BioMedical Engineering OnLine
Kalman filtering, Estimation theory
Background: We describe the first automatic algorithm designed to estimate the pulse pressure variation ([Formula: see text]) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate [Formula: see text] accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating [Formula: see text] 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 [Formula: see text] and the gold standard [Formula: see text] obtained with manual annotations. All of the automatically estimated [Formula: see text] index measurements ([Formula: see text]) were in agreement with manual gold standard measurements ([Formula: see text]) within ±4 % accuracy.
Conclusion: The proposed automatic algorithm is able to give reliable estimations of [Formula: see text] given ABP signals alone during spontaneous breathing.
Kim, Sunghan; Noor, Fouzia; Aboy, Mateo, and McNames, James. (2016). A Novel Particle Filtering Method for Estimation of Pulse Pressure Variation During Spontaneous Breathing. BioMedical Engineering OnLine, Volume 15.