Stochastic Neuromodulation Parameter Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neural modulation technologies, such as Spinal Cord Stimulation (SCS) and Deep Brain Stimulation (DBS), often rely on fixed stimulation parameters, which may not fully address the variability and complexity of neural activity, potentially limiting therapeutic efficacy and increasing side effects.
Innovation Solution
The development of a neuromodulation system that stochastically modulates neuromodulation parameters, such as pulse amplitude, pulse width, and pulse rate, using a stochastic modulator and a modulation model, to generate a stochastically-modulated neuromodulation field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed stimulation parameters are used in neural modulation, then device complexity is reduced and ease of operation is improved, but therapeutic efficacy is limited and side effects increase
Solution Approach 1:
The patent applies dynamics by transitioning from fixed stimulation parameters to dynamically varying parameters. The system randomly selects stimulation parameters (amplitude, pulse width, frequency) from predefined ranges during operation, enabling the neuromodulation system to adapt to changing neural conditions and improve therapeutic efficacy while maintaining operational simplicity through automated parameter selection.
2Device complexity
If fixed stimulation parameters are used, then device complexity is reduced, but adaptability to varying neural activity is worsened
Solution Approach 1:
The system implements dynamic parameter selection where stimulation amplitude, pulse width, and frequency are randomly varied within therapeutic ranges during operation. This allows the device to adapt to varying neural activity patterns without requiring complex real-time adjustment mechanisms, maintaining relatively simple device architecture while improving adaptability.
Solution Approach 2:
The patent changes multiple stimulation parameters simultaneously (amplitude, pulse width, frequency) according to a random selection process. By varying these parameters within predefined therapeutic ranges, the system achieves adaptability to different neural conditions without increasing device complexity, as the parameter changes are implemented through software-controlled random selection rather than hardware complexity.
3Reliability
If stochastically-modulated parameters are used, then therapeutic effect is enhanced and side effects are reduced, but device complexity increases
Solution Approach 1:
The system employs dynamic random selection of stimulation parameters to enhance therapeutic effect and reduce side effects. A controller randomly selects from predefined ranges of amplitude, pulse width, and frequency values, creating stochastic modulation that improves clinical outcomes while avoiding the need for complex real-time parameter optimization algorithms, thus limiting the increase in device complexity.
Solution Approach 2:
The patent implements periodic re-selection of stimulation parameters at defined intervals or after a certain number of pulses. This periodic action creates stochastic modulation patterns that enhance therapeutic efficacy by preventing neural adaptation, while the structured periodic nature of the parameter changes keeps the control logic relatively simple compared to continuous real-time optimization systems.
Data Source
AI summary
An example of a system may include an electrode arrangement and a neuromodulation device configured to use electrodes in the electrode arrangement to generate a neuromodulation field. The neuromodulation device may include a neuromodulation generator, a neuromodulation control circuit and a storage. The storage may include a stochastically-modulated neuromodulation parameter set and the stochastically-modulated neuromodulation parameter set may include at least one stochastically-modulated parameter. The controller may be configured to control the neuromodulation generator using the stochastically-modulation parameter set to generate the neuromodulation field.