DBS Parameter Selection via Spectral Power Peak Detection
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Solution Overview
Problem
Current deep brain stimulation (DBS) technologies face challenges in accurately delivering electrical stimulation to treat neurological movement disorders like Parkinson's disease, as they often result in side effects such as dyskinesia due to inadequate targeting of brain regions, requiring lengthy trial-and-error processes to identify effective electrode combinations and stimulation parameters.
Innovation Solution
A system that monitors brain signals from the motor cortex and subthalamic nucleus during DBS, using spectral power analysis to detect peaks indicative of dyskinesia, allowing for real-time adjustment of stimulation parameters and electrode combinations to optimize therapy while minimizing side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DBS is delivered to treat neurological movement disorders, then treatment efficacy is improved, but side effects such as dyskinesia occur due to inadequate targeting
Solution Approach 1:
The system monitors brain signals during DBS delivery and uses spectral power analysis to detect peaks indicative of dyskinesia. This feedback loop allows real-time adjustment of stimulation parameters to eliminate harmful side effects while maintaining treatment efficacy. The processor continuously analyzes sensed brain signals and modifies stimulation based on detected neural responses.
Solution Approach 2:
The system automatically adjusts stimulation parameters including amplitude, pulse width, and electrode combinations based on spectral power analysis of brain signals. By changing these parameters in response to detected dyskinesia indicators, the system optimizes the therapeutic window to maximize benefit while minimizing harmful effects.
2Reliability
If trial-and-error processes are used to identify effective electrode combinations and stimulation parameters, then appropriate therapy can be found, but lengthy time is required
Solution Approach 1:
The system performs self-optimization by automatically analyzing brain signals and selecting appropriate stimulation parameters without requiring extensive manual trial-and-error by clinicians. The processor independently identifies effective electrode combinations and parameter settings through spectral power analysis, significantly reducing programming time while maintaining therapy effectiveness.
Solution Approach 2:
The system replaces manual clinical trial-and-error procedures with automated computational analysis. Instead of relying on time-consuming manual adjustment of parameters by clinicians, the system uses spectral power analysis and automated processing to rapidly identify optimal settings, substituting mechanical/manual processes with automated electronic analysis.
3Measurement precision
If spectral power analysis is used to detect dyskinesia peaks, then precise parameter selection is achieved, but device complexity increases
Solution Approach 1:
The system extracts only the critical spectral power information needed for dyskinesia detection from the complex brain signals. By focusing on specific frequency peaks and spectral characteristics rather than analyzing all signal components, the system achieves precise parameter selection while keeping the processing requirements manageable and the device complexity controlled.
Data Source
AI summary
Devices, systems, and techniques are described for identifying stimulation parameter values based on electrical stimulation that induces dyskinesia for the patient. For example, a method may include controlling, by processing circuitry, a medical device to deliver electrical stimulation to a portion of a brain of a patient, receiving, by the processing circuitry, information representative of an electrical signal sensed from the brain after delivery of the electrical stimulation, determining, by the processing circuitry and from the information representative of the electrical signal, a peak in a spectral power of the electrical signal at a second frequency lower than a first frequency of the electrical stimulation, and responsive to determining the peak in the spectral power of the electrical signal at the second frequency, performing, by the processing circuitry, an action.