Brain Stimulation Frequency Tuning via Bioelectrical Resonance
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Solution Overview
Problem
Current electrical stimulation therapies for neurological conditions, such as Parkinson's disease, often rely on predetermined frequency settings, which may not be tailored to individual patients' needs, leading to suboptimal symptom relief and increased energy consumption.
Innovation Solution
A method and system for identifying a bioelectrical resonance response in a patient's brain by varying stimulation frequencies and monitoring bioelectrical oscillations, allowing for customization of stimulation parameters based on resonant properties of specific brain areas to evoke a pronounced oscillatory response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined frequency settings are used for electrical stimulation therapy, then the device complexity is reduced and ease of operation is improved, but the adaptability to individual patients' needs deteriorates and therapeutic effectiveness is suboptimal
Solution Approach 1:
The system dynamically adjusts the stimulation frequency based on real-time monitoring of bioelectrical oscillations. The frequency is not fixed but adapts to the patient's individual resonance characteristics, transforming a static predetermined frequency system into a dynamic responsive system that optimizes therapeutic effectiveness for each patient.
Solution Approach 2:
The system implements a feedback loop where bioelectrical oscillations are continuously monitored and used to adjust the stimulation frequency. The monitored oscillation data feeds back to the frequency adjustment mechanism, creating a closed-loop control system that automatically adapts to individual patient needs without requiring manual reconfiguration.
2Device complexity
If predetermined frequency settings are used, then the device complexity is reduced, but energy consumption increases due to non-optimized stimulation parameters
Solution Approach 1:
The system employs dynamic frequency adjustment that responds to the patient's physiological state. By continuously adapting the stimulation frequency to match the brain's natural resonance, the system optimizes energy utilization and avoids wasteful stimulation at ineffective frequencies, thereby reducing overall energy consumption.
Solution Approach 2:
The system changes the stimulation frequency parameter based on monitored bioelectrical oscillations. By adjusting this critical parameter to match the patient's individual resonance frequency, the system achieves more efficient energy transfer and maximizes therapeutic effect per unit of energy consumed, reducing total energy requirements.
3Adaptability or versatility
If individualized frequency customization is implemented, then adaptability and therapeutic effectiveness are improved, but the device complexity and measurement requirements increase
Solution Approach 1:
The system combines multiple functions within a single integrated platform: frequency monitoring, oscillation detection, resonance identification, and automatic frequency adjustment. This multi-functional approach achieves individualized therapy customization without proportionally increasing device complexity, as all functions operate within a unified control architecture.
Solution Approach 2:
The system performs self-adjustment by automatically monitoring bioelectrical oscillations and tuning the stimulation frequency without requiring external intervention or complex manual configuration. The device serves itself by using its own monitored data to optimize its operation, reducing the burden on users and simplifying the overall system interface.
4Manufacturing precision
If bioelectrical oscillation monitoring is implemented to identify resonance frequency, then adaptability and therapeutic precision are improved, but the measurement precision requirements and device complexity increase
Solution Approach 1:
The system replaces complex manual tuning and trial-and-error procedures with automated electronic monitoring and control. By using electronic sensors to detect bioelectrical oscillations and electronic control circuits to adjust frequency, the system achieves high precision without the complexity of manual adjustment mechanisms or multiple external testing devices.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Customized stimulation frequencies can provide more effective therapeutic outcomes, reduce energy consumption, and minimize side effects by matching the stimulation frequency to the brain's natural resonance, leading to improved symptom management and extended battery life in implantable devices.
Implementation Method 1
identifying a stimulation frequency parameter that evokes a bioelectrical resonance response of an area of a patient's brain
Data Source
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AI summary
Various methods and apparatuses are disclosed that concern delivering electrical stimulation to a brain at a plurality of different stimulation frequencies, sensing one or more bioelectrical signals, and identifying a bioelectrical resonance response of the brain to the electrical stimulation. The bioelectrical resonance response may be identified based on a parameter of oscillation of the one or more bioelectrical signals and indicative of resonance of an area of the brain to one stimulation frequency of the plurality of stimulation frequencies. A stimulation frequency parameter for a therapy may be set based on the identified bioelectrical resonance response, wherein the stimulation frequency parameter is set at or near the one stimulation frequency.