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Mathematical Medicine and Biology Advance Access originally published online on December 12, 2006
Mathematical Medicine and Biology 2007 24(2):225-249; doi:10.1093/imammb/dql031
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© The author 2006. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Parameter estimation of a respiratory control model from noninvasive carbon dioxide measurements during sleep

T. Aittokallio1, M. Gyllenberg2, O. Polo3 and A. Virkki**,4

1 Department of Mathematics, University of Turku, FIN-200141 Turku, Finland, Turku Centre for Computer Science, Joukahaisenkatu 3-5 B, 6th floor, FIN-20520 Turku, Finland, Sleep Research Unit, Department of Physiology, University of Turku, FIN-20014 Finland and Department of Computer Science, Graduate School in Computational Biology, Bioinformatics and Biometry (ComBi), University of Helsinki, FIN-000142 Helsinki, Finland, 2 Rolf Nevanlinna Institute, Department of Mathematics and Statistics, University of Helsinki, FIN-000142 Helsinki, Finland, 3 Sleep Research Unit, Department of Physiology, University of Turku, FIN-200141 Turku, Finland and Department of Pulmonary Medicine, Tampere University Hospital, FIN-335213 Tampere, Finland, 4 Department of Mathematics, University of Turku, FIN-200141 Turku, Finland and Turku Centre for Computer Science, Joukahaisenkatu 3-5 B, 6th floor, FIN-20520 Turku, Finland

** Email: arho.virkki{at}utu.fi


  Abstract

A new method for estimating the parameters of a human gas exchange model is presented. Sensitivity analysis is used both to inspect the relative importance of the model parameters and to speed up the par-ameter estimation process. Multistart optimization is used to compensate for the effects of partial and noisy measurements. The validity of the method is first investigated with a test problem for which par-ameter identifiability is shown. The method is then applied to the estimation of sleep-related changes in the respiratory control system from the end-tidal and transcutaneous carbon dioxide measurements on human subjects. The results show that it is possible to gain insight into the behaviour of the rather complex physiological system using only a few noninvasive measurements and tractable computations.

Keywords: physiological modelling; human respiratory control; parameter estimation; sensitivity analysis

Received on 17 November 2005. Revised on 15 July 2006. Accepted on 4 November 2006.


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[Abstract] [Full Text] [PDF]


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