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Modelling for computer controlled neuromuscular blockade.

T J Gilhuly¹, G A Dumont, B A Macleod

¹Dept. Electrical & Computer Engineering, Dept. Pharmacology & Therapeutics, University of British Columbia, 2176 Health Sciences Mall, Vancouver, BC, V6T 1Z4, Canada. terenceg@ece.ubc.ca.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

This study developed models for automated neuromuscular blockade (NMB) using rocuronium in rabbits and humans. A 6th order Laguerre model was selected for its accuracy and simplicity in predicting NMB responses.

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Area of Science:

Pharmacology
Biomedical Engineering
Control Systems

Background:

Automated neuromuscular blockade (NMB) requires accurate physiological models.
Understanding neuromuscular response variability is crucial for developing effective control strategies.

Purpose of the Study:

To present data collection and model selection methods for automated NMB.
To identify optimal model structures (ARX, Laguerre) for representing neuromuscular responses.
To establish a basis for designing controllers for automated NMB.

Main Methods:

Collected neuromuscular response data in rabbits (N=5) and humans (N=14) under rocuronium administration.
Developed average responses and determined optimal ARX and Laguerre model parameters.
Selected a 6th order Laguerre model based on accuracy and simplicity.
Analyzed model variations within and between species using frequency domain techniques.

Main Results:

A 6th order Laguerre model was identified as optimal for representing neuromuscular blockade.
Significant inter-subject variability was observed in static gain (45.4-45.8% SD) and range (121-159% of mean).
Frequency domain analysis revealed species-specific differences in gain (12-15dB) and phase (45-75°).

Conclusions:

The selected Laguerre model provides an accurate and simple representation of neuromuscular blockade.
Quantified variability in neuromuscular response is essential for robust controller design.
This research provides foundational knowledge for developing automated NMB systems.