Bayesian Optimization for Deep Brain Stimulation Settings
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Solution Overview
Problem
Current deep brain stimulation (DBS) treatments for epilepsy lack a systematic approach to optimize stimulation settings, such as frequency, pulse width, and amplitude, which may not be optimal for all patients, leading to suboptimal patient outcomes.
Innovation Solution
A method that measures neural activity using recording electrodes while delivering neurostimulation with varying settings, using Bayesian optimization to determine subject-specific settings by generating a response surface from the measured data, allowing for the selection of optimal stimulation parameters that minimize neural activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single fixed stimulation frequency and pulse width are used clinically, then the treatment is simple to implement, but the patient outcome is suboptimal because the setting is not optimal for all patients
Solution Approach 1:
The patent implements dynamic adjustment of stimulation parameters by systematically varying frequency and pulse width settings during the treatment process. The system transitions from static, fixed parameters to dynamic, adaptable parameters that can be optimized for each patient's neural response characteristics, thereby improving patient outcomes while maintaining manageable complexity through automated protocols
Solution Approach 2:
The patent applies parameter changes by systematically modifying stimulation frequency and pulse width values to identify optimal settings for each patient. The method involves testing multiple parameter combinations and selecting the configuration that produces the desired neural response, thus resolving the contradiction between using fixed simple settings and achieving optimal personalized outcomes
2Reliability
If multiple different neurostimulation settings are tested to find optimal parameters, then patient outcome can be improved through personalized settings, but the time required for treatment optimization increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing a systematic framework for parameter optimization that guides the testing process. The method prepares a structured approach to evaluating multiple settings in advance, which streamlines the optimization process and reduces the time required to identify effective parameters while maintaining comprehensive evaluation
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring neural responses to different stimulation settings and using this information to guide subsequent parameter selections. The system measures neural activity in response to each setting and uses this feedback to efficiently narrow down optimal parameters, thereby reducing overall optimization time while improving outcome reliability
3Measurement precision
If neural activity is measured while delivering neurostimulation with multiple settings, then optimal subject-specific settings can be determined, but the measurement and data processing complexity increases
Solution Approach 1:
The patent applies universality by designing a measurement and processing framework that handles multiple stimulation settings through a unified analytical approach. The system uses consistent measurement protocols and data processing methods across all parameter combinations, which standardizes the complexity and makes it manageable while maintaining high measurement precision for determining subject-specific optimal settings
Data Source
AI summary
Described here are systems and methods for testing neurostimulation settings and measuring their effects on neural activity, which may then be used to select subject-specific neurostimulation settings. In general, the present disclosure provides systems and methods that utilize neural recordings to help determine the neurostimulation settings that maximally reduce a particular neural activity.