Online nonlinear identification of the effect of drugs in anaesthesia using a minimal parameterization and BIS measurements

This paper addresses the problem of modeling and identification of the Depth of Anaesthesia (DoA). It presents a new MISO Wiener model for the pharmacokinetics and pharmacodynamics of propofol and remifentanil, when jointly administered to patients undergoing surgery. The models most commonly used to describe the effect of drugs in the human body are overparameterized Wiener models. In particular, in an anaesthesia environment, the high number of patient-dependent parameters coupled with the insufficient excitatory pattern of the input signals (drug dose profiles) and the presence of noise make robust identification strategies difficult to find. In fact, in such clinical application the user cannot freely choose the input signals to enable accurate parameter identification. A new MISO Wiener model with only four parameters is hence proposed to model the effect of the joint administration of the hypnotic propofol and the analgesic remifentanil. An Extended Kalman Filter (EKF) algorithm was used to perform the nonlinear online identification of the system parameters. The results show that both the new model and the identification strategy outperform the currently used tools to infer individual patient response. The proposed DoA identification scheme was evaluated in a real patient database, where the DoA is quantified by the Bispectral Index Scale (BIS) measurements. The results obtained so far indicate that the developed approach will be a powerful tool for modeling and identification of anaesthetic drug dynamics during surgical procedures.