Spinal Cord Stimulation Pattern Optimization for Response Variability
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Solution Overview
Problem
Patient responses to spinal cord stimulation (SCS) for chronic pain relief are highly variable due to factors like spinal anatomy, electrode position, and pain characteristics, making it challenging to optimize SCS programming effectively.
Innovation Solution
The method involves generating optimized temporal patterns of SCS using an optimization algorithm based on predetermined performance criteria, evaluating these patterns using a computational model of a neuronal network, and identifying patterns that reduce pain variability across different pain states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SCS programming methods are used, then device complexity is reduced and ease of operation is improved, but patient response variability remains high and pain relief efficacy is inconsistent
Solution Approach 1:
The patent applies preliminary action by pre-calculating and pre-optimizing SCS parameter settings before actual use. A computational model predicts optimal stimulation parameters based on patient-specific anatomical data, and these pre-optimized parameters are stored in the device for automatic deployment, eliminating the need for complex real-time optimization during clinical procedures
Solution Approach 2:
The patent introduces a computational model as an intermediary between patient anatomical data and SCS parameter optimization. This virtual surrogate model simulates neural responses and predicts optimal parameters, serving as a mediator that translates complex anatomical information into actionable stimulation settings without requiring direct complex optimization during treatment
2Reliability
If multiple SCS parameters are varied to optimize pain relief, then pain reduction efficacy is improved, but the breadth of parameter space makes optimization a significant clinical challenge
Solution Approach 1:
The patent systematically varies multiple SCS parameters including amplitude, pulse width, frequency, and pattern timing within defined ranges. The computational model evaluates different parameter combinations and identifies optimal settings by analyzing how changes in each parameter affect pain relief predictions, allowing comprehensive parameter exploration without manual trial-and-error
Solution Approach 2:
The patent creates a virtual copy of the patient's spinal anatomy and neural pathways through a computational model. This digital replica allows repeated simulation of different SCS parameter combinations without physical risk, enabling extensive parameter optimization through virtual experimentation before actual device programming
3Reliability
If SCS parameters are customized for each patient, then pain relief effectiveness is improved, but evaluating SCS performance becomes difficult due to broad parameter settings and different device protocols
Solution Approach 1:
The patent creates a virtual copy of the patient's spinal anatomy and neural pathways through a computational model. This digital replica allows repeated simulation of different SCS parameter combinations without physical risk, enabling extensive parameter optimization through virtual experimentation before actual device programming
Solution Approach 2:
The patent establishes a common evaluation framework that normalizes SCS performance across different device protocols and parameter settings. By using the computational model to simulate and compare outcomes under standardized conditions, the system creates an equipotential evaluation space where different parameter combinations can be objectively compared regardless of specific device protocols
Data Source
AI summary
This present disclosure provides systems and methods relating to neuromodulation. In particular, the present disclosure provides systems and methods for identifying optimized temporal patterns of spinal cord simulation (SCS) for minimizing variability in patient responses to SCS. The systems and methods of neuromodulation disclosed herein facilitate the treatment of neuropathic pain associated with various disease states and clinical indications.