Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors
(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.
(Less)
- author
- Sornmo, Leif LU ; Bailon, Raquel and Laguna, Pablo
- organization
- publishing date
- 2022
- 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
- 2024-09-20 06:24:57
@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&#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}}, }