Neurostimulator Bracketing Algorithm for Rapid Parameter Optimization
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Solution Overview
Problem
Manual programming of stimulation parameters for neurostimulators is time-consuming and inefficient, leading to prolonged delays between pulse tests and reduced accuracy in determining effective pain relief for patients.
Innovation Solution
An electronic apparatus and method that automatically generates and programs a range of stimulation parameter values using a bracketing process, allowing for rapid delivery of multiple stimulation pulses with different parameter combinations without user input, utilizing a clinician programmer with a touch-sensitive interface to select and adjust parameters such as amplitude, frequency, and contact location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming of stimulation parameters is used, then the clinician can control each parameter individually, but the programming process becomes time-consuming and inefficient
Solution Approach 1:
The system performs automatic bracketing of stimulation parameters without requiring continuous manual intervention. The processor automatically generates and tests multiple parameter combinations based on initial clinician inputs, allowing the system to serve itself in the parameter optimization process while the clinician defines the therapeutic goals.
Solution Approach 2:
The clinician performs preliminary action by setting initial parameter values and defining the bracketing range. The system then automatically executes the bracketing process to explore the parameter space, combining the clinician's expert initial setup with automated systematic exploration to reduce overall programming time.
2Measurement precision
If manual reprogramming is performed between pulse tests, then parameter adjustments can be made based on patient feedback, but the delays reduce accuracy in determining effective pain relief
Solution Approach 1:
The automatic bracketing process maintains continuity by rapidly cycling through multiple parameter combinations without interruption. The system continuously delivers test pulses with different parameters based on patient feedback from previous pulses, eliminating idle time between tests and maintaining the useful action of parameter optimization throughout the session.
Solution Approach 2:
The system employs periodic action by systematically cycling through predetermined parameter combinations in a structured sequence. Each parameter set is tested in a periodic manner based on the bracketing algorithm, allowing efficient exploration of the parameter space while maintaining rhythmic, organized progression through the optimization process.
3Adaptability or versatility
If multiple parameter combinations are tested manually, then comprehensive coverage of parameter space is achieved, but the process becomes excessively time-consuming
Solution Approach 1:
The parameter space is segmented into systematic brackets or ranges for each parameter (amplitude, frequency, pulse width). The automatic bracketing process divides the comprehensive parameter exploration into manageable segments, testing representative values within each bracket rather than exhaustively testing every possible combination, thus maintaining coverage while improving efficiency.
Solution Approach 2:
The system efficiently explores parameter space by making systematic parameter changes according to the bracketing algorithm. Instead of manual adjustment of each parameter, the processor automatically varies multiple parameters in coordinated fashion, changing them in structured patterns that ensure comprehensive coverage while minimizing the number of tests required.
Data Source
AI summary
The present disclosure involves a method of setting stimulation parameters for neurostimulation. A plurality of stimulation parameters available for bracketing is displayed. The stimulation parameters are selected from the group consisting of: stimulation current amplitude, pulse width, frequency, and contact location. Thereafter, in response to an input from a user, at least a subset of the stimulation parameters is selected for bracketing. A respective initial value is then obtained for each of the stimulation parameters in the selected subset. Thereafter, a bracketing process is used to generate a plurality of bracketed values for each of the stimulation parameters in the selected subset. The bracketed values are generated as a function of the initial value. A plurality of stimulation pulses is then delivered to a patient through a neurostimulator that is automatically programmed with a different combination of the bracketed values for the stimulation parameters for each stimulation pulse.


