Many Researchers have widely investigated developing a mathematical model for generating artificial electrocardiogram (ECG signals. In this paper we present a new comprehensive model for artificial ECG generation. Using a new neural network approach in a nonlinear dynamical system provides the ability of generating a wide range of ECG signals In addition, using the Integral Pulse Frequency Mo dulator (IPFM) model incorporates the effects of sympathetic and parasympathetic activities in simulating the heart rate variability (HRV) signals The inter-coupling between sympathetic and parasympathetic systems is also included
