Nonlinear dynamics of the patient’s response to drug effect during general anesthesia

• Wavelet time–frequency analysis is used to extract useful information from the clinical signals.
• Model based predictive control algorithms are employed to regulate the depth of sedation.
• Two manipulating drugs are used.
• The results of identification from real data suggest that the proposed approach is suitable for clinical practice.

In today’s healthcare paradigm, optimal sedation during anesthesia plays an important role both in patient welfare and in the socio-economic context. For the closed-loop control of general anesthesia, two drugs have proven to have stable, rapid onset times: propofol and remifentanil. These drugs are related to their effect in the bispectral index, a measure of EEG signal. In this paper wavelet time–frequency analysis is used to extract useful information from the clinical signals, since they are time-varying and mark important changes in patient’s response to drug dose. Model based predictive control algorithms are employed to regulate the depth of sedation by manipulating these two drugs. The results of identification from real data and the simulation of the closed loop control performance suggest that the proposed approach can bring an improvement of 9% in overall robustness and may be suitable for clinical practice.