Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction.

Frank Lam,James S Kang,Chung-Che Wu,Hsiang-Wei Lu,Zekeriya Aliyazicioglu

doi:10.1155/2017/6975085

Abstract

Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C, peripheral resistance R, aortic impedance r, and the inertia of blood L, to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies.

Highlights

Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements
In comparison with conventional methods of noise reduction which entails the elimination of data that exists at lower and higher bound frequencies with high-pass or low-pass filters—since the Kalman filter operates through the prediction of future states based on prior state knowledge, it is able to strategically eliminate noise and estimate an ABP waveform containing low amounts of noise without the absolute loss of high or low frequency waveform data
We proposed the use of the Kalman filter and the 4-element Windkessel model to represent aortic circulation for generating accurate estimations of ABP waveforms by reducing noise and artifacts

Summary

Introduction

Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Hemodynamic monitoring instruments can measure ABP waveforms invasively using catheters that are inserted into the arteries [2] or utilize noninvasive sensors that obtain arterial pressure pulses without catheter insertion. Regardless of the method used for acquiring ABP waveforms, ABP waveforms can be used as an input into hemodynamic parameter estimation algorithms to estimate hemodynamic parameters such as cardiac output (CO) or stroke volume (SV) [2, 3]. Since hemodynamic parameter estimation algorithms often have to detect events and features from measured ABP waveforms in order to generate hemodynamic parameters, the integration of noise and artifacts into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms

Methods

Results

Conclusion