Deep Brain Stimulation Parameter Selection via Meta-Active Learning
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Solution Overview
Problem
Programming deep brain stimulation devices for conditions like Parkinson's disease is challenging due to the vast number of parameter combinations, requiring a more efficient method to quickly find the best therapy for each patient.
Innovation Solution
A system using a hybrid meta-learning and mathematical programming approach to analyze data from deep brain stimulation devices or electromyography, generating optimal stimulation parameters efficiently, safely, and computationally quickly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trial-and-error methods are used to search through parameter combinations, then the clinician can find effective therapy parameters, but the process takes months and requires multiple patient visits
Solution Approach 1:
The system performs preliminary analysis of patient-specific data (neural recordings, biomarkers) before parameter selection to predict effective parameter ranges. This pre-processing of patient data allows the system to narrow down the vast parameter space before optimization, significantly reducing the time required for clinical programming while maintaining accuracy.
Solution Approach 2:
A machine learning model acts as an intermediary between raw patient data and optimal parameter selection. The model processes complex neural recordings and biomarker data, translating them into predictions about effective stimulation parameters. This intermediary layer enables rapid, accurate parameter selection without requiring months of trial-and-error clinical testing.
2Reliability
If a vast number of parameter combinations are evaluated to ensure optimal therapy, then treatment effectiveness is maximized, but computational complexity and time increase significantly
Solution Approach 1:
The parameter search space is segmented into manageable regions based on patient-specific characteristics extracted from neural recordings and biomarkers. Instead of evaluating all possible parameter combinations, the system divides the search into targeted segments relevant to each patient's pathology and anatomy, reducing computational complexity while maintaining therapy effectiveness.
Solution Approach 2:
The system dynamically changes search parameters and evaluation criteria based on patient-specific data analysis. By adapting the parameter search strategy to individual patient characteristics (such as lesion location, neural response patterns), the system efficiently identifies effective parameters without exhaustively searching the entire parameter space.
3Measurement precision
If manual parameter programming is performed by neurologists over multiple visits, then patient-specific optimization is achieved, but neurologists' time is consumed and patient access is limited
Solution Approach 1:
The system enables self-service parameter optimization by automatically analyzing patient neural recordings and biomarker data to generate personalized parameter recommendations. This automated self-service capability maintains patient-specific optimization while freeing neurologists from routine programming tasks, allowing them to focus on complex clinical decisions and increasing overall clinic throughput.
Solution Approach 2:
The system incorporates real-time feedback from neural recordings and biomarker measurements during parameter optimization. By continuously monitoring patient responses and adjusting parameters based on this feedback, the system achieves accurate patient-specific optimization automatically, eliminating the need for multiple manual adjustment visits and increasing neurologist productivity.
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 significantly reduces the time needed to find effective stimulation parameters, improving patient outcomes and freeing up neurologists' time, while also being scalable for other diseases like epilepsy.
Implementation Method 1
instruct the deep brain stimulation device to deliver electromagnetic energy to at least a portion of a brain of a user
Implementation Method 2
analyze, using a machine learning model, data from the deep brain stimulation device or an electromyography
Data Source
AI summary
An exemplary embodiment of the present disclosure provides a system for generating a set of stimulation parameters, the system comprising at least one processor and a memory in communication with the at least one processor and having stored thereon instructions that, when executed by the at least one processor, is configured to cause the system to analyze, using a machine learning model, data from the deep brain stimulation device or an electromyography and generate, in response to analyzing, at least in part, the data, a set of stimulation parameters for deep brain stimulation.