Modeling of arterial pulse waveforms for virtual reconstruction and measurements

The subject of this paper is concerned with utilization of virtual reality in biomeasurements. Biomedical signals carry information that is often enclosed in a signal shape. Problems connected with modeling and simulations of the arterial blood pulse waveform are considered. This waveform is a major component of a given plethysmographic signal that may be acquired form various body sites with noninvasive sensors. Basing on selective biooptical properties of human tissues, using optoelectronic sensors allows acquiring photoplethysmographic (PPG) signals. The PPG signal may be considered as the response of a given dynamic system to an input cyclical signal that is quasi-periodical. The known descriptions of PPG attributes are mostly non-parametrical. On the contrary, a way of parametrical description using the methods, which are utilized to identify dynamic systems, is presented in this paper. The ARMAX structure has been utilized as a base of mathematical modeling. The maximum likelihood method was used in identification of investigated PPG signals. Efficiency of the proposed reconstruction procedure has been tested on processing of real signals.