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    Model and neural control of the depth of anesthesia during surgery

    • Autor
      Fernández-de-Cañete-Rodríguez, Francisco JavierAutoridad Universidad de Málaga; Medina, Mayte; Fernández de Cañete, Rafael; Alcain, Nuria; Ramos-Diaz, Juan Carlos
    • Fecha
      2018
    • Editorial/Editor
      WASET
    • Palabras clave
      Anestesia
    • Resumen
      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.
    • URI
      https://hdl.handle.net/10630/15350
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    abstract Model_and_Neural_Control_of_the_Depth_of_Anesthesia_during_S ICBMM 2018.pdf (29.88Kb)
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    • Ponencias, Comunicaciones a congresos y Pósteres

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    REPOSITORIO INSTITUCIONAL UNIVERSIDAD DE MÁLAGA