Directional Electrode Sensing for Faster DBS Programming
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Solution Overview
Problem
The selection of effective stimulation parameters for deep brain stimulation (DBS) therapy is time-consuming and prone to causing undesirable side effects due to the trial-and-error approach in determining appropriate electrode combinations and parameters.
Innovation Solution
A system that utilizes sensed local field potentials (LFPs) to identify electrodes closest to a target tissue region by analyzing electrical signals between different electrode combinations, allowing for the determination of directional stimulation parameters based on signal characteristics such as spectral power, thereby reducing the time required for parameter selection and minimizing side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trial-and-error approach is used to determine appropriate electrode combinations and parameters, then therapy efficacy can be achieved, but the process is time-consuming and prone to causing undesirable side effects
Solution Approach 1:
The system performs preliminary sensing of local field potentials (LFPs) across multiple electrode combinations before finalizing therapy parameters. By pre-identifying electrodes closest to the target region through signal analysis, the system eliminates the need for extensive trial-and-error stimulation testing, thereby reducing programming time while maintaining therapy efficacy
Solution Approach 2:
The system uses feedback from sensed LFP signals to guide electrode selection and parameter optimization. By analyzing signal characteristics such as spectral power and comparing them against reference values, the system iteratively refines electrode combination selection, ensuring effective therapy with minimal time investment and reduced risk of side effects
2Reliability
If a trial-and-error approach is used to determine appropriate electrode combinations, then therapy efficacy can be achieved, but undesirable side effects are more likely to occur
Solution Approach 1:
The system performs preliminary identification of optimal electrode combinations by sensing LFPs and analyzing signal characteristics before delivering stimulation therapy. This pre-selection process based on signal strength and spectral analysis ensures that therapy parameters are optimized in advance, reducing the likelihood of adverse effects while maintaining efficacy
Solution Approach 2:
The system replaces the mechanical trial-and-error testing process with an automated signal-based selection mechanism. By substituting empirical testing with computational analysis of LFP spectral power and signal characteristics, the system achieves more precise electrode selection with fewer side effects
3Reliability
If multiple electrode combinations are tested to find optimal parameters, then effective therapy can be delivered, but the complexity of the programming process increases
Solution Approach 1:
The system performs self-service by automatically sensing LFPs, analyzing signal characteristics, and identifying optimal electrode combinations without requiring extensive manual testing. The processing circuitry autonomously compares signal strength against reference values and determines appropriate electrode combinations, significantly simplifying the programming process while ensuring effective therapy delivery
Solution Approach 2:
The system replaces complex manual trial-and-error programming with automated computational analysis of neural signals. By substituting mechanical testing procedures with digital signal processing and spectral analysis, the system reduces programming complexity while maintaining high therapy efficacy through data-driven electrode selection
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more efficient and targeted electrical stimulation therapy by identifying optimal electrode combinations based on signal strength, reducing the time needed for programming and minimizing adverse effects.
Implementation Method 1
sensing, by sensing circuitry, electrical signals from a plurality of electrode combinations
Data Source
AI summary
Devices, systems, and techniques for identifying electrodes closest to a target region of tissue are described. In one example, a device includes sensing circuitry configured to sense electrical signals from a plurality of electrode combinations. Processing circuitry identifies a first electrode combination of a first subset of electrode combinations. Each electrode combination of the first subset of electrode combination includes electrodes located at different axial positions along a length of the medical lead. The processing circuitry identifies a second electrode combination of a second subset of electrode combinations. Each electrode combination of the second subset of electrode combinations includes electrodes located at a same axial position and different circumferential positions around a perimeter of the medical lead. The processing circuitry then determines a third electrode combination and controls delivery of electrical stimulation via the third electrode combination.