Fractional-Order Predictive Neurostimulation With Closed-Loop Feedback
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Solution Overview
Problem
Existing electrical neurostimulation devices have limited sensing, actuating, and processing capabilities, failing to utilize the potential information in measured electrical activity for effective therapeutic intervention in neurological disorders.
Innovation Solution
A system controller circuitry employing fractional-order model predictive control (MPC) that includes an optimization module, feedback control module, and model update module to predict and provide control inputs based on a fractional order model of the neurophysiological system, updating model parameters and objective function parameters to adapt to the system's current state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional electrical neurostimulation devices use predefined stimulation doses determined during manual calibration, then the device structure remains simple and easy to manufacture, but the therapeutic effectiveness is limited because potential information in measured electrical activity is not utilized
Solution Approach 1:
The patent implements a closed-loop feedback system where the controller continuously receives measured electrical activity from sensors, processes this information through optimization algorithms, and adjusts stimulation parameters in real-time based on the current neurophysiological state. This feedback mechanism transforms the open-loop predefined dose system into a dynamic adaptive system that responds to actual brain activity patterns
Solution Approach 2:
The system transitions from static predefined stimulation doses to dynamic adaptive control where stimulation parameters continuously evolve based on real-time optimization. The controller recalculates optimal stimulation doses at each time step using measured electrical activity, creating a dynamic system that adapts to changing neurophysiological conditions during therapy
2Measurement precision
If the system uses optimization algorithms to predict control inputs based on fractional order models, then the control precision and adaptability improve, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent employs fractional-order system parameters (characterized by order α where 0 < α < 1) to model neurophysiological dynamics, capturing memory effects and hereditary properties of brain tissue more accurately than integer-order models. The optimization algorithm adjusts these fractional parameters along with stimulation amplitudes and durations to achieve precise control while maintaining computational tractability through efficient numerical methods
3Adaptability or versatility
If the system continuously updates model parameters and objective function parameters based on recent state, then the adaptability to changing neurophysiological conditions improves, but the computational load and energy consumption increase
Solution Approach 1:
The system implements periodic updates of model parameters and objective function parameters at discrete time intervals rather than continuous updates. The optimization module recalculates control inputs at each sampling period based on accumulated measurements, balancing adaptability with computational efficiency and energy conservation in the implanted device
Data Source
AI summary
One embodiment provides a system controller circuitry for mitigating a neurophysiological disorder. The system controller circuitry includes an optimization module, a feedback control module, and a model update module. The optimization module is configured to predict a sequence of control inputs based, at least in part, on a fractional order model of a neurophysiological system. A duration of the predicted sequence of control inputs corresponds to a prediction horizon. The feedback control module is configured to provide at least a portion of the sequence of control inputs to the neurophysiological system based, at least in part, on a current state of the neurophysiological system. A duration of the at least a portion corresponds to a control horizon. The model update module is configured to update one or more of a model parameter and/or an objective function parameter at an update interval, based, at least in part, on a recent state of the neurophysiological system.