The paper presents new modeling and identification strategies to address the many difficulties in the identification of anaesthesia dynamics. During general anaesthesia procedures muscle relaxants are drugs frequently administered. The most commonly used models for the effect of such drugs, called NeuroMuscular Blockade (NMB), comprise a high number (greater than eight) of pharmacokinetic and pharmacodynamic (PK/PD) parameters. The main issue concerning the NMB system identification is that, in the clinical practice, the user cannot freely choose the system input signals (drug dose profiles to be administered to the patients) to enable the identification of such a high number of parameters. The limited amount of measurement data also indicates a need for new identification strategies. A new SISO Wiener model with two parameters is hence proposed to model the effect of the muscle relaxant atracurium. A batch Prediction Error Method (PEM) was first developed to optimize the model structure. Secondly, an Extended Kalman Filter (EKF) approach was used to perform the online identification of the system parameters. Both approaches outperform conventional identification strategies, showing good results regarding parameter identification and measured signal tracking, when evaluated on a large patient database. The new methods 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 methods are of general validity for the identification of drug dynamics in the human body.
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