Spinal Cord Stimulation Parameter Modeling for Sub-Perception Therapy
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Solution Overview
Problem
Existing spinal cord stimulation (SCS) therapies face challenges in achieving effective pain relief without paresthesia, particularly with sub-perception therapy, which can be inconvenient due to battery drain and lengthy wash-in periods, and electrode selection is difficult without patient feedback.
Innovation Solution
A method involving supra-perception sweet spot searching to quickly determine effective electrodes, followed by titrating to sub-perception levels, using models to optimize stimulation parameters based on patient-specific relationships between frequency, pulse width, and amplitude, and employing patient-specific GUIs for adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sub-perception therapy is used to provide pain relief without paresthesia, then pain relief effectiveness is improved, but battery drain increases and wash-in period lengthens
Solution Approach 1:
The system performs preliminary supra-perception stimulation to map the patient's neural response and determine optimal electrode locations and stimulation parameters before transitioning to sub-perception therapy. This preliminary mapping action accelerates the wash-in period by pre-establishing the therapeutic parameter profile, reducing the time required for the therapy to become effective while maintaining low battery drain during the actual sub-perception treatment phase
2Reliability
If sub-perception therapy is used to provide pain relief without paresthesia, then pain relief effectiveness is improved, but wash-in period lengthens
Solution Approach 1:
The system performs preliminary supra-perception stimulation to map the patient's neural response and determine optimal electrode locations and stimulation parameters before transitioning to sub-perception therapy. This preliminary mapping action accelerates the wash-in period by pre-establishing the therapeutic parameter profile, reducing the time required for the therapy to become effective
Solution Approach 2:
The system uses real-time feedback from patient responses during supra-perception mapping to dynamically adjust and optimize stimulation parameters. This feedback mechanism allows the system to identify the precise parameter set that provides maximum pain relief with minimal wash-in time, enabling faster transition to effective sub-perception therapy
3Device complexity
If traditional electrode selection methods are used without patient feedback, then device complexity is reduced, but difficulty in determining effective electrodes increases
Solution Approach 1:
The system employs supra-perception stimulation with real-time patient feedback to map neural responses and identify optimal electrode locations. The patient reports perception thresholds during structured testing, providing direct feedback that guides the system in determining which electrodes produce effective stimulation. This feedback-driven approach simplifies the overall process by using intuitive patient reporting rather than complex automated detection algorithms
Solution Approach 2:
The system systematically varies stimulation parameters (amplitude, frequency, pulse width) across different electrode combinations during the mapping phase. By changing these parameters and monitoring patient responses, the system identifies the optimal parameter set for each electrode, transforming the complex problem of electrode selection into a manageable parameter optimization process guided by patient feedback
Data Source
AI summary
An algorithm for determining optimal sub-perception stimulation parameters for a Spinal Cord Stimulation patient is disclosed. The algorithm uses various modelling information, including a model determined based on empirical testing that relates optimal frequencies and pulse widths with perception thresholds reported by patients at those frequencies and pulse widths. A patient's perception threshold is then measured at different pulse widths, with the result compared to the model to determine a range or volume in the model that best relates frequency pulse width and perception threshold for that patient. Further modeling allows the perception thresholds to be related to an optimal amplitudes, thus resulting in optimal situation parameters (F, PW, and A) for the patient. The optimal stimulation parameters may be provided to a patient's external controller to allow the patient to adjust stimulation within a range of volume of these parameters.