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ARX modeling of drug effects on brain signals during general anesthesia
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Advisor(s)
Abstract(s)
The effect of drugs’ interaction on the brain
signal Bispectral Index (BIS) of the EEG, is of great importance for an anesthesia control drug infusion system. In this
study, the objective was to investigate if an autoregressive
with exogenous inputs model (ARX) could be a suitable
approach to predicting BIS according to the anesthetic drugs
concentrations. Data were collected in 45 neurosurgeries
with total intravenous anesthesia every 5s. A stochastic ARX
model was fitted to the data of each patient. The models
structure that performed better as predictor used a 30s lag
for BIS, 1min lag for propofol and 2min lag for remifentanil.
The models had a good performance with statistical zero
errors (P < 0.05) in 31 patients. The average of absolute
errors was 8.2 ± 2.5, showing that the model captures the
brain signal trend. This model proved to be effective in
modeling and one step prediction of the BIS signal capturing
unique characteristics. The results show that the previous
brain response trend has influence on the present value, in
addition the drugs concentrations from the previous 2min
still have influence. This is an important conclusion for the
development of drug infusion controller algorithms.
Description
Keywords
Citation
C. S. Nunes, F. A. Lobo, P. Amorim, S. de Anestesiologia and C. H. do Porto, "ARX modeling of drug effects on brain signals during general anesthesia," 21st Mediterranean Conference on Control and Automation, Platanias, Greece, 2013, pp. 202-205
Publisher
IEEE