Adaptive Brain-Machine Interface for Anesthesia Delivery
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Solution Overview
Problem
Current closed-loop anesthetic delivery systems for medical coma require offline system-identification sessions, are prone to bias due to time-varying brain dynamics, and exhibit unstable infusion rate variations, lacking theoretical guarantees and generalizability to different anesthetic states.
Innovation Solution
An adaptive controller is developed that builds time-varying parametric drug dynamics models, employs novel adaptive estimation and feedback control algorithms, and penalizes large infusion rate changes, allowing operation without initial model knowledge and tracking non-stationary dynamics, while estimating model parameter ratios for precise control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If offline system-identification sessions are used to establish model parameters, then initial model knowledge is obtained, but the system requires additional time and cannot adapt to time-varying brain dynamics
Solution Approach 1:
The patent implements time-varying parametric models where model parameters are continuously updated in real-time based on incoming neural signals, transforming the static offline identification approach into a dynamic online adaptation process. This allows the system to track changing brain dynamics without requiring separate offline sessions.
Solution Approach 2:
The adaptive estimation algorithm enables the system to self-update its model parameters using only the neural signals it receives during operation, without requiring external calibration sessions or additional experimental procedures. The system serves itself by learning from its own operational data.
2Device complexity
If static model parameters are used, then model simplicity is maintained, but the system exhibits bias due to time-varying brain dynamics
Solution Approach 1:
The patent transitions from static to dynamic model parameters that evolve over time according to the underlying brain state changes. The time-varying parameters capture the non-stationary nature of neural dynamics during anesthesia, eliminating the bias inherent in static models while maintaining computational tractability through efficient recursive estimation.
Solution Approach 2:
The system explicitly models parameter changes over time by treating model parameters as stochastic processes rather than fixed values. This allows the parameters to adapt to changing brain dynamics while the underlying model structure remains relatively simple, achieving high accuracy without excessive complexity.
3Speed
If aggressive feedback control is applied, then rapid convergence to target state is achieved, but large infusion rate variations and instability occur
Solution Approach 1:
The patent incorporates regularization terms in the cost function that penalize excessive infusion rate changes before they can cause instability. This preventive approach smooths the control signal in advance, preventing the aggressive fluctuations that would otherwise occur during rapid convergence phases while maintaining overall convergence speed.
Solution Approach 2:
The system uses a carefully designed feedback controller that balances convergence speed and stability by incorporating both tracking error correction and rate-of-change penalization. The feedback law adapts the infusion rate based on current state deviations while simultaneously limiting the magnitude of rate changes to prevent instability.
4Measurement precision
If complete model parameter estimation is performed, then precise control is achieved, but computational burden increases
Solution Approach 1:
The patent extracts only the essential parameter ratios from the complete model parameter set, recognizing that not all individual parameters are needed for effective control. By focusing estimation efforts on the critical parameter combinations that directly influence the controlled variable, the system achieves precise control with reduced computational burden.
Solution Approach 2:
The system transforms the parameter estimation problem by working with parameter ratios rather than absolute parameter values. This change in parameter representation reduces the dimensionality of the estimation problem and decreases computational requirements while maintaining the precision needed for effective adaptive control.
Data Source
AI summary
A system for controlling a state of anesthesia of a patient. The system includes a neural sensor configured to be coupled to a patient and generate an output signal indicative of neural activity of the patient; and an electronic processor. The electronic processor is coupled to the neural sensor and to an infusion pump, and is configured to receive the output signal from the neural sensor, estimate a drug concentration of the patient based on a model including a first model parameter, and estimate a second model parameter based on the output signal from the neural sensor. The electronic processor is also configured to update the model for estimating the drug concentration of the patient with the second model parameter, and output control signals to the infusion pump to deliver a drug, the output signals being based on the estimated drug concentration for the patient and estimates of the parameters.