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Publication
Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms
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Advisor(s)
Abstract(s)
Objective: The first part of this research relates to two strands: classification of
depth of anaesthesia (DOA) and the modelling of patient’s vital signs.
Methods and Material: First, a fuzzy relational classifier was developed to classify a
set of wavelet-extracted features from the auditory evoked potential (AEP) into
different levels of DOA. Second, a hybrid patient model using Takagi—Sugeno Kang
fuzzy models was developed. This model relates the heart rate, the systolic arterial
pressure and the AEP features with the effect concentrations of the anaesthetic drug
propofol and the analgesic drug remifentanil. The surgical stimulus effect was
incorporated into the patient model using Mamdani fuzzy models.
Results: The result of this study is a comprehensive patient model which predicts the
effects of the above two drugs on DOA while monitoring several vital patient’s signs.
Conclusion: This model will form the basis for the development of a multivariable
closed-loop control algorithm which administers ‘optimally’ the above two drugs
simultaneously in the operating theatre during surgery.
Description
Keywords
Depth of anaesthesia Audio evoked potential Neural fuzzy Classifier Wavelet
Pedagogical Context
Citation
Nunes, C.S., M. Mahfouf, D. Linkens, and J. Peacock (2005) "Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms: Part I- classification of depth of anaesthesia and development of a patient model," Artificial Intelligence in Medicine, 35(3): 195-206
Publisher
Elsevier