DBS Electrode Arrangement Optimization via Search Waveforms
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Solution Overview
Problem
Current Deep Brain Stimulation (DBS) systems lack clinical evidence for the majority of commonly used stimulation parameters, which are often determined by clinical experience and technical restrictions, rather than optimized therapeutic effects.
Innovation Solution
A method for determining electrical stimulation parameters using a leadwire with one or more electrodes, involving the application of a search waveform at various electrode arrangements to find optimal parameters for therapeutic effect, and then applying a therapy waveform based on these determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If stimulation parameters are selected based on clinical experience and technical restrictions, then device operation is simplified, but therapeutic optimization is limited
Solution Approach 1:
The system performs preliminary testing by applying search waveforms with different polarities before final therapy delivery. This preliminary action identifies the optimal polarity for each patient, resolving the contradiction by establishing evidence-based parameters before committed therapeutic use.
Solution Approach 2:
The system incorporates feedback mechanisms where patient response to search waveforms informs subsequent therapy waveform configuration. This feedback loop enables optimization of therapeutic parameters based on individual patient characteristics, improving reliability while maintaining operational simplicity through automated decision-making.
2Reliability
If a wider parameter space is explored for stimulation, then therapeutic optimization improves, but device complexity increases
Solution Approach 1:
The parameter space is segmented into distinct phases: search waveform testing with multiple polarities, followed by therapy waveform delivery with optimized parameters. This segmentation allows comprehensive parameter exploration without overwhelming device complexity, as each phase builds systematically on the previous results.
Solution Approach 2:
The system systematically changes key parameters (polarity, amplitude, pulse width) during the search phase to identify optimal settings. By automating parameter variation and selection, the device manages complexity while thoroughly exploring the parameter space to achieve therapeutic optimization.
3Measurement precision
If search waveforms use leading phases with highest amplitude, then therapeutic window determination is improved, but energy consumption increases
Solution Approach 1:
The search waveform uses leading phases with amplitude equal to or greater than any other phase to ensure sufficient signal strength for accurate therapeutic window detection. This partial excessive action guarantees reliable measurement while the brief duration of search phases limits overall energy consumption compared to continuous high-amplitude therapy.
Data Source
AI summary
Methods of providing neuromodulation therapy to a patient are disclosed herein. In particular, methods of applying deep brain stimulation (DB S) for the treatment of Parkinson's disease (PD) and related disorders are disclosed. Aspects of the methods involve using stimulation waveforms having a first polarity (e.g., cathodic stimulation) to determine an optimum arrangement of electrodes for providing the therapy (i.e., identifying a sweet-spot for stimulation). Therapy is then provided using the optimum arrangement of electrodes to deliver stimulation waveforms having the opposite polarity (e.g., anodic stimulation).