Neural Stimulation Waveform Optimization for Energy Efficiency
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Solution Overview
Problem
Implantable electrical stimulators for neurological disorders face challenges in energy efficiency, leading to frequent battery replacements and device size issues, as existing waveform shapes are not optimized for energy efficiency and can cause tissue damage due to excessive charge delivery.
Innovation Solution
A genetic algorithm is coupled with a computational model of extracellular stimulation of a mammalian myelinated axon to derive optimized waveform shapes that are more energy and charge-efficient, reducing power consumption and prolonging battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional waveform shapes (rectangular, ramp, exponential) are used for neural stimulation, then the stimulation can be delivered to treat neurological disorders, but the energy consumption is high and battery life is limited
Solution Approach 1:
The patent applies parameter changes by optimizing the waveform shape parameters (amplitude, duration, and temporal profile) to minimize energy consumption. The genetic algorithm systematically varies these parameters to find the optimal waveform that delivers effective neural stimulation with minimal energy expenditure, directly addressing the energy efficiency vs. battery life contradiction
Solution Approach 2:
The patent employs dynamics by using a genetic algorithm to dynamically optimize waveform parameters rather than using fixed conventional shapes. The algorithm adaptively adjusts waveform characteristics based on energy efficiency metrics, enabling the system to find and maintain optimal operating parameters that extend battery life while preserving therapeutic efficacy
2Use of energy by moving object
If stimulation parameters are adjusted to improve energy efficiency, then power consumption decreases, but charge delivery may become excessive causing tissue damage
Solution Approach 1:
The patent implements feedback by using a computational model of extracellular stimulation that predicts both energy consumption and charge delivery. The genetic algorithm uses this feedback to evaluate waveform candidates and reject those that deliver excessive charge, ensuring that energy-efficient waveforms do not compromise tissue safety. This closed-loop optimization simultaneously minimizes power consumption while preventing harmful charge accumulation
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between energy efficiency goals and tissue safety constraints. This model predicts charge delivery and energy consumption, allowing the optimization algorithm to select waveforms that satisfy both criteria without direct trial-and-error on actual tissue, thus preventing tissue damage while achieving energy efficiency
3Device complexity
If passive membrane models are used to determine optimal waveform shape, then the analysis is simplified, but the results do not accurately reflect in vivo conditions and energy efficiency
Solution Approach 1:
The patent applies mechanics substitution by replacing the passive mechanical membrane model with a more biologically realistic computational model that incorporates active ion channels and membrane dynamics. Although this increases model complexity, it provides accurate predictions of energy efficiency and charge delivery in vivo conditions, resolving the contradiction between simplicity and reliability through the use of advanced computational methods
Data Source
AI summary
Systems and methods for stimulation of neurological tissue apply a stimulation waveform that is derived by a developed genetic algorithm (GA), which may be coupled to a computational model of extracellular stimulation of a mammalian myelinated axon. The waveform is optimized for energy efficiency.