Monitoring the anesthetic-induced unconsciousness (hypnosis) using wavelet analysis of the electroencephalogram

Zikov, T.

doi:10.14288/1.0065055

2002

DOI: 10.14288/1.0065055

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Monitoring the anesthetic-induced unconsciousness (hypnosis) using wavelet analysis of the electroencephalogram

T. Zikov¹

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2003

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Quantifying uncertainty bounds in anesthetic PKPD models

Bibian

Dumont

Huzmezan

et al.

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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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.

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Quantifying uncertainty bounds in anesthetic PKPD models

Bibian

Dumont

Huzmezan

et al.

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

Clinical anesthesia and control engineering: Terminology, concepts and issues

Bibian

Ries

Huzmezan

et al. 2003

2003 European Control Conference (ECC)

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Monitoring the anesthetic-induced unconsciousness (hypnosis) using wavelet analysis of the electroencephalogram

Cited by 2 publications

References 0 publications

Quantifying uncertainty bounds in anesthetic PKPD models

Quantifying uncertainty bounds in anesthetic PKPD models

Clinical anesthesia and control engineering: Terminology, concepts and issues

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