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A Universal Scaling Relation for Defining Power Spectral Bands in Mammalian Heart Rate Variability Analysis

Overview of attention for article published in Frontiers in Physiology, August 2018
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Title
A Universal Scaling Relation for Defining Power Spectral Bands in Mammalian Heart Rate Variability Analysis
Published in
Frontiers in Physiology, August 2018
DOI 10.3389/fphys.2018.01001
Pubmed ID
Authors

Joachim A. Behar, Aviv A. Rosenberg, Ori Shemla, Kevin R. Murphy, Gideon Koren, George E. Billman, Yael Yaniv

Abstract

Background: Power spectral density (PSD) analysis of the heartbeat intervals in the three main frequency bands [very low frequency (VLF), low frequency (LF), and high frequency (HF)] provides a quantitative non-invasive tool for assessing the function of the cardiovascular control system. In humans, these frequency bands were standardized following years of empirical evidence. However, no quantitative approach has justified the frequency cutoffs of these bands and how they might be adapted to other mammals. Defining mammal-specific frequency bands is necessary if the PSD analysis of the HR is to be used as a proxy for measuring the autonomic nervous system activity in animal models. Methods: We first describe the distribution of prominent frequency peaks found in the normalized PSD of mammalian data using a Gaussian mixture model while assuming three components corresponding to the traditional VLF, LF and HF bands. We trained the algorithm on a database of human electrocardiogram recordings (n = 18) and validated it on databases of dogs (n = 17) and mice (n = 8). Finally, we tested it to predict the bands for rabbits (n = 4) for the first time. Results: Double-logarithmic analysis demonstrates a scaling law between the GMM-identified cutoff frequencies and the typical heart rate (HRm): fVLF-LF = 0.0037⋅ HR m 0.58 , fLF-HF = 0.0017⋅ HR m 1.01 and fHFup = 0.0128⋅ HR m 0.86 . We found that the band cutoff frequencies and Gaussian mean scale with a power law of 1/4 or 1/8 of the typical body mass (BMm), thus revealing allometric power laws. Conclusion: Our automated data-driven approach allowed us to define the frequency bands in PSD analysis of beat-to-beat time series from different mammals. The scaling law between the band frequency cutoffs and the HRm can be used to approximate the PSD bands in other mammals.

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Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Professor 6 17%
Student > Bachelor 5 14%
Professor > Associate Professor 4 11%
Researcher 3 8%
Other 2 6%
Other 8 22%
Unknown 8 22%
Readers by discipline Count As %
Engineering 6 17%
Medicine and Dentistry 5 14%
Veterinary Science and Veterinary Medicine 4 11%
Agricultural and Biological Sciences 3 8%
Computer Science 2 6%
Other 4 11%
Unknown 12 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 07 September 2018.
All research outputs
#18,647,094
of 23,100,534 outputs
Outputs from Frontiers in Physiology
#8,270
of 13,847 outputs
Outputs of similar age
#254,592
of 331,125 outputs
Outputs of similar age from Frontiers in Physiology
#336
of 476 outputs
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