Evaluation of physiology-based models for noninvasive blood pressure estimation using pulse arrival time.

A noninvasive and nonocclusive blood pressure (BP) measurement method is essential for ambulatory and long-term monitoring. It appears that in the case of wearable systems for continuous pressure measurement, the technique utilizing the dependence of pulse wave velocity on pressure is particularly useful. However, it has some limitations, e.g., accuracy. A generalized, nonlinear model, with respect to physiological parameters, for BP estimation, utilizing information about pulse arrival time (PAT), pulse period (RR), and respiratory activity phase (resp), is proposed and analysed. Analyses have been conducted using a publicly available database. The models are compared against each other using various measures such as correlation, root mean square, Akaike information criterion, Bayesian information criterion, and minimum description length. An optimal model, superior to those presented in the literature, is recommended for each measure. In addition, the influence of individual signals on the pressure estimation error was analysed. The results show that simple models generate large errors in BP estimation. Including more parameters improves the results, but the errors are still relatively large. The presented results suggest that the considered signals, i.e., PAT, RR, and resp, contain incomplete information about the current pressure value.