摘要
A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6—7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability, in the signal.
A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6-7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability in the signal.
