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Electromyogram prediction during anesthesia by using a hybrid intelligent model

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Abstract

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.

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Acknowledgements

This study was conducted under the auspices of Research Project DPI2010-18278, supported by the Spanish Ministry of Innovation and Science.

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Authors and Affiliations

  1. Dpto. de Ingeniería Industrial, University of A Coruña, A Coruña, Spain

    José-Luis Casteleiro-Roca, María del Carmen Meizoso-López, Benigno Antonio Rodríguez-Gómez & José Luis Calvo-Rolle

  2. ALGORITMI Centre, University of Minho, Braga, Portugal

    Marco Gomes

  3. Dpto. de Ingeniería de Sistemas y Automática y Arquitectura y Tecnología de Computadores, University of La Laguna, San Cristóbal de La Laguna , Spain

    Juan Albino Méndez-Pérez

  4. Dpto. de Ingeniería Eléctrica y de Sistemas y Automática, University of León, León, Spain

    Héctor Alaiz-Moretón

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  1. José-Luis Casteleiro-Roca
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  2. Marco Gomes
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  3. Juan Albino Méndez-Pérez
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  4. Héctor Alaiz-Moretón
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Correspondence to Héctor Alaiz-Moretón.

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Casteleiro-Roca, JL., Gomes, M., Méndez-Pérez, J.A. et al. Electromyogram prediction during anesthesia by using a hybrid intelligent model. J Ambient Intell Human Comput 11, 4467–4476 (2020). https://doi.org/10.1007/s12652-019-01426-8

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