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

Sunghan Kim,Mateo Aboy,James McNames,Fouzia Noor

doi:10.1186/s12938-016-0214-x

Abstract

BackgroundWe describe the first automatic algorithm designed to estimate the pulse pressure variation (text {PPV}) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate text {PPV} accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating text {PPV} 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.ResultsOur assessment results indicate good agreement between the automatically estimated text {PPV} and the gold standard text {PPV} obtained with manual annotations. All of the automatically estimated text {PPV} index measurements (text {PPV}_{text {auto}}) were in agreement with manual gold standard measurements (text {PPV}_{text {manu}}) within ±4 % accuracy.ConclusionThe proposed automatic algorithm is able to give reliable estimations of text {PPV} given ABP signals alone during spontaneous breathing.

Highlights

We describe the first automatic algorithm designed to estimate the pulse pressure variation (PPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions
The objectives of this paper are to introduce a new algorithm for automatic estimation of PPV given arterial blood pressure (ABP) signals alone during spontaneous breathing and to assess its performance on real ABP signals from the Massachusetts General Hospital Waveform Database (MGHDB) [14] available on PhysioNet [15]
All of PPVauto measurements were in agreement with PPVmanu measurements within ±3.5 % accuracy

Summary

Introduction

We describe the first automatic algorithm designed to estimate the pulse pressure variation (PPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. Conclusion: The proposed automatic algorithm is able to give reliable estimations of PPV given ABP signals alone during spontaneous breathing. Individuals’ responsiveness to fluid therapy varies significantly and there are few clinical signs for clinicians to rely on to predict the fluid responsiveness Dynamic variables such as stroke volume variation (SVV), systolic pressure variation (SPV), and pulse pressure variation (PPV) have been proposed as reliable indicators to guide fluid therapy in mechanically ventilated patients [4]. PPV attempts to quantify the degree of fluctuations in the difference between the systolic and diastolic arterial blood pressure (ABP). It can be calculated as follows, Kim et al BioMed Eng OnLine (2016) 15:94

Objectives

Discussion

Conclusion