Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization

Abstract

Anesthesia refers to the process of preventing pain and relieving stress on the patient’s body during medical operations. Due to its vital importance in health care systems, the automation of anesthesia has gained a lot of interest in the past two decades and, for this purpose, several models of anesthesia are proposed in the literature. In this paper, a Wiener Box-Jenkins model, consisting of linear dynamics followed by a static polynomial nonlinearity and additive colored noise, is used to model anesthesia. A set of input–output data is generated using closed-loop simulations of the Pharmacokinetic–Pharmacodynamic nonlinear (PK/PD) model relating the drug infusion rates, in [μgkg−1min−1], to the Depth of Anesthesia (DoA), in [%]. The model parameters are then estimated offline using particle swarm optimization (PSO) technique. Several Monte Carlo simulations and validation tests are conducted to evaluate the performance of the identified model. The simulation showed very promising results with a quick convergence in less than 10 iterations, with a percentage error less than 1.5%.