• Ana Castro, Pedro Amorim, and Catarina S Nunes.
• Faculdade de Ciências da Universidade do Porto, Departamento de Matemática Aplicada, Rua do Campo Alegre 687, 4169-007, Porto, Portugal. ana.castro@fc.up.pt
• Conf Proc IEEE Eng Med Biol Soc. 2007 Jan 1;2007:1949-52.

AbstractBecause of the complexity of raw electroencephalogram (EEG), for the anesthesiologist it is very difficult to evaluate the patient's hypnosis state. Because of this, several depth of anesthesia monitors have been developed, and are in current use at the operating room (OR). These monitors convert the information supplied by the EEG or derived signals into a simple, easy to understand index. Nowadays, general anesthesia is controlled only by the clinician, which decides what is the best drug combination for the patient, regarding all information given by monitors and sensors in the OR. In this work, we collected data from two study groups with auditory evoked potentials (AEP) monitoring, and Entropy (SE) monitoring. A model was fitted to the signals and the Hill equation parameters adjusted, in both study groups. The objective was to predict hypnosis indices, regarding only the drugs administered to a patient, and capture the initial individual patient characteristics that might influence the drugs interaction in the human body. Hypnotic and analgesic drugs interact in different ways throughout the anaesthesia stages. The models obtained captured the different dynamic interaction of drugs, during the induction and maintenance phases, demonstrating that the model must have incorporated all this information in order to perform satisfactorily. Other information like haemodynamic variables might be included in the search for the optimum model.

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