Neural Sensing Parameter Selection 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 is time-consuming and requires significant manual effort.
Innovation Solution
An automated system and method for determining optimal recording configurations by evaluating a sequence of test recording configurations, including stimulation and sensing parameters, to enhance the detection of target neural responses such as ECAPs, using a recording analyzer to optimize signal-to-noise ratio and feature magnitude.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter selection is used for sensing biopotential signals, then measurement precision can be achieved, but loss of time increases significantly
Solution Approach 1:
The system performs self-optimization by automatically evaluating test recording configurations and determining optimal sensing parameters without requiring manual intervention. The implantable neurostimulator autonomously tests different parameter combinations and selects the best configuration for detecting neural signals, eliminating the time-consuming manual programming process while maintaining detection accuracy.
Solution Approach 2:
The system uses feedback from evaluating test recording configurations to iteratively improve parameter selection. By monitoring the quality of detected neural signals across different parameter settings and using this feedback to guide the selection of optimal parameters, the system achieves accurate detection automatically without manual intervention.
2Productivity
If automated parameter determination is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The implantable neurostimulator is designed with multi-functionality, combining both stimulation delivery and automated sensing parameter optimization capabilities in a single device. This universal approach allows the device to perform multiple functions (stimulation, sensing, and automated parameter determination) without requiring separate external programming equipment, thereby increasing productivity while managing complexity through integration.
Solution Approach 2:
The device performs self-configuration by automatically determining optimal sensing parameters during operation. This self-service capability eliminates the need for complex external programming systems and manual intervention, achieving high productivity through automated internal processes while keeping the overall system complexity manageable.
3Measurement precision
If extensive parameter evaluation is performed, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system evaluates a limited set of predetermined test recording configurations rather than exhaustively testing all possible parameters. By selecting and evaluating only the most promising parameter combinations based on expected signal characteristics, the system achieves sufficient measurement precision for clinical purposes while significantly reducing the energy consumption associated with comprehensive parameter evaluation.
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.