Neuromodulation System Algorithmic Mode Selection
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Solution Overview
Problem
Current neuromodulation systems face challenges in efficiently and accurately adapting to complex neural signals and changing patient conditions, requiring systems capable of multiple stimulation modes and precise programming to optimize therapy while minimizing undesirable effects.
Innovation Solution
A neuromodulation system that uses patient-specific information to determine optimal stimulation modes, such as anodic or cathodic neuromodulation, and adjusts parameters like current fractionalization across electrodes to deliver targeted therapy, incorporating an algorithm that recommends settings to clinicians and configures devices for personalized treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neuromodulation systems use multiple stimulation modes and complex programming to adapt to changing patient conditions, then treatment efficacy is improved, but device complexity and programming difficulty increase
Solution Approach 1:
The system performs preliminary actions by automatically selecting stimulation modes and configuring parameters based on patient information before therapy delivery. The IPG device proactively adapts to patient conditions without requiring complex manual programming, resolving the contradiction by preparing the optimal configuration in advance based on stored patient data and algorithmic analysis.
Solution Approach 2:
The neuromodulation system performs self-service through automatic mode selection and parameter adjustment capabilities. The device uses embedded algorithms to autonomously determine optimal stimulation parameters based on patient information, reducing the burden on clinicians and patients while maintaining high treatment efficacy without complex programming requirements.
2Reliability
If neuromodulation systems provide personalized treatment through patient-specific information, then therapeutic outcomes are enhanced, but information processing requirements and system complexity increase
Solution Approach 1:
Patient information is collected and processed in advance before therapy delivery. The system performs preliminary analysis of patient data to determine optimal stimulation parameters and modes, storing this information for later use. This preliminary action enables personalized treatment without requiring complex real-time processing during therapy delivery.
Solution Approach 2:
The system introduces an intermediary algorithmic layer that processes patient information and translates it into stimulation parameters. This intermediary processing layer simplifies the overall system architecture by handling information processing tasks separately from the core stimulation delivery function, reducing the complexity burden on the main device.
Data Source
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AI summary
A system may comprise a controller configured to implement an algorithm on a received input to produce an output, and a system input operably connected to the controller and configured for use to enter at least one input into the algorithm. The at least one input may include: one or more sensor inputs or one or more inputs from smart appliances or one or more user inputs regarding at least one of time of day or mental or physical state; or at least one of a user-inputted disease, a user-inputted disease state, or a user-inputted symptom-related information into the algorithm. The controller may be configured to provide instructions through the system output to implement a system action. The algorithm implemented by the controller may be configured to identify one, or a combination of more than one, of the neuromodulation modes as a candidate neuromodulation mode based on the input(s).