• DocumentCode
    2467754
  • Title

    Time series modeling of heart rate dynamics

  • Author

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

  • Author_Institution
    Beth Israel Hospital, Boston, MA, USA
  • fYear
    1993
  • fDate
    5-8 Sep 1993
  • Firstpage
    273
  • Lastpage
    276
  • 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
  • Keywords
    cardiology; physiological models; time series; Akaike Information Criteria; autoregressive-moving average model; bilinear model; heart rate dynamics; heart rate time series; model order; model structure; polynomial autoregressive model; residual variance; supine position; time series modeling; Heart rate; Hospitals; Least squares approximation; Nonlinear dynamical systems; Parameter estimation; Physiology; Polynomials; Predictive models; Testing; White noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computers in Cardiology 1993, Proceedings.
  • Conference_Location
    London
  • Print_ISBN
    0-8186-5470-8
  • Type

    conf

  • DOI
    10.1109/CIC.1993.378451
  • Filename
    378451