Quantification of the multiplicative uncertainty in the linearized minimally parameterized parsimonious Wiener model for the neuromuscular blockade in closed-loop anesthesia

Abstract

In this paper the multiplicative uncertainty in the linearized minimally parameterized parsimonious Wiener model for the neuromuscular blockade is quantified. A set of model parameters was identified from input-output data collected in the surgery room from a population of fifty patients undergoing general anesthesia. The nominal model was considered to be the average of the transfer functions over all fifty realizations. The non-parametric and parametric methods that were implemented delivered similar beta-tolerance regions in the frequency domain for the multiplicative model uncertainty to be used in e.g. robust control design.

Nonlinear Estimation of a Parsimonious Wiener Model for the Neuromuscular Blockade in Closed-loop Anesthesia

“…The phenomenon has been extensively assessed for the two main components of anesthesia: the NMB, in e.g. Caiado et al (2012) and Silva et al (2013); and the depth of anesthesia (DoA), in e.g. Niño et al (2009), Dumont et al (2009, and van Heusden et al (2013).…”