At present, the experimentation of anesthetic drugs on
patients requires a regulation protocol, and the response of each patient
to several doses of entry drug must be well known. Therefore, the
development of pharmacological dose control systems is a promising
field of research in anesthesiology.
In this paper it has been developed a non-linear compartmental
pharmacokinetic-pharmacodynamical model which describes the
anesthesia depth effect on a sufficiently reliable way over a set of
patients with the depth effect quantified by the Bi-Spectral Index.
Afterwards, an Artificial Neural Network (ANN) predictive controller
has been designed based on the depth of anesthesia model so as to keep
the patient on the optimum condition while he undergoes surgical
treatment.
For the purpose of quantifying the efficiency of the neural predictive
controller, a classical proportional-integral-derivative controller has
also been developed to compare both strategies. Results show the
superior performance of predictive neural controller during Bi-
Spectral Index reference tracking.
