TY - JOUR
T1 - Experimental Parameterization of a Model of Hypoxia Dynamics in Yorkshire Swine
AU - Wood, Ssam
AU - Commins, Annina
AU - Doosthosseini, Mahsa
AU - Naselsky, Warren
AU - Culligan, Melissa
AU - Aroom, Kevin
AU - Aroom, Majid
AU - Kadkhodaeielyaderani, Behzad
AU - Moon, Yejin
AU - Leibowitz, Joshua
AU - Stewart, Shelby
AU - Yu, Miao
AU - Friedberg, Joseph
AU - Hahn, Jin Oh
AU - Fathy, Hosam K.
N1 - Publisher Copyright:
© 2022 Elsevier B.V.. All rights reserved.
PY - 2022
Y1 - 2022
N2 - This paper estimates the parameters of a model of hypoxia in large animals (specifically, Yorkshire swine) from experimental data. The paper adopts a simple, control-oriented model of oxygen transport and consumption dynamics. The model uses three state variables to represent oxygen concentrations and/or partial pressures in the animal's alveolar, arterial, and venous compartments. Hypoxia is induced in a laboratory animal by placing the animal on mechanical ventilation and reducing the fraction of inspired oxygen. The resulting dataset is used for the optimal estimation of key parameters governing the animal's oxygen transport, oxygen consumption, and hemoglobin dissociation. In this nonlinear system identification exercise, the optimization objective is a weighted sum of the mean square errors in predicting (i) blood oxygen saturation and (ii) end-tidal oxygen fraction. The resulting model fits the experimental hypoxia data reasonably well, and is intended as a baseline for ongoing research on the treatment of respiratory failure.
AB - This paper estimates the parameters of a model of hypoxia in large animals (specifically, Yorkshire swine) from experimental data. The paper adopts a simple, control-oriented model of oxygen transport and consumption dynamics. The model uses three state variables to represent oxygen concentrations and/or partial pressures in the animal's alveolar, arterial, and venous compartments. Hypoxia is induced in a laboratory animal by placing the animal on mechanical ventilation and reducing the fraction of inspired oxygen. The resulting dataset is used for the optimal estimation of key parameters governing the animal's oxygen transport, oxygen consumption, and hemoglobin dissociation. In this nonlinear system identification exercise, the optimization objective is a weighted sum of the mean square errors in predicting (i) blood oxygen saturation and (ii) end-tidal oxygen fraction. The resulting model fits the experimental hypoxia data reasonably well, and is intended as a baseline for ongoing research on the treatment of respiratory failure.
KW - Five to ten keywords
KW - preferably chosen from the IFAC keyword list
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U2 - 10.1016/j.ifacol.2022.11.272
DO - 10.1016/j.ifacol.2022.11.272
M3 - Conference article
SN - 2405-8963
VL - 55
SP - 752
EP - 757
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 37
T2 - 2nd Modeling, Estimation and Control Conference, MECC 2022
Y2 - 2 October 2022 through 5 October 2022
ER -