A novel particle filtering method for estimation of pulse pressure variation during spontaneous breathing

Abstract

Background: We describe the first automatic algorithm designed to estimate the pulse pressure variation (PPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPV accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating PPV on spontaneously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). We report the performance assessment results of the proposed algorithm on real ABP signals from spontaneously breathing subjects. Results: Our assessment results indicate good agreement between the automatically estimated PPV and the gold standard PPV obtained with manual annotations. All of the automatically estimated PPV index measurements (PPVauto) were in agreement with manual gold standard measurements (PPVmanu) within ±4 % accuracy. Conclusion: The proposed automatic algorithm is able to give reliable estimations of PPV given ABP signals alone during spontaneous breathing.