A New IPFM Based Model For Artifitial Generating Of HRV With Random Input

The heart rate variability (HRV) signal is a useful and non-invasive tool for the evaluation of cardiovascular system and Autonomic Nervous System. Many researcher tried to model this signal. There are many different approaches for this modelling. One of the most frequently used method is Integral Pulse Frequency Modulator (IPFM) model. In IPFM model there is an input signal which is fed to an integrator and when the output of the integrator reaches to a threshold then we have a pulse. This pulse simulates the R-wave of the ECG signal. The source of ECG is sinoatrial (SA) node. SA node is affected by many factors. The power spectrum of HRV signal is divided into three main bands i.e. VLF, LF and HF. The information in these three bands shows how the Autonomic Nervous System (ANS) affects on heart activity. The ratio LF/HF is related to sampatho-vagal balance. The proposed model generates both normal HRV and HRV of some abnormalities with regard to ANS. As we used random signal in the input of the IPFM the output of the model has random variations and the output never be as a periodic signal. Our model is very similar to real HRV than a normal IPFM. In our previous work we added the random signal to threshold and now we apply the random signal to the main input. We used Guassian random signal with zero mean with variance equals to 1.