Spinal Cord Stimulation Model Using Physiological Midline Alignment
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Solution Overview
Problem
Current Spinal Cord Stimulation (SCS) systems face challenges in determining effective stimulation parameters due to uncertainties in electrode placement and the electrical environment, leading to inefficiencies in delivering therapy for chronic pain management.
Innovation Solution
The technique involves determining the location of the physiological midline to improve the current mapping algorithm, allowing for the construction of a target stimulation field parallel to the anatomical midline and adjusting stimulation intensity to maintain consistent neural activation, using a combination of spinal and peripheral electrodes to assess neural responses and optimize electrode current allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SCS systems use standard current mapping algorithms based on anatomical midline assumptions, then the system structure remains simple and easy to operate, but the precision of stimulation field alignment with physiological structures deteriorates
Solution Approach 1:
The system uses peripheral electrode responses as feedback to iteratively optimize current allocation. The algorithm measures neural responses from peripheral electrodes and adjusts the current distribution matrix accordingly, creating a closed-loop system that continuously refines stimulation precision based on actual physiological feedback.
Solution Approach 2:
The system performs preliminary optimization by determining the physiological midline location and calculating the optimal current allocation matrix before delivering the actual therapeutic stimulation. This pre-positioning of stimulation parameters based on predicted optimal pathways ensures precise alignment when therapy begins.
2Productivity
If electrode placement and electrical environment are treated as uncertain factors, then the system can accommodate variability, but the effectiveness of pain management deteriorates due to inefficient therapy delivery
Solution Approach 1:
The system dynamically changes electrical parameters (current allocation ratios) based on measured peripheral responses. By adjusting the current distribution matrix according to actual neural pathway activation patterns, the system adapts to individual patient variability and optimizes therapy efficiency for each case.
Solution Approach 2:
The system applies different current allocation ratios to different electrode pairs based on their specific anatomical locations and observed response characteristics. Each electrode configuration is optimized locally according to its unique physiological context rather than using a uniform approach, thereby improving overall therapy effectiveness.
3Measurement precision
If the stimulation model assumes anatomical midline alignment, then the modeling process remains simple, but the accuracy of neural activation prediction deteriorates
Solution Approach 1:
The modeling process incorporates feedback from peripheral electrode measurements to refine predictions of neural activation. The system compares predicted versus actual responses and adjusts the stimulation model parameters accordingly, creating an iterative refinement process that improves prediction accuracy.
Solution Approach 2:
The system transitions from a simple anatomical midline assumption to a more comprehensive model that includes physiological midline determination and individualized current allocation optimization. This adds dimensional complexity by incorporating multiple optimization criteria and physiological variables beyond basic anatomical positioning.
Data Source
AI summary
Techniques for determining the location of a physiological midline and utilizing the physiological midline location to improve a spinal cord stimulation model are disclosed. A first improvement constructs a target stimulation field along a line that is parallel with the determined physiological midline. An allocation of stimulation among the electrodes to mimic the target field is computed. A second improvement models a response of neural elements at evaluation positions that are parallel with the physiological midline based on the electric field that is generated for the computed allocation of stimulation among the electrodes. The stimulation amplitude is adjusted based on the neural element modeling to maintain stimulation intensity, and the stimulation amplitude and allocation of stimulation among the electrodes are compiled into an electrode configuration that is communicated to a neurostimulator.