Stimulation Electrode Selection via Beta Band Power Analysis
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Solution Overview
Problem
Current methods for selecting stimulation electrodes in medical devices for treating movement disorders are time-consuming and require significant clinical expertise, as they do not consider specific anatomical and physiological characteristics of individual patients, leading to inefficiencies in identifying effective electrode combinations for delivering electrical stimulation therapy.
Innovation Solution
The method involves sensing bioelectrical signals within the brain using multiple electrode combinations, analyzing frequency domain characteristics such as beta band power, and selecting stimulation electrodes based on these characteristics to optimize electrical stimulation delivery, thereby reducing the time and expertise required to find efficacious combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to select stimulation electrodes, then clinical expertise and time are required to identify effective electrode combinations, but this leads to time-consuming programming sessions and inefficiencies in therapy optimization
Solution Approach 1:
The system performs automatic electrode combination selection based on impedance measurements and signal quality analysis, eliminating the need for manual clinician intervention in the selection process. The medical device autonomously evaluates multiple electrode combinations and identifies the optimal configuration without requiring extensive clinical expertise or time-consuming manual programming sessions
Solution Approach 2:
The system measures and compares impedance parameters across different electrode combinations to objectively identify the optimal configuration. By using quantitative impedance data and signal quality metrics, the system replaces subjective manual selection with objective parameter-based automation, resolving the contradiction between selection accuracy and programming time
2Productivity
If multiple electrode combinations are tested manually to find the optimal configuration, then thorough evaluation is possible, but this increases the complexity and duration of the programming process
Solution Approach 1:
The system replaces manual mechanical testing of electrode combinations with automated electrical impedance measurements. The processor automatically evaluates multiple electrode configurations by measuring impedance parameters, substituting the manual mechanical programming process with automated electrical characterization, thereby increasing productivity while managing complexity through algorithmic evaluation
3Adaptability or versatility
If conventional electrode selection methods are used, then simplicity of operation is maintained, but the ability to tailor therapy to individual patient anatomy and physiology is reduced
Solution Approach 1:
The system performs preliminary automated evaluation of electrode combinations by measuring impedance and signal quality characteristics before final therapy configuration. This preliminary automated assessment tailors the electrode selection to the specific patient's anatomy and physiology, enabling customization while keeping the final programming step simple and straightforward for the clinician
Data Source
AI summary
Bioelectrical signals may be sensed within a brain of a patient with a plurality of sense electrode combinations. A stimulation electrode combination for delivering stimulation to the patient to manage a patient condition may be selected based on the frequency band characteristics of the sensed signals. In some examples, a stimulation electrode combination associated with the sense electrode combination that sensed a bioelectrical brain signal having a relatively highest relative beta band power level may be selected to deliver stimulation therapy to the patient. Other frequency bands characteristics may also be used to select the stimulation electrode combination.