Pulse pressure variation tracking using sequential Monte Carlo methods

The pulse pressure variation (PPV) is a measure of the respiratory effect on the variation of systemic arterial blood pressure (ABP) in patients receiving full mechanical ventilation. It is a promising predictor of increases in cardiac output due to an infusion of fluid. We describe a novel automatic algorithm to estimate the PPV of ABP signals recorded under full respiratory support. The algorithm utilizes our recently vailable online 17 April 2013 eywords: mplitude modulation ulse pressure variation equential Monte Carlo method tate-space model developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). MAM-PF estimates the state-space model parameters of the ABP signal continuously and its upper and lower envelopes are derived as a combination of those parameter estimates. Then, the continuous PPV values can be easily obtained based on those estimated envelopes. We report the assessment results of the proposed algorithm on real ABP signals. © 2013 Elsevier Ltd. All rights reserved.