Neurostimulation Parameter Optimization via Simulation
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Solution Overview
Problem
Current neurostimulation systems face challenges in efficiently determining optimal stimulation parameters for personalized therapy, leading to lengthy and complex processes that can be cumbersome for patients.
Innovation Solution
The system incorporates a pattern optimization circuit and a programming control circuit that use an objective function and a personalized model to determine an optimal pattern of neurostimulation, including fixed values for certain parameters to minimize sensitivity and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neurostimulation parameters are determined by sweeping values through multiple parameters with patient testing, then personalized therapy optimization is achieved, but the process becomes long and complicated
Solution Approach 1:
The system performs preliminary simulation-based optimization to identify which stimulation parameters have minimal impact on the objective function before actual patient testing. By pre-determining that certain parameters can be fixed at arbitrary values without significantly affecting therapy outcomes, the system reduces the dimensionality of the parameter space that requires time-consuming patient testing, thereby resolving the contradiction between optimization accuracy and time consumption
Solution Approach 2:
The system creates a computational model copy of the patient's neural system and uses simulation to predict parameter effects. This virtual copy allows extensive parameter exploration and optimization to be performed in silico before validating with actual patient testing, significantly reducing the time required for real-world parameter determination while maintaining optimization precision
2Reliability
If multiple stimulation parameters are tested to determine optimal values, then therapy effectiveness is improved, but the complexity of the programming process increases
Solution Approach 1:
The system extracts and identifies the critical subset of stimulation parameters that actually influence therapy effectiveness using simulation analysis. By taking out the non-essential parameters from the optimization process and fixing them at arbitrary values, the system reduces programming complexity from managing multiple interdependent parameters to optimizing only the essential few, while maintaining therapy effectiveness through focused parameter tuning
Solution Approach 2:
The system changes the state of certain parameters from variable to fixed by demonstrating through simulation that their values have minimal impact on the objective function. This parameter state change simplifies the programming process by reducing the number of parameters that require careful adjustment, while the remaining parameters are optimized to ensure therapy effectiveness is maintained
Data Source
AI summary
A system for delivering neurostimulation to a patient may include a pattern optimization circuit and a programming control circuit. The pattern optimization circuit may receive an objective function, a model, and one or more patterns of neurostimulation each including multiple stimulation parameters and may include optimization processing circuitry configured to determine an optimal pattern of neurostimulation using a simulation with the objective function, the model, and the one or more patterns of neurostimulation. The optimal pattern of neurostimulation including first stimulation parameter(s) each identified by the simulation to have a fixed value or value range with which a sensitivity to values of each of second stimulation parameter(s) on the objective function is identified by the simulation to be minimized. The programming control circuit may be configured to generate information for programming a stimulation device to control the delivery of the neurostimulation according to the optimal pattern of neurostimulation.