Abstract
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.
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References
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.
Leondes, C.T. (ed.): Theory and Application of Kaiman Filtering, North Atlantic Treaty Organization Advisory Group for Aerospace Research and Development, AGARDograph No. 139.
Leibelt, P. B.: An Introduction to Optimal Estimation, Addison-Wesley Publishing Co., 1967.
Wilson, P. David: Optimal Estimation Theory and Method for Patient Monitoring. In preparation for publication.
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© 1973 Plenum Press, New York
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Wilson, P.D. (1973). Filtering and Prediction of Blood Flow and Oxygen Consumption for Patient Monitoring. In: Bruley, D.F., Bicher, H.I. (eds) Oxygen Transport to Tissue. Advances in Experimental Medicine and Biology, vol 37B. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-5089-7_32
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DOI: https://doi.org/10.1007/978-1-4684-5089-7_32
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