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  • Electromyogram prediction d...
    Casteleiro-Roca, José-Luis; Gomes, Marco; Méndez-Pérez, Juan Albino; Alaiz-Moretón, Héctor; Meizoso-López, María del Carmen; Rodríguez-Gómez, Benigno Antonio; Calvo-Rolle, José Luis

    Journal of ambient intelligence and humanized computing, 11/2020, Letnik: 11, Številka: 11
    Journal Article

    In the search for new and more efficient ways to administer drugs, clinicians are turning to engineering tools. The availability of these models to predict physiological variables are a significant factor. A model is set out in this research to predict the EMG (electromyogram) signal during surgery, in patients under general anaesthesia. This prediction hinges on the Bispectral Index™ (BIS) and the infusion rate of the drug propofol. The results of the research are very satisfactory, with error values of less than 0.67 (for a Normalized Mean Squared Error). A hybrid intelligent model is used which combines both clustering and regression algorithms. The resulting model is validated and trained using real data.