Neural Sensing Parameter Optimization for Faster ECAP Detection
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Solution Overview
Problem
Existing neurostimulation systems face challenges in efficiently determining optimal stimulation and sensing parameters for sensing biopotential signals, particularly evoked compound action potentials (ECAPs), which can take a long time to manually set and require expertise, affecting therapy efficacy and safety.
Innovation Solution
An automated algorithm optimizes stimulation and sensing parameters by sweeping a sequence of parameter sets, analyzing recorded data to determine optimal configurations for detecting specified neural signals, including ECAPs, using a system with a programming device and electrodes to reduce setup time and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter setting is used for sensing biopotential signals, then parameter selection can be customized based on expertise, but the time required for programming is significantly increased
Solution Approach 1:
The system performs self-programming by automatically determining optimal sensing and stimulation parameters through an automated algorithm that evaluates test recording configurations and selects the best parameters without requiring manual expert intervention
Solution Approach 2:
The manual expert-based parameter selection process is replaced with an automated computational algorithm that systematically evaluates multiple parameter configurations and determines optimal settings through computerized analysis of test recordings
2Measurement precision
If manual parameter setting is used for sensing biopotential signals, then expert judgment can be applied, but the process becomes complex and requires specialized knowledge
Solution Approach 1:
The system autonomously performs parameter optimization by automatically executing test recordings, analyzing results, and selecting optimal parameters without requiring the user to possess specialized knowledge or perform complex manual adjustments
Solution Approach 2:
The complex manual expert-based parameter optimization process is replaced with an automated computational algorithm that systematically evaluates multiple parameter configurations and determines optimal settings through computerized analysis
3Productivity
If automated parameter optimization is implemented, then programming time is reduced, but the system complexity increases
Solution Approach 1:
The stimulation device is designed to perform multiple functions including delivering neurostimulation, sensing biopotential signals, executing automated parameter optimization algorithms, and evaluating test recording configurations, thereby integrating complexity into a single multi-functional system
Solution Approach 2:
The system automatically performs preliminary parameter optimization and testing before actual therapy delivery by evaluating multiple test recording configurations and pre-determining optimal sensing and stimulation parameters
Data Source
AI summary
An example of a system for delivering neurostimulation and sensing one or more signals may include a programming control circuit and a parameter control circuit. The programming control circuit may be configured to control the delivery of the neurostimulation according to stimulation parameters and the sensing of a target neural signal including target neural responses according to sensing parameters. The parameter control circuit may be configured to determine the stimulation parameters and the sensing parameters and may include a recording analyzer. The recording analyzer may be configured to evaluate a sequence of test recording configurations each including a set of recording configuration parameters selected from the stimulation parameters and the sensing parameters and to determine one or more recording configurations suitable for detection of the target neural responses using an outcome of the evaluation.