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

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Mateo Aboy (Creator)

Sunghan Kim (Creator)

James McNames (Creator)

Fouzia Noor (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: "Background: We describe the first automatic algorithm designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV 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 PPVPPV and the gold standard PPVPPV obtained with manual annotations. All of the automatically estimated PPVPPV index measurements (PPVautoPPVauto) were in agreement with manual gold standard measurements (PPVmanuPPVmanu) within ±4 % accuracy. Conclusion: The proposed automatic algorithm is able to give reliable estimations of PPVPPV given ABP signals alone during spontaneous breathing."

Additional Information

Publication

Other

BioMedical Engineering OnLine. 2016 Aug 11;15(1):94

Language: English

Date: 2016

Keywords

Extended Kalman filter, A-posteriori distribution, Maximum a-posteriori estimation, Marginalized particle filter, Multi-harmonic signal

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