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
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;
Conference_Titel :
Computers in Cardiology 1993, Proceedings.
Conference_Location :
London
Print_ISBN :
0-8186-5470-8
DOI :
10.1109/CIC.1993.378451