An Intelligent Model for Bispectral Index (BIS) in Patients Undergoing General Anesthesia
Nowadays, the engineering tools play an important role in medicine, 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.
KeywordsEMG BIS Clustering SOM MLP SVM
This study was conducted under the auspices of Research Project \(DPI2010-18278\), supported by the Spanish Ministry of Innovation and Science.
- 5.Calvo-Rolle, J.L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R.F.: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J. Appl. Logic 13(1), 37–47 (2015)CrossRefzbMATHGoogle Scholar
- 8.Casteleiro-Roca, J.L., Pérez, J.A.M., Piñón-Pazos, A.J., Calvo-Rolle, J.L., Corchado, E.: Modeling the electromyogram (EMG) of patients undergoing anesthesia during surgery. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds.) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol. 368, pp. 273–283. Springer, Heidelberg (2015)Google Scholar
- 11.De Cos Juez, F.J., Lasheras, F.S., García Nieto, P., Suárez, M.S.: A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Int. J. Comput. Math. 86(10–11), 1878–1887 (2009)CrossRefzbMATHGoogle Scholar
- 15.Heiberger, R., Neuwirth, E.: Polynomial regression. In: R Through Excel. Use R, pp. 269–284. Springer, New York (2009)Google Scholar
- 18.Litvan, H., Jensen, E.W., Galan, J., Lund, J., Rodriguez, B.E., Henneberg, S.W., Caminal, P., Villar Landeira, J.M.: Comparison of conventional averaged and rapid averaged, autoregressive-based extracted auditory evoked potentials for monitoring the hypnotic level during propofol induction. J. Am. Soc. Anesthesiologists 97(2), 351–358 (2002)Google Scholar
- 19.Machón-González, I., López-García, H., Calvo-Rolle, J.L.: A hybrid batch SOM-NG algorithm. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–5 (2010)Google Scholar
- 20.Manuel Vilar-Martinez, X., Aurelio Montero-Sousa, J., Luis Calvo-Rolle, J., Casteleiro-Roca, J.L.: Expert system development to assist on the verification of “tacan” system performance. Dyna 89(1), 112–121 (2014)Google Scholar
- 31.Wu, X.: Optimal designs for segmented polynomial regression models and web-based implementation of optimal design software. State University of New York at Stony Brook, Stony Brook, NY, USA (2007)Google Scholar