Pulse pressure variation estimation using a sequential Monte Carlo method.
We describe a novel automatic algorithm to continuously estimate the pulse pressure variation (PPV) index from arterial blood pressure (ABP) signals. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM) based on a maximum A-Posterior adaptive marginalized particle filter (MAM-PF). The PPV index is one of most specific and sensitive dynamic indicators of fluid responsiveness in mechanically ventilated patients. We report the assessment results of the proposed algorithm on real ABP signals.
Kim
Sunghan
S
Biomedical Signal Processing Laboratory at Portland State University, Oregon, USA. sunghan@pdx.edu
Aboy
Mateo
M
McNames
James
J
D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D009010:Monte Carlo Method; D009991:Oscillometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity