Spinal Cord Stimulation Field Optimization for Targeted Neural Activation
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Solution Overview
Problem
Conventional spinal cord stimulation (SCS) therapies face challenges such as high failure rates in pain relief, uncomfortable sensations, and limited efficacy due to poor electrode placement and inability to provide targeted stimulation, as well as the complexity of signal interactions involved in pain expression and abatement, which conventional computational models are unable to effectively optimize.
Innovation Solution
The development of computational models using finite element methods (FEM) and optimization frameworks that allow for selective neural activation in regions of interest, optimizing stimulation signals across various dimensions and signal types, minimizing off-target effects by employing methods like generalized Lagrange multipliers and epsilon-constraint techniques to find Pareto fronts for multi-objective optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SCS stimulation configurations are used to activate neural tissues in a region of interest, then pain relief is achieved, but off-target activation in regions of avoidance occurs causing uncomfortable sensations and side effects
Solution Approach 1:
The patent applies local quality by optimizing stimulation parameters specifically for the region of interest while minimizing activation in regions of avoidance. The computational model enables different stimulation characteristics to be applied to different spatial locations, allowing targeted activation of dorsal horn neurons while avoiding dorsal column axon activation, thus achieving pain relief without uncomfortable sensations
Solution Approach 2:
The patent employs parameter changes by systematically varying stimulation configuration parameters (electrode combinations, pulse widths, amplitudes, frequencies) to find optimal settings that maximize target activation while minimizing off-target effects. The computational optimization framework explores the parameter space to identify configurations that resolve the contradiction between effective pain relief and avoidance of side effects
2Reliability
If stimulation amplitude is increased to maximize target area activation, then pain relief efficacy improves, but activation of off-target areas increases leading to more side effects
Solution Approach 1:
The computational model enables spatially selective stimulation by optimizing the distribution of stimulation intensity across different regions. Rather than uniformly increasing amplitude everywhere, the system concentrates activation in the region of interest while maintaining low activation levels in regions of avoidance, achieving effective pain relief without proportional increases in side effects
Solution Approach 2:
The patent applies preliminary action by using computational modeling to predict and optimize stimulation outcomes before actual clinical application. The system pre-calculates the optimal stimulation configuration that will achieve the desired therapeutic effect with minimal side effects, allowing clinicians to set appropriate amplitude levels without trial-and-error that would otherwise cause off-target activation
3Device complexity
If conventional computational models with limited parameters are used for optimization, then computational complexity is reduced, but the ability to optimize stimulation at precise locations and across multiple dimensions is limited
Solution Approach 1:
The patent applies dimensionality change by extending the optimization framework from conventional limited parameter spaces to a comprehensive multi-dimensional parameter space that includes electrode configurations, stimulation waveforms, temporal patterns, and spatial distributions. This enables precise targeting in three-dimensional space while optimizing across multiple objective criteria simultaneously, achieving high targeting precision without being constrained by simplified models
Data Source
AI summary
Field optimization techniques for targeted neural stimulation are provided using finite element method (FEM) model of electrodes and of target region of interest (ROI) biological features. The FEM model estimates the electric potential fields generated by applied stimulation, and include single or multiple electrode configurations and further include biological features, such as encapsulation, dorsal rootles, dura mater, and the vertebral column, in various examples of spinal cord stimulation. The techniques use a single- or multiple-factor optimization that maximize stimulation to the ROI while minimizing effects on a region of avoidance (ROA). Various configurations apply a generalized Lagrange multiplier method to formulate the optimization problem and an epsilon-constraint method to find the Pareto front for multi-objective optimization problems.