Electrode Ranking for Electrical Stimulation Therapy
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Solution Overview
Problem
Current medical devices face challenges in efficiently programming electrodes for electrical stimulation therapy, as the process is time-intensive and requires iterative trials to optimize stimulation parameters, leading to potential side effects and reduced therapeutic efficacy.
Innovation Solution
The system determines a subset of electrodes based on position and direction relative to target tissue regions and ranks them based on a ratio of electrical efficiency to therapeutic window. This ranking is used to select a final electrode set through either a dynamically minimalistic or systematically reductive search, aided by biomarkers like local field potentials to refine the selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative trials are used to optimize stimulation parameters, then therapeutic efficacy is improved, but programming time increases
Solution Approach 1:
The system performs preliminary calculations of the electric field distribution and electrode rankings before actual stimulation delivery. By pre-computing which electrodes are most likely to achieve therapeutic effects based on the ratio of electrical efficiency to therapeutic window, the system reduces the number of iterative trials needed during clinical programming, thus reducing programming time while maintaining therapeutic efficacy.
2Reliability
If multiple electrodes are used for stimulation, then therapeutic efficacy is improved, but device complexity increases
Solution Approach 1:
The system ranks electrodes based on their individual characteristics and the local requirements of the target tissue region. By identifying and activating only the specific electrodes that provide the optimal balance of electrical efficiency and therapeutic window for each local region, the system achieves effective stimulation without requiring all electrodes to be active, thus reducing programming complexity while maintaining therapeutic efficacy.
Solution Approach 2:
The system divides the electrode array into ranked groups based on their effectiveness metrics. This segmentation allows the programming process to focus on selecting from pre-ranked electrode subsets rather than evaluating all possible electrode combinations, significantly reducing the complexity of programming while still achieving optimal therapeutic outcomes through the selected electrode segments.
3Object-affected harmful factors
If iterative electrode selection is performed, then side effects are reduced, but programming time increases
Solution Approach 1:
The system pre-calculates and ranks electrodes based on their likelihood of producing side effects versus therapeutic benefits, using the ratio of electrical efficiency to therapeutic window as a predictive metric. This preliminary ranking allows clinicians to select electrodes that are predisposed to minimize side effects without requiring extensive iterative testing, thus reducing both side effects and programming time simultaneously.
Data Source
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AI summary
A system includes processing circuitry configured to determine, for each respective electrode of a plurality of electrodes, a score based on a ratio of an electrical efficiency for the respective electrode to a therapeutic window for the respective electrodes. The processing circuitry is further configured to determine, based on the score of each respective electrode, a ranking of the plurality of electrodes, and to select, based on the ranking, a subset of the plurality of electrodes for delivery of electrical stimulation therapy.