title: Pulse pressure variation estimation using a sequential Monte Carlo method.
abstract: 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.
authors: Biomedical Signal Processing Laboratory at Portland State University, Oregon, USA. sunghan@pdx.edu
authors: Sunghan
authors: S
authors: Kim
authors: Mateo
authors: M
authors: Aboy
authors: James
authors: J
authors: McNames
mesh_terms: 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