Spectrum

Overview of attention for article published in Frontiers in Cardiovascular Medicine, June 2016
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Title
Decomposition of Heart Rate Variability Spectrum into a Power-Law Function and a Residual Spectrum
Published in
Frontiers in Cardiovascular Medicine, June 2016
DOI 10.3389/fcvm.2016.00016
Pubmed ID
Authors

Jane Kuo, Cheng-Deng Kuo

Abstract

The power spectral density (PSD) of heart rate variability (HRV) contains a power-law relationship that can be obtained by plotting the logarithm of PSD against the logarithm of frequency. The PSD of HRV can be decomposed mathematically into a power-law function and a residual HRV (rHRV) spectrum. Almost all rHRV measures are significantly smaller than their corresponding HRV measures except the normalized high-frequency power (nrHFP). The power-law function can be characterized by the slope and Y-intercept of linear regression. Almost all HRV measures except the normalized low-frequency power have significant correlations with the Y-intercept, while almost all rHRV measures except the total power [residual total power (rTP)] do not. Though some rHRV measures still correlate significantly with the age of the subjects, the rTP, high-frequency power (rHFP), nrHFP, and low-/high-frequency power ratio (rLHR) do not. In conclusion, the clinical significances of rHRV measures might be different from those of traditional HRV measures. The Y-intercept might be a better HRV measure for clinical use because it is independent of almost all rHRV measures. The rTP, rHFP, nrHFP, and rLHR might be more suitable for the study of age-independent autonomic nervous modulation of the subjects.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 33%
Other 2 22%
Student > Doctoral Student 1 11%
Student > Ph. D. Student 1 11%
Unknown 2 22%
Readers by discipline Count As %
Medicine and Dentistry 2 22%
Nursing and Health Professions 1 11%
Mathematics 1 11%
Neuroscience 1 11%
Engineering 1 11%
Other 0 0%
Unknown 3 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 July 2016.
All research outputs
#18,465,704
of 22,880,230 outputs
Outputs from Frontiers in Cardiovascular Medicine
#3,191
of 6,783 outputs
Outputs of similar age
#254,897
of 339,292 outputs
Outputs of similar age from Frontiers in Cardiovascular Medicine
#8
of 10 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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