Filtering and Prediction of Blood Flow and Oxygen Consumption for Patient Monitoring

  • P. David Wilson
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 191)


Suppose data is to be electronically acquired at discrete times Δ, 2Δ, 3Δ,..., tΔ,... (starting from an arbitrary origin) on arterial and venous oxygen concentration as well as an independent variable which is either blood flow rate or oxygen consumption, and that the remaining dependent variable (O2 consumption or blood flow) is to be predicted from the data. We refer to “time tΔ” merely as “time t”. Let y 1t be the observed value of the independent variable at time t and let y2t, y3t. be the observed values of the arterial and venous O2 concentrations respectively at time t. The observations are physiological state values corrupted by noise or observation error. For j = 1,2,3 corresponding to the observation subscripts, let x̃j (t) be the jth physiological state (existing in continuous time) and let vjt be the noise or observation error of the jth observation at time t.


Tissue Oxygenation Blood Flow Rate Observation Error Multivariate Time Series Dependent Random Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wilson, P. David: Adaptive Smoothing and Prediction of a Nonstationary, Multivariate Time Series; An Approach to Computer Monitoring of Patients in an Intensive Care Unit. Doctoral Dissertation, Johns Hopkins University, 1970.Google Scholar
  2. 2.
    Leondes, C.T. (ed.): Theory and Application of Kaiman Filtering, North Atlantic Treaty Organization Advisory Group for Aerospace Research and Development, AGARDograph No. 139.Google Scholar
  3. 3.
    Leibelt, P. B.: An Introduction to Optimal Estimation, Addison-Wesley Publishing Co., 1967.Google Scholar
  4. 4.
    Wilson, P. David: Optimal Estimation Theory and Method for Patient Monitoring. In preparation for publication.Google Scholar

Copyright information

© Plenum Press, New York 1973

Authors and Affiliations

  • P. David Wilson
    • 1
  1. 1.Institute for Emergency MedicineUniversity of Maryland HospitalBaltimoreUSA

Personalised recommendations