Nonlinear Identification of a Minimal Neuromuscular Blockade Model in Anesthesia

Abstract

This brief presents new modeling and identification strategies to address many difficulties in the identification of anesthesia dynamics. The most commonly used models for the effect of muscle relaxants during general anesthesia comprise a high number (greater than eight) of pharmacokinetic and pharmacodynamic parameters. The main issue concerning the neuromuscular blockade system identification is that, in the clinical practice, the input signals (drug dose profiles to be administered to the patients) vary too little to provide a sufficient excitation of the system. The limited amount of measurement data also indicates a need for new identification strategies. A new single-input single-output Wiener model with two parameters is hence proposed to model the effect of atracurium. An extended Kalman filter approach is used to perform the online identification of the system parameters. This approach outperforms many conventional identification strategies, and shows good results regarding parameter identification and measured signal tracking, when evaluated on a large patient database. The new method proved to be adequate for the description of the system, even with the poor input signal excitation and the few measured data samples present in this application. It turns out that the method is of general validity for the identification of drug dynamics in the human body.