DocumentCode :
429151
Title :
Quantifying uncertainty bounds in anesthetic PKPD models
Author :
Bibian, Stéphane ; Dumont, Guy A. ; Huzmezan, Mihai ; Ries, Craig R.
Author_Institution :
Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada
Volume :
1
fYear :
2004
fDate :
1-5 Sept. 2004
Firstpage :
786
Lastpage :
789
Abstract :
A major challenge faced when designing controllers to automate anesthetic drug delivery is the large variability that exists between and within patients. This intra- and inter-patient variability have been reported to lead to instability. Hence, defining and quantifying uncertainty bounds provides a mean to validate the control design, ensure its stability and assess performance. In this work, the intra- and inter-patient variability measured from thiopental induction data is used to define uncertainty bounds. It is shown that these bounds can be reduced by up to 40% when using a patient-specific model as compared to a population-normed model. It is also shown that identifying only the overall static gain of the patient system already decreases significantly this uncertainty.
Keywords :
drug delivery systems; physiological models; anesthetic PKPD models; anesthetic drug delivery; control design; controllers; inter-patient variability; intra-patient variability; patient-specific model; pharmacodynamics; pharmacokinetics; population-normed model; thiopental induction; uncertainty bounds; Anesthesia; Anesthetic drugs; Control design; Control systems; Delay effects; Electrical capacitance tomography; Frequency; Medical treatment; Stability; Uncertainty; Anesthesia induction; Pharmacodynamics; Pharmacokinetics; Uncertainty; Wavelet-based Anesthetic Value;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
Type :
conf
DOI :
10.1109/IEMBS.2004.1403276
Filename :
1403276
Link To Document :
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