Electrical Stimulation Parameter Selection Using Iterative Electrode Testing
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Solution Overview
Problem
Current implantable electrical stimulation systems face challenges in determining optimal electrode combinations and stimulation parameters to achieve therapeutic benefits while minimizing side effects, particularly in complex nerve anatomy where orientation and response can vary significantly.
Innovation Solution
A method and system for selecting stimulation parameters that involve testing various electrode combinations and parameter sets to identify optimal configurations for specific nerve regions, using a processor to analyze feedback and adjust electrode combinations and parameters to maximize therapeutic effect while reducing side effects, including alternating between different electrode groups for sustained efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple electrode combinations are tested to identify optimal configurations for specific nerve regions, then therapeutic effectiveness is improved, but device complexity and programming time increase
Solution Approach 1:
The system performs preliminary testing of multiple electrode combinations during initial programming to identify optimal configurations for specific nerve regions. This preliminary action establishes a foundation of tested, effective electrode combinations that can be automatically selected later without requiring complex real-time decisions, thereby improving therapeutic effectiveness while managing device complexity.
Solution Approach 2:
The system uses feedback from sensory and motor threshold measurements to automatically evaluate and rank electrode combinations. By measuring electrical thresholds and using these measurements to guide selection, the system objectively identifies optimal configurations without requiring extensive manual testing, thus improving reliability while reducing the practical burden of complexity.
2Measurement precision
If multiple electrode combinations are tested to identify optimal configurations, then measurement precision of nerve response is improved, but time required for programming increases
Solution Approach 1:
The system performs preliminary measurements of electrical thresholds for multiple electrode combinations during initial programming. These preliminary measurements establish baseline data that enables precise identification of optimal electrode configurations. By completing this measurement phase upfront, the system achieves high measurement precision while reducing the need for repeated testing during subsequent programming sessions.
Solution Approach 2:
The system uses feedback from threshold measurements to automatically evaluate and compare electrode combinations. By measuring electrical thresholds and using these measurements to guide selection, the system objectively and precisely identifies optimal configurations without requiring extensive manual testing, thus improving measurement precision while reducing programming time.
3Reliability
If stimulation parameters are optimized for specific nerve regions, then therapeutic benefits are enhanced, but device complexity increases
Solution Approach 1:
The system optimizes stimulation parameters specifically for identified target nerve regions rather than using uniform settings across all electrodes. By tailoring amplitude, pulse width, and frequency parameters to the characteristics of specific nerve regions, the system enhances therapeutic benefits. The processor automatically manages this complexity by selecting from pre-tested parameter sets, maintaining reliability while managing device complexity.
4Productivity
If extensive electrode combination testing is performed, then productivity of therapy customization is improved, but loss of time during initial setup increases
Solution Approach 1:
The system performs extensive electrode combination testing as a preliminary action during initial programming to establish a library of optimized configurations. Although this requires significant initial setup time, it enables rapid therapy customization afterward by automatically selecting from pre-tested combinations based on patient-specific measurements, thereby improving long-term productivity of therapy customization.
Solution Approach 2:
The system uses feedback from threshold measurements to automatically evaluate and rank electrode combinations during the testing phase. This automated feedback mechanism reduces the need for manual assessment and allows the system to efficiently identify optimal configurations, improving the productivity of therapy customization while making the initial setup time more efficient through automation.
Data Source
AI summary
Methods and systems for selecting electrical stimulation parameters for an electrical stimulation device implanted in a patient can use an iterative process for identifying electrodes for stimulation, as well as suitable stimulation parameters. The process begins with an initial set of electrode combinations to identify regions of the nerve or other tissue for stimulation. This leads to selection of other electrode combinations to test, followed by the selection of multiple electrode groups (which can include three or more electrodes) for stimulation.