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Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors

Sornmo, Leif LU ; Bailon, Raquel and Laguna, Pablo (2022) In IEEE Reviews in Biomedical Engineering p.1-21
Abstract

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from... (More)

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
in press
subject
keywords
Analytical models, confounding factors, Data models, heart rate variability, Heart rate variability, Mathematical models, Physiology, redefinition of frequency bands, Resonant frequency, respiration-guided decomposition, spectral analysis, Spectral analysis, time-varying analysis
in
IEEE Reviews in Biomedical Engineering
pages
21 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85141608012
  • pmid:36346854
ISSN
1937-3333
DOI
10.1109/RBME.2022.3220636
language
English
LU publication?
yes
id
c142fbf9-e202-412c-a659-3734f3f8a444
date added to LUP
2022-12-06 15:02:19
date last changed
2024-06-13 21:12:54
@article{c142fbf9-e202-412c-a659-3734f3f8a444,
  abstract     = {{<p>The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time&amp;#x2013;frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.</p>}},
  author       = {{Sornmo, Leif and Bailon, Raquel and Laguna, Pablo}},
  issn         = {{1937-3333}},
  keywords     = {{Analytical models; confounding factors; Data models; heart rate variability; Heart rate variability; Mathematical models; Physiology; redefinition of frequency bands; Resonant frequency; respiration-guided decomposition; spectral analysis; Spectral analysis; time-varying analysis}},
  language     = {{eng}},
  pages        = {{1--21}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Reviews in Biomedical Engineering}},
  title        = {{Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors}},
  url          = {{http://dx.doi.org/10.1109/RBME.2022.3220636}},
  doi          = {{10.1109/RBME.2022.3220636}},
  year         = {{2022}},
}