Simulation of long-term Heart Rate Variability records with Gaussian distribution functions

Abstract

The work presents an algorithm for mathematical modeling long-term Heart Rate Variability data using Gaussian distribution functions. The representation of the cardiac series in time domain is performed using an inverse wavelet transform. The transform is implemented with different Daubechies wavelet bases and is compared with the implementation of the algorithm with the classical Fourier transform. The created time sequences are analyzed in terms of the wavelet bases (Db4, Db6, Db8, Db12, Db20) used and the required CPU time for implementation of the program procedure. The obtained results show higher IT efficiency of the presented algorithm, realized with Daubechies transform.