Time series modeling of heart rate dynamics

F. M. Bennett*, D. J. Christini, H. Ahmed, K. Lutchen, J. M. Hausdorff, N. Oriol

*Corresponding author for this work

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

Abstract

Autoregressive (AR), autoregressive-moving average (ARMA), bilinear (BL), and polynomial autoregressive (PAR) models were fit to heart rate time series obtained from 9 subjects in the supine position. For each data set and model structure, model order was determined by the Akaike Information Criteria (AIC). For all data sets, the nonlinear BL model had a lower residual variance and AIC compared to AR models. In most cases, BL models provided a better fit to the data than either ARMA or PAR models. For most data sets, the nonlinear BL model provides a more accurate representation of HR dynamics compared to the other model structures tested.

Original languageEnglish
Title of host publicationComputers in Cardiology
PublisherPubl by IEEE
Pages273-276
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

Conference

ConferenceProceedings of the 1993 Conference on Computers in Cardiology
CityLondon, UK
Period5/09/938/09/93

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