Uncertainty of AR spectral estimates

D. J. Christini*, A. Kulkarni, S. Rao, E. R. Stutman, F. M. Bennett, J. M. Hausdorff, N. Oriol, K. R. Lutchen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The statistical uncertainty of autoregressive (AR) model heart rate (HR) power spectra was investigated. HR time series, obtained from 9 subjects in supine and standing positions, were fit to AR models by least squares minimization via singular value decomposition (SVD). AR spectral uncertainty due to inexact parameter estimation was assessed in a Monte Carlo study. For each of 50,000 spectral realizations, all AR parameters were varied randomly within 1 standard deviation of their SVD estimated values. Histogram techniques were used to evaluate the resulting distribution of spectral estimates. It was determined that the uncertainty of HR AR spectral estimates can be quite high, especially at the locations of spectral peaks. Thus, AR spectra may be unreliable and assigning physiological origins to specific spectral features may be inappropriate.

Original languageEnglish
Title of host publicationComputers in Cardiology
PublisherPubl by IEEE
Number of pages4
ISBN (Print)0818654708
StatePublished - 1993
Externally publishedYes
EventProceedings of the 1993 Conference on Computers in Cardiology - London, UK
Duration: 5 Sep 19938 Sep 1993

Publication series

NameComputers in Cardiology
ISSN (Print)0276-6574


ConferenceProceedings of the 1993 Conference on Computers in Cardiology
CityLondon, UK


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