SIMULATION MODELING OF THE PULSE WAVE DURING PHYSICAL LOAD AS A MEANS OF VERIFYING SIGNAL PROCESSING METHODS

Abstract

The article presents a mathematical and simulation model of the human pulse signal during physical load, which can be used to verify digital signal processing methods in biomedical applications. The relevance of the research arises from the need to develop controlled models that reproduce physiologically consistent cardiovascular dynamics without the necessity of complex experimental procedures. The proposed approach integrates parametric modeling of heart rate variability, temporal discretization of observation, and morphological synthesis of pulse waves by convolving discrete cardiac impulses with a vascular response kernel. The model is implemented in the MATLAB environment, enabling numerical experiments, parameter variation, and simulation of different physiological conditions ranging from rest to active workload.

Heart rate dynamics are described by a piecewise-smooth function that includes rest, acceleration, steady-load, and recovery phases with continuous transitions corresponding to the real physiological adaptation of the cardiovascular system. Cardiac beats are simulated through phase integration of the instantaneous frequency, where each crossing of an integer phase value denotes a new cardiac impulse. The morphology of the pulse wave is modeled as a superposition of forward and reflected components, whose amplitudes, delays, and asymmetry determine the characteristic dual-peaked shape typical of pulse signals. This allows the model to reproduce waveform transformations under varying load conditions.

The resulting signal is physiologically plausible, parameter-controlled, and suitable for verifying algorithms of peak detection, filtering, adaptive segmentation, and heart rate variability (HRV) analysis. The model demonstrates high reproducibility, stability, and simplicity of implementation. Using simulation modeling as a verification tool reduces dependence on real clinical experiments and provides standardized conditions for testing digital signal processing techniques. The proposed work contributes to the development of bioengineering models of cardiovascular dynamics and to the enhancement of intelligent monitoring systems for human physiological parameters.