Adaptive Neurostimulation Control via State Space Estimation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current neuromodulation techniques rely on high amplitude pulses delivered at fixed intervals, which are inefficient and may not adapt to the changing state of the nervous system, leading to suboptimal energy usage and effectiveness in suppressing pathological neural activity.

Innovation Solution

The adaptive real-time state space (ARTISTS) control framework uses an autoregressive model and an amended Kalman filter to estimate the neural state, and a linear quadratic regulator to determine optimal stimulation waveforms based on the current state of the nervous system, allowing for continuous updates and closed-loop control.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If high amplitude pulses are delivered at fixed intervals, then the neurostimulation device can provide consistent stimulation, but energy consumption increases and adaptability to changing neural states decreases

Engineering Contradiction:

Improveadaptability to changing neural statesVSAvoidenergy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system transitions from fixed-interval stimulation to dynamic, state-dependent stimulation. The controller continuously estimates neural states using autoregressive models and adjusts stimulation delivery based on real-time state estimates, making the stimulation protocol adaptive rather than static.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements closed-loop feedback by measuring neural activity, constructing autoregressive models to estimate current neural states, and using these estimates to determine subsequent stimulation settings. This feedback mechanism enables the system to respond to changing neural states and optimize energy usage.

Inventive Principle:

Principle #23Feedback

2Reliability

If high amplitude pulses are delivered at fixed intervals, then the stimulation protocol is simple to implement, but effectiveness in suppressing pathological neural activity decreases

Engineering Contradiction:

Improveeffectiveness in suppressing pathological neural activityVSAvoidcontrol framework complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system introduces autoregressive models and state estimation algorithms as intermediaries between the neural activity measurements and stimulation delivery. These mathematical models serve as mediators that translate raw neural signals into actionable stimulation commands, improving effectiveness while managing complexity through structured computational approaches.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces simple mechanical timing circuits with computational state estimation and control algorithms. Instead of relying on fixed mechanical intervals, the system uses software-based autoregressive modeling and linear quadratic regulation to determine optimal stimulation timing and parameters.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If continuous monitoring and real-time adjustments are implemented, then adaptability and effectiveness improve, but device complexity and computational requirements increase

Engineering Contradiction:

Improvetreatment effectivenessVSAvoidcomputational complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary construction of autoregressive models during periods when sufficient neural data is available, preparing state estimation frameworks in advance. This allows real-time operation to rely on pre-computed model structures rather than performing complex model fitting during critical treatment moments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements state estimation and model updating at strategically selected time points rather than continuously at maximum resolution. By updating models periodically or when significant state changes are detected, the system achieves adequate treatment effectiveness while reducing computational burden compared to continuous maximum-rate processing.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240366945A1Adaptive real-time state space control of a neurostimulation device

Publication Date: 2024.11.07 REGENTS OF THE UNIVERSITY OF MINNESOTA

AI summary

Neurostimulation, such as electrical and/or magnetic neurostimulation, is controlled using an adaptive real-time state space (ARTISTS) control framework to determine and/or adjust stimulation settings (e.g., stimulation waveforms). The ARTISTS control framework generally includes an adaptive autoregressive model of the nervous system's response to stimulation, an amended Kalman filter to estimate the state and coefficients of the autoregressive model, and a linear quadratic regulator to determine the stimulation waveform to be delivered.