A reduced MIMO Wiener model for recursive identification of the depth of anesthesia

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Margarida M Silva (Autor) Outra; Torbjorn Wigren (Autor) Outra; Teresa Mendonca (Autor) FCUP

Revista

Título: International Journal of Adaptive Control and Signal Processing

Vol. 28

Páginas: 1357-1371

ISSN: 0890-6327

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 8 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-008-GA5

DOI: 10.1002/acs.2447

Abstract (EN): This paper presents a modeling and identification strategy for the depth of anesthesia using the propofol and remifentanil rates as the system inputs, and the bispectral index and state entropy measurements as the systems outputs. The standard model used for this purpose has twenty two patient-dependent parameters. This high number of parameters, the little input excitation, and the small amount of output data make classical system identification approaches unsuccessful. To overcome these issues, the new model presented in this paper has six parameters, thereby meeting the parsimony principle of system identification. An extended Kalman filter algorithm is also developed and applied to real data. The good fitting results, combined with noise suppression and a recursive update of the model parameters, are promising for the design of the depth of anesthesia controllers to be used in real time platforms. Copyright (c) 2013 John Wiley & Sons, Ltd.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15