DBS Stimulation Parameter Selection Using LFP Signal Analysis
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Solution Overview
Problem
Conventional deep brain stimulation (DBS) methods require a laborious and time-consuming trial-and-error process to select optimal stimulation parameters, relying on subjective clinical assessments, as the therapeutic subregions are not easily identifiable on imaging and vary patient-specific.
Innovation Solution
An algorithm that uses stimulation-induced changes in oscillatory brain activity as an objective measure to identify optimal stimulation parameters, utilizing local field potential recordings and computational modeling to predict optimal settings during a single recording session.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual trial-and-error methods are used to select DBS parameters, then clinicians can adjust stimulation settings based on patient response, but the process becomes laborious and time-consuming requiring multiple monthly visits
Solution Approach 1:
The patent replaces the manual mechanical process of parameter adjustment with an automated computational system. The algorithm automatically analyzes LFP signals and determines optimal stimulation parameters without requiring manual trial-and-error adjustment by clinicians, thereby reducing programming time while maintaining or improving selection accuracy.
Solution Approach 2:
The system enables self-determination of stimulation parameters through automated algorithmic analysis. The computational algorithm independently processes the LFP data and generates parameter recommendations without requiring iterative manual intervention, allowing the system to 'serve itself' in determining optimal settings.
2Measurement precision
If manual visual assessment of patient symptoms is used to evaluate stimulation effectiveness, then clinicians can identify therapeutic benefit, but the assessment remains subjective and requires repeated clinic visits
Solution Approach 1:
The patent replaces subjective visual clinical assessment with an automated computational algorithm that objectively analyzes LFP signals. This substitution transforms the evaluation process from manual visual inspection to automated signal processing, improving both measurement precision and productivity by eliminating the need for repeated subjective assessments across multiple visits.
Solution Approach 2:
The system implements automated feedback loops where the algorithm continuously monitors LFP changes in response to different stimulation parameters and uses this feedback to determine optimal settings. This objective feedback mechanism replaces subjective clinical assessment and enables more efficient parameter optimization.
3Ease of manufacture
If the therapeutic subregion is not easily identifiable on imaging, then electrode placement can be performed in visible brain regions, but the actual therapeutic target remains difficult to locate and varies patient-specifically
Solution Approach 1:
The patent replaces reliance on imaging-based anatomical identification with a functional identification method using LFP signal analysis. The algorithm automatically identifies the therapeutic subregion by detecting characteristic neural oscillations, eliminating the need for precise pre-surgical imaging localization and enabling accurate target identification that adapts to patient-specific variations.
Data Source
AI summary
A method and apparatus are provided for determining stimulation parameters for DBS. The method includes receiving first data indicating a value of a local field potential (LFP) over a first time period from first contacts positioned adjacent a brain region. The method further includes receiving third data indicating a value of a LFP over second time periods from the first contacts, where the brain region is stimulated by second contacts over second time periods based on stimulation parameter values. The method further includes determining a frequency band encompassing a difference between a first frequency spectrum of the first data and a second frequency spectrum of the third data. The method further includes determining a value of the second frequency spectrum over the frequency band for each stimulation parameter value. The method further includes determining a value of an optimal stimulation parameter based on the value of the second frequency spectrum and each stimulation parameter value.