The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Quantifying Errors in Spectral Estimates of HRV Due to Beat Replacement and Resampling
Abstract
Spectral estimates of heart rate variability (HRV) often involve the use of techniques such as the fast Fourier transform (FFT), which require an evenly sampled time series. HRV is calculated from the variations in the beat-to-beat (RR) interval timing of the cardiac cycle which are inherently irregularly spaced in time. In order to produce an evenly sampled time series prior to FFT-based spectral estimation, linear or cubic spline resampling is usually employed. In this paper, by using a realistic artificial RR interval generator, interpolation and resampling is shown to result in consistent over-estimations of the power spectral density (PSD) compared with the theoretical solution. The Lomb-Scargle (LS) periodogram, a more appropriate spectral estimation technique for unevenly sampled time series that uses only the original data, is shown to provide a superior PSD estimate. Ectopy removal or replacement is shown to be essential regardless of the spectral estimation technique. Resampling and phantom beat replacement is shown to decrease the accuracy of PSD estimation, even at low levels of ectopy or artefact. A linear relationship between the frequency of ectopy/artefact and the error (mean and variance) of the PSD estimate is demonstrated. Comparisons of PSD estimation techniques performed on real RR interval data during minimally active segments (sleep) demonstrate that the LS periodogram provides a less noisy spectral estimate of HRV.
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Citations
Cites
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
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Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
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On the use of windows for harmonic analysis with the discrete Fourier transform
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Least-squares frequency analysis of unequally spaced data
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A dynamical model for generating synthetic electrocardiogram signals
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Self-citationAnalysis of Heart Rate Variability
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An Efficient Algorithm for Spectral Analysis of Heart Rate Variability
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Cited by
Quintana, D. S., Alvares, G. A., & Heathers, J. A. J. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.73 · Cited by 420
Weippert, M., Kumar, M., Kreuzfeld, S., Arndt, D., Rieger, A., & Stoll, R. (2010) · European Journal of Applied Physiology · doi:10.1007/s00421-010-1415-9 · Cited by 300
ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis.
Kaufmann, T., Sütterlin, S., Schulz, S. M., & Vögele, C. (2011) · Behavior Research Methods · doi:10.3758/s13428-011-0107-7 · Cited by 284
On the validity of using the Polar RS800 heart rate monitor for heart rate variability research
Quintana, D. S., Heathers, J. A. J., & Kemp, A. H. (2012) · European Journal of Applied Physiology · doi:10.1007/s00421-012-2453-2 · Cited by 118
Ellis, R. J., Zhu, B., Koenig, J., Thayer, J. F., & Wang, Y. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/9/1827 · Cited by 85
Metadata
- Authors
- Clifford, G. D., & Tarassenko, L.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2005-04
- Volume
- 52
- Issue
- 4
- Pages
- 630-638
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2120599197
- Cited by
- 285
- References
- 29
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- closed
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