Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction

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

研究成果: 雜誌貢獻文章

1 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號6975085
期刊Computational and Mathematical Methods in Medicine
2017
DOIs
出版狀態已發佈 - 一月 1 2017

ASJC Scopus subject areas

  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics

指紋 深入研究「Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction」主題。共同形成了獨特的指紋。

  • 引用此