Neural Stimulation Pulse Patterns With Frequency-Preserving Mutation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Standard genetic algorithms perform poorly when applied to designing and selecting temporal patterns of neural stimulation due to their inadequacy in maintaining the average frequency of stimulation pulses, which is crucial for effective neural stimulation.
Innovation Solution
A modified genetic algorithm incorporating pulse mutation, competitive immigrants, variable pattern length, and predictive immigrants to maintain average frequency and improve convergence speed and accuracy in determining optimal temporal patterns of neural stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard genetic algorithm is used to design temporal patterns of neural stimulation, then the algorithm can search for optimal solutions, but it fails to maintain the average frequency of stimulation pulses which is crucial for effective neural stimulation
Solution Approach 1:
The patent modifies the genetic algorithm by changing the mutation operator to specifically preserve the average frequency parameter. Instead of standard bit-flip mutation, the algorithm uses constrained mutation that only allows changes which maintain the target average frequency, thereby resolving the contradiction between optimization capability and frequency maintenance reliability
Solution Approach 2:
The patent implements feedback mechanisms where the average frequency is continuously monitored and used to guide the selection and mutation processes. Patterns that deviate from the target frequency are penalized or corrected, creating a feedback loop that ensures frequency maintenance while still allowing the algorithm to search for optimal temporal patterns
2Productivity
If the temporal pattern is optimized for effectiveness, then neural stimulation efficiency improves, but the complexity of determining optimal patterns increases
Solution Approach 1:
The patent segments the temporal pattern optimization into distinct genetic algorithm operations: selection, crossover, and constrained mutation. Each operation handles a specific aspect of pattern optimization while maintaining the average frequency constraint, reducing the overall complexity by breaking down the optimization problem into manageable, modular components
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing the average frequency of parent patterns before mutation. This preliminary computation allows the mutation operator to efficiently generate offspring that maintain the target frequency without requiring complex real-time calculations during the optimization process, thereby improving efficiency while managing complexity
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods for determining optimal temporal patterns of neural stimulation are disclosed. According to an aspect, a method includes selecting a temporal pattern for neural stimulation. The method also includes determining a mutation type for altering a pattern of pulses of the temporal pattern. The method also includes identifying a location within the pattern of pulses of the temporal pattern to alter based on the determined mutation type. The method further includes altering the pattern of pulses of the temporal pattern based on the identified location and mutation type for application of the altered temporal pattern to a subject.