Deep Brain Stimulation Parameter Optimization via Latent Symptom Evaluation
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Solution Overview
Problem
Current deep brain stimulation (DBS) systems face challenges in optimizing treatment for neurological disorders due to time-consuming programming processes, difficulty in balancing side effects and optimal treatment, and the inability for patients to subjectively feel the effects of stimulation, leading to suboptimal parameter selection and prolonged programming sessions.
Innovation Solution
A neurostimulation system that includes control circuitry for conveying electrical stimulation energy according to predetermined periods of time and adjusting parameters based on symptom response, using a patient monitor to evaluate effects and automatically determine optimal stimulation parameters, allowing for remote and efficient programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming methods are used to optimize stimulation parameters, then treatment effectiveness can be adjusted, but programming time becomes excessively long and complex
Solution Approach 1:
The system automatically evaluates stimulation parameter effectiveness and adjusts parameters without requiring continuous manual intervention. The processor independently determines optimal parameters based on evaluation data, enabling the system to serve itself in the optimization process.
Solution Approach 2:
The system implements a feedback loop where stimulation effects are evaluated and this information is used to adjust subsequent stimulation parameters. The processor receives evaluation results and uses them to automatically modify parameters, creating a closed-loop control system that improves treatment while reducing programming time.
2Reliability
If high amplitude stimulation is used to ensure adequate treatment, then therapeutic benefit is achieved, but energy consumption increases and side effects occur
Solution Approach 1:
The system dynamically adjusts stimulation parameters including amplitude based on evaluated effectiveness. Rather than using fixed high amplitude settings, the processor modifies parameters in response to feedback, finding the minimum effective amplitude that provides therapeutic benefit while conserving energy and reducing side effects.
3Measurement precision
If extensive parameter testing is performed to find optimal stimulation settings, then treatment precision improves, but programming complexity and time increase
Solution Approach 1:
The system performs preliminary evaluation of stimulation effects before final parameter selection. By evaluating parameter effectiveness in advance and using this information to guide subsequent adjustments, the system reduces the need for extensive trial-and-error testing during the programming phase.
Data Source
AI summary
Neurostimulation systems and methods for providing therapy to a patient suffering from a symptom of a disease that latently responds to electrical stimulation therapy are provided. First electrical stimulation energy is conveyed to or from a tissue region of the patient in accordance with a first set of stimulation parameters, thereby affecting the symptom. A predetermined period of time estimated for the symptom to resolve in response to electrical stimulation therapy is allowed to elapse. Second electrical stimulation energy is conveyed to or from the tissue region in accordance with a second set of stimulation parameters different from the first set of stimulation parameters.