Vagus Nerve Stimulation State Transition Model
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Solution Overview
Problem
Current closed-loop stimulation control methods for active implantable medical devices face challenges such as difficulty in defining optimal rules for individual patients, high computational complexity, and limited digital processing power, as well as a fixed time constant that does not account for varying physiological processes on different time scales.
Innovation Solution
A stimulation treatment system using a state transition model with a transition matrix and connection matrix to apply optimal stimulation parameters, incorporating self-adaptation mechanisms that adjust based on actual physiological levels and responses, allowing for dynamic regulation of stimulation to maintain target physiological levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based approaches are used for closed-loop stimulation control, then the control logic is simple to implement, but it is difficult to define optimal rules for individual patients and the rules cannot adapt to inter- and intra-patient variability
Solution Approach 1:
The patent implements a dynamic control system that transitions from static rule-based approaches to a living model that continuously adapts. The state transition model is updated in real-time based on actual physiological responses, allowing the control strategy to evolve and personalize treatment for each patient while maintaining implementability through a structured framework.
Solution Approach 2:
The system employs self-adaptation mechanisms where the state transition model automatically adjusts its parameters based on observed physiological responses without requiring manual reprogramming. The model learns from actual patient data and autonomously optimizes stimulation control, reducing the need for complex manual rule definition while achieving patient-specific adaptation.
2Adaptability or versatility
If linear or nonlinear transfer functions are used for control, then continuous adjustment is possible, but the computational complexity increases and large amounts of data are required
Solution Approach 1:
The patent segments the complex control problem into discrete states and transitions between them. Instead of using continuous complex mathematical functions, the system divides the physiological control space into manageable state categories with defined transition rules, reducing computational burden while maintaining adaptability through the state transition framework.
Solution Approach 2:
The system changes the fundamental parameter representation from continuous function parameters to discrete state parameters. By using a state transition model with defined states and transitions, the system achieves continuous adjustment capability through sequential state changes rather than complex continuous function evaluation, significantly reducing computational requirements.
3Ease of manufacture
If fixed time constants are used for closed-loop control, then the control timing is simple to manage, but it cannot account for physiological processes that operate on different time scales
Solution Approach 1:
The patent implements dynamic time scaling where the control system adapts its timing characteristics based on the current physiological state and the time scale of the underlying physiological processes. The state transition model inherently handles multiple time scales by allowing transitions to occur at appropriate intervals based on the physiological context, rather than enforcing a fixed time constant.
Solution Approach 2:
The system adds the dimension of state-dependent timing to the control framework. Instead of using a single fixed time constant, the control timing becomes a function of the current state and the characteristics of the physiological process being regulated, allowing the system to naturally accommodate multiple time scales through state-based timing adjustments.
Data Source
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AI summary
This system includes a stimulation device (28), such as a vagus nerve stimulation probe, and means for controlling the stimulation device according to a set of stimulation parameters. A memory contains a state transition model (2272), with each state having a defined set of stimulation parameters and at least one expected response when stimulation with these parameters is applied. A matrix determines state transition rules based on physiological levels measured relative to target levels.A state transition control device (2271) determines in an organized time manner possible transitions between states according to the application of rules on the physiological levels obtained in response to the application of the stimulation with the parameters of the current state, a transition from a current state to a new state causing a corresponding change in the set of parameters used for the stimulation.