Stimulation Electrode Selection via Brain Signal Frequency Analysis
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Solution Overview
Problem
Current implantable medical devices for electrical stimulation and therapy delivery lack an efficient method to select optimal electrode combinations for targeting specific tissue sites in the brain based on bioelectrical signals, leading to suboptimal therapy efficacy and increased time and expertise required for programming.
Innovation Solution
The method involves determining frequency domain characteristics of bioelectrical brain signals to select stimulation electrode combinations, using algorithms that compare energy levels within specific frequency bands to identify the most effective electrode configurations for delivering electrical stimulation therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual electrode selection methods are used by clinicians during programming sessions, then therapy programs can be generated, but the process requires excessive time and expertise while achieving suboptimal therapy efficacy
Solution Approach 1:
The system performs automated electrode selection based on frequency domain characteristics of sensed brain signals, eliminating the need for manual clinician intervention. The processor automatically analyzes signals from multiple electrode combinations and selects the optimal configuration, allowing the device to serve itself rather than requiring external expert operation.
Solution Approach 2:
The system transforms the electrode selection process from a manual, experience-based procedure to an automated, data-driven process by analyzing frequency domain parameters (power spectral density) of brain signals. This parameter-based approach objectively identifies optimal electrodes based on signal characteristics rather than subjective clinical judgment.
2Ease of operation
If manual electrode selection methods are used by clinicians during programming sessions, then therapy programs can be generated, but excessive expertise is required
Solution Approach 1:
The patent replaces the mechanical/cognitive process of manual electrode selection with an automated computational system. Instead of relying on clinician expertise and manual procedures, the system uses processor-based analysis of frequency domain characteristics to automatically determine optimal electrode configurations, substituting human judgment with algorithmic decision-making.
Solution Approach 2:
The device autonomously performs electrode selection without requiring external expert intervention. The processor analyzes brain signals and independently determines the optimal electrode combination, making the complex selection process self-executing rather than operator-dependent.
3Reliability
If manual electrode selection methods are used, then therapy programs can be generated, but suboptimal therapy efficacy is achieved
Solution Approach 1:
The system uses feedback from frequency domain analysis of brain signals to guide electrode selection. By continuously monitoring the power spectral density characteristics of neural activity and using this information to select electrodes that target abnormal signal patterns, the system creates a closed-loop approach that improves therapy efficacy based on actual physiological feedback.
Solution Approach 2:
The system changes from static, pre-programmed electrode selections to dynamic, signal-based selection by analyzing frequency domain parameters. This allows the system to adapt electrode choices based on the actual spectral characteristics of brain signals, improving reliability by matching therapy delivery to physiological state.
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 can be selected based on a frequency domain characteristic of the sensed bioelectrical signals. In some examples, a stimulation electrode combination is selected based on a determination of which of the sense electrodes are located closest to a target tissue site, as indicated by the one or more sense electrodes that sensed a bioelectrical brain signal with a relatively highest value of the frequency domain characteristic. In some examples, determining which of the sense electrodes are located closest to the target tissue site may include executing an algorithm using relative values of the frequency domain characteristic.