Electromyogram prediction during anesthesia by using a hybrid intelligent model
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Electromyogram prediction during anesthesia by using a hybrid intelligent modelAutor(es)
Data
2019-08-23Cita bibliográfica
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
Resumo
[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.
Palabras chave
EMG
BIS™
Clustering
MLP
SVM
BIS™
Clustering
MLP
SVM
Versión do editor
Dereitos
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
ISSN
1868-5145