Use this link to cite:
http://hdl.handle.net/2183/31879 Electromyogram prediction during anesthesia by using a hybrid intelligent model
Loading...
Identifiers
Publication date
Authors
Gomes, Marco
Méndez Pérez, Juan Albino
Alaiz Moretón, Héctor
Advisors
Other responsabilities
Journal Title
Bibliographic citation
Casteleiro-Roca J-L, Gomes M, Méndez-Pérez JA, et al. (2020) Electromyogram prediction during anesthesia by using a hybrid intelligent model. J Ambient Intell Human Comput 11:4467–4476. https://doi.org/10.1007/s12652-019-01426-8
Type of academic work
Academic degree
Abstract
[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.
Description
Keywords
Editor version
Rights
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s12652-019-01426-8