Application of linear and nonlinear time series modeling to heart rate dynamics analysis

• Published in: IEEE transactions on biomedical engineering 1995-04, Vol.42 (4), p.411-415
• Main Authors: Christini, D.J., Bennett, F.H., Lutchen, K.R., Ahmed, H.M., Hausdorff, J.M., Oriol, N.
• Format: Article
• Language: English
• Subjects: Adult; Aerospace electronics; Aged; Bias; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Electrocardiography; Evaluation Studies as Topic; Female; Heart Rate; Humans; Linear Models; Male; Medical sciences; Middle Aged; Models and simulation; Models, Cardiovascular; Nonlinear dynamical systems; Parametric statistics; Polynomials; Predictive models; Predictive Value of Tests; Reproducibility of Results; Signal processing; Signal Processing, Computer-Assisted; Statistical distributions; Time series analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.376135

The linear autoregressive (AR) model is often used to investigate the pathophysiologic mechanisms controlling heart rate (HR) dynamics. This study implemented parametric models new to this field to determine if a more appropriate HR dynamics modeling structure exists. The linear AR and autoregressive-moving average (ARMA) models, and the nonlinear polynomial autoregressive (PAR) and bilinear (BL) models were fit to instantaneous HR time series obtained from nine subjects in the supine position. Model orders were determined by the Akaike Information Criteria (AIC). Model residual variance was used as the primary intermodel comparison criterion, with significance evaluated by a /spl lambda//sup 2/ distributed statistic. The BL model best represented the HR dynamics, as its residual variance was significantly (p