An Intelligent Model for Bispectral Index (BIS) in Patients Undergoing General Anesthesia

  1. José-Luis Casteleiro-Roca 1
  2. Juan Albino Méndez Pérez 2
  3. José Antonio Reboso 2
  4. Francisco Javier de Cos Juez 3
  5. Francisco Javier Pérez Castelo 1
  6. José Luis Calvo Rolle 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

  3. 3 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Book:
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings
  1. Manuel Graña Romay (ed. lit.)
  2. José Manuel López Guede (ed. lit.)
  3. Oier Etxaniz (ed. lit.)
  4. Álvaro Herrero Cosío (ed. lit.)
  5. Héctor Quintián Pardo (ed. lit.)
  6. Emilio Santiago Corchado Rodríguez (ed. lit.)

Publisher: Springer

ISBN: 978-3-319-47364-2

Year of publication: 2017

Pages: 290-300

Congress: International Conference on Computational Intelligence in Security for Information Systems (9. 2016. San Sebastián)

Type: Conference paper

Abstract

Nowadays, the engineering tools play an important role inmedicine, regardless of the area. The present research is focused in anesthesiology, specifically on the behavior of sedated patients. The work shows the Bispectral Index Signal (BIS) modeling of patients undergoing general anesthesia during surgery. With the aim of predicting the patient BIS signal, a model that allows to know its performance from the Electromyogram (EMG) and the propofol infusion rate has been created. The proposal has been achieved by using clustering combined with regression techniques and using a real dataset obtained from patients undergoing general anesthesia. Finally, the created model has been tested also with data from real patients, and the results obtained attested the accuracy of the model.