Neurostimulation Pattern Composition with Neuronal Network Models
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Solution Overview
Problem
Current neurostimulation systems are limited by their inability to deliver customized and complex patterns of neurostimulation pulses that emulate natural neural signals, leading to unintended sensations and side effects due to the lack of post-manufacturing programmability and pre-defined stimulation patterns.
Innovation Solution
A system and method for programming neurostimulation patterns using a user interface that allows for the customization of neurostimulation waveforms and patterns, including graphical editing and composition of pulses, bursts, trains, and sequences, utilizing neuronal models for personalized and efficient delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-defined stimulation patterns are used at manufacturing time, then device complexity is reduced, but adaptability and customization capability deteriorate
Solution Approach 1:
The system transitions from static pre-defined patterns to dynamic programmable patterns that can be adjusted in real-time. The neurostimulator accepts and executes programmed pulse patterns with variable parameters including pulse width, amplitude, frequency, and duty cycle, allowing the stimulation pattern to adapt dynamically to patient needs and treatment requirements.
Solution Approach 2:
The invention enables modification of multiple stimulation parameters including pulse width, amplitude, frequency, and duty cycle through programming. This allows the system to deliver customized pulse patterns by changing parameters post-manufacturing, thereby achieving adaptability without requiring complex hardware redesign.
2Device complexity
If simple uniform pulse patterns are delivered, then device complexity is reduced, but therapeutic efficacy deteriorates due to inability to emulate natural neural signals
Solution Approach 1:
The system employs periodic pulse delivery with programmable intervals and duty cycles to emulate natural neural signaling patterns. By delivering pulses in controlled sequences with varying frequencies and intervals, the system can mimic physiological neural activity patterns, thereby improving therapeutic efficacy while maintaining manageable device complexity.
Solution Approach 2:
The neurostimulator delivers dynamic pulse patterns that can vary in amplitude, frequency, and timing to replicate the sophistication of natural neural signals. This dynamic capability allows the device to move beyond simple uniform pulses and achieve more effective therapy that mirrors physiological processes.
3Adaptability or versatility
If sophisticated pulse patterns are programmed post-manufacturing, then adaptability and customization are improved, but device complexity increases
Solution Approach 1:
The system achieves sophistication through parameter variation rather than structural complexity. By programmatically controlling pulse width, amplitude, frequency, and duty cycle parameters, the neurostimulator can deliver complex patterns without requiring complex hardware architecture, thereby balancing adaptability with manageable device complexity.
4Object-affected harmful factors
If customized neurostimulation patterns are delivered, then side effects are reduced, but programming complexity increases
Solution Approach 1:
The system applies local quality by delivering customized stimulation patterns targeted to specific neural pathways and regions. By programming precise pulse patterns that selectively activate desired neural circuits while avoiding non-targeted tissue, the system reduces side effects such as paresthesia and unwanted muscle contractions, balancing customization benefits with programming requirements.
Data Source
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AI summary
An example of a system for programming a neurostimulator may include a storage device and a pattern generator. The storage device may store a pattern library and one or more neuronal network models. The pattern library may include fields and waveforms of neuromodulation. The one or more neuronal network models may each be configured to allow for evaluating effects of one or more fields in combination with one or more waveforms in treating one or more indications for neuromodulation. The pattern generator may be configured to construct and approximately optimize a spatio-temporal pattern of neurostimulation and/or its building blocks using at least one neuronal network model.