Neurostimulation Setting Clustering for Faster Patient Optimization
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Solution Overview
Problem
Existing neurostimulation optimization processes are time-consuming and inefficient, often failing to provide sufficient relief during trial periods, particularly for patients with unique responses to stimulation patterns, leading to suboptimal or delayed therapeutic benefits.
Innovation Solution
A method utilizing Bayesian optimization and hierarchical clustering to derive population-based neurostimulation settings, allowing for rapid identification of personalized settings by mapping patients to clusters based on user preferences, leveraging average surfaces to iteratively refine settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neurostimulation optimization processes are used, then comprehensive testing of stimulation parameters is performed, but the process is time-consuming and delays therapeutic benefits
Solution Approach 1:
The system performs preliminary clustering analysis on population data before patient testing, pre-identifying likely effective parameter ranges. This preliminary action reduces the search space for individual patients, allowing faster convergence to optimal settings without compromising accuracy.
Solution Approach 2:
The patent segments the population into distinct clusters based on response patterns to stimulation parameters. By dividing the continuous parameter space into discrete clusters, the system can efficiently guide patients to appropriate clusters using fewer test settings, reducing trial period duration while maintaining optimization accuracy.
2Adaptability or versatility
If population-based optimized settings are used directly, then personalization is limited, but the process is faster
Solution Approach 1:
The system adds a hierarchical dimension to the optimization process by combining population-level clustering with individual patient mapping. This two-dimensional approach (population clusters + individual preferences) enables both personalization and speed, as patients are quickly mapped to pre-identified clusters rather than searching the entire parameter space individually.
Solution Approach 2:
The system dynamically adapts the optimization process by iteratively refining cluster assignments based on patient feedback. As patients provide preference data, the system updates cluster boundaries and reassigns patients, allowing the personalization to evolve dynamically while maintaining efficiency through the structured cluster framework.
3Measurement precision
If multiple test settings are presented to discriminate between clusters, then accurate patient mapping is achieved, but the number of settings increases complexity
Solution Approach 1:
The system extracts key discriminative features from the full set of stimulation parameters to create a reduced subset of test settings. By identifying and extracting only the most informative parameters for cluster discrimination, the system achieves accurate patient mapping with fewer test settings, reducing complexity while maintaining precision.
Data Source
AI summary
User-specific neurostimulation settings are efficiently determined and optimized based on an optimized set of population-based neurostimulation settings. The population data are clustered and a set of test settings for a new user are selected as settings that efficiently discriminate between the clusters. User preference of the test settings are used to map the user to a particular cluster of settings, which can be used to determine user-specific neurostimulation settings. The user-based settings can be iteratively updated and/or optimized using information from the population data, such as by using average preference score surfaces in the population data to identify and/or filter new test settings for the user.