title: 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 ([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.
  
  
  CONCLUSIONS
  The proposed automatic algorithm is able to give reliable estimations of [Formula: see text] given ABP signals alone during spontaneous breathing.
authors: Biomedical Instrumentation & Data Analysis Laboratory, East Carolina University, Greenville, NC, USA. kims@ecu.edu.
authors: Sunghan
authors: http://orcid.org/0000-0002-8914-5905
authors: S
authors: Kim
authors: Biomedical Instrumentation & Data Analysis Laboratory, East Carolina University, Greenville, NC, USA.
authors: Fouzia
authors: F
authors: Noor
authors: Electrical Engineering Department, Oregon Institute of Technology, Portland, OR, USA.
authors: Mateo
authors: M
authors: Aboy
authors: Biomedical Signal Processing Laboratory, Portland State University, Portland, OR, USA.
authors: James
authors: J
authors: McNames
mesh_terms: D000465:Algorithms; D001795:Blood Pressure Determination; D006801:Humans; D009010:Monte Carlo Method; D012119:Respiration; D012121:Respiration, Artificial; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic
keywords: Extended Kalman filter; Marginalized particle filter; Maximum a-posteriori estimation; Multi-harmonic signal; a-posteriori distribution