A Subspace-based Wiener System Identification Method for the Individualized Anesthesia Care ?

This study focuses on the individual sedation model identification problem, and proposes a subspace-based Wiener system identification method. The traditional compartmental pharmacokinetics-pharmacodynamics hypnosis model is considered as a specific Wiener system with the Hill equation nonlinear term. To deal with the Hill nonlinear term, the proposed method employs a set of specific bases to turn the Wiener system into a linear one, and then the subspace orthogonal projection identification method has been implemented to identify the transformed linear model. Compared with the traditional anesthesia model identification methods, the proposed method can effectively overcome the shortage of measurement data, get rid of the estimation of the effect compartment concentration which is impossible to be measured, and improve the individualization performance of the identified model. A simulation study on 24 various virtual patients from the Wang’s Simulator has been conducted and validates the efficiency and robustness of the proposed method, and a drug infusion instruction has been provided in order to get relatively accurate identification performance.