Jafarnia Dabanloo - Azad University IRAN,
Mclernon - University of Leeds UK
Ayatollahi - lran University of Science and Technology IRAN
Zhang - University of Manchester UK

The time intewals berween two sttccessive Rwaves (the largest amplitude of the normal ECG) give the RR tachogram, which contains very important information about the dynamic response of the cardiovascular control system to changes of physiological conditions. Devising a mathematical model to produce this RR tachogram time series (i.e. an artificial ECG signal generator) has attracted consi der a b I e int er ests r ec ently. In a previotts work flJ, we developed a new nonlinear model for an ECG generator, which sttccessftilly produced the RRtachogram and the actions ofthe intercoupling between sympathetic and parasympathetic systems. In this present study, we upgrade our previous model by considering a new stochastic process in order to improve its performance in generating the heart rate variability (HRV). The modified model is able to produce RR-interval time series with power spectrum hrming very low frequency and Mayer waves components, as well as the respiration sinus arrlrythmia (RSA) component. In addition, stochastic behqvior in the simulated HRV signal was also observed, which is similar to that observed in real clinical recordings.

ECG, HRV, Integral Pulse Frequency Model, dynamical model