Imitation Modeling of the Daily Pulse Signal for Long-Term Monitoring Systems

Abstract

The imitation model of the daily (24 hours) pulse signal is developed on the basis of its mathematical model in the form of the periodically elongated sums of two functions with normal distribution and exponential attenuation. Unlike existing models this one takes into account the randomness arising as a result of the internal and external noises influence, periodicity related to the nature of the pulse signal generation, changing of the oscillation phase as a result of the vessels stiffness changing, the amplitude of the direct and reflected waves of the human vessels blood flow and the day and night structure (i.e. changes in the human cardiovascular system and brain functioning during the day and night). The imitation model makes it possible, based on known medical parameters, to simulate the daily pulse signals of pathologies and norms for the task of the methods for such signals processing in the long-term monitoring systems verification. This is important in the timely diagnosis of the human vessels state for the correct diseases treatment and prevention. Based on the imitation model and the Matlab environment software the algorithms of the daily pulse signal processing and the corresponding graphical user interface are developed. The magnitudes of the pulse signals amplitude, durations, moments of the blood filling maximum time and a number of the other parameters which are characteristic of the pulse signal direct and reflected waves, as well as the number of the day and night stages and their time duration are the input data for such signals simulation. The testing procedure of the developed software for the daily pulse signals modeling is carried out. It is established that obtained simulated implementation of daily pulse signals provide a complete reproduction of the experimental signals form in time and amplitude parameters of the direct and reflected waves. This gives grounds to assert that the developed imitation model is suitable for using in the long-term monitoring systems.