Vagus Nerve Stimulation State Transition Model

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Solution Overview

Problem

Current stimulation control methods for active implantable medical devices face limitations such as difficulty in defining optimal rules for individual patients, high computational complexity, and inflexibility in adjusting to varying physiological conditions, particularly in managing intermingled processes across different time scales.

Innovation Solution

A pacing therapy system with an implantable pulse generator, sensors, and a control module that uses a state transition model with multiresolution capabilities to adjust stimulation parameters based on real-time physiological data, allowing for flexible and precise control of stimulation at various temporal and spatial resolutions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If rules-based approaches are used for stimulation control, then the implementation is simple, but it is difficult to define optimal rules for individual patients and the rules are based on thresholds that vary with inter-patient and intra-patient variability

Engineering Contradiction:

Improvecontrol system complexityVSAvoidpatient-specific adaptability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent implements a dynamic state transition model that adapts to individual patients by learning from their physiological responses over time. The model transitions between different states based on real-time data, allowing the stimulation parameters to be dynamically adjusted according to each patient's unique response pattern, thereby resolving the contradiction between simple implementation and patient-specific adaptability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where physiological measurements from sensors are continuously fed back into the state transition model. This feedback loop enables the system to learn from patient responses and refine its control rules, transforming static threshold-based rules into adaptive, patient-specific control strategies that improve over time.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If linear or non-linear transfer function approaches are used for stimulation control, then the adaptability to physiological conditions is improved, but the computational complexity increases and the amount of data required to adjust parameters increases

Engineering Contradiction:

Improvephysiological condition adaptabilityVSAvoidcomputational complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the control problem into discrete states and transitions, replacing complex continuous transfer functions with a state transition model. Each state represents a specific physiological condition, and transitions between states are governed by simpler rules based on sensor data, reducing computational complexity while maintaining adaptability to physiological variations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes parameters by transitioning between discrete states rather than continuously adjusting parameters through complex functions. This discrete parameter approach reduces the computational burden of parameter estimation while still capturing the essential adaptability needed for different physiological conditions, as each state encapsulates a specific physiological state with its own optimal parameters.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If a fixed time constant is used for closed loop control, then the control implementation is simple, but it cannot effectively handle intermingled processes that occur on different time scales in physiology

Engineering Contradiction:

Improvecontrol implementation complexityVSAvoidmulti-time scale adaptability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic time constants that adapt to the current physiological state and processing requirements. The state transition model can adjust the timing of control actions based on the urgency and nature of the physiological condition being treated, enabling effective handling of multi-time-scale processes while maintaining manageable implementation complexity through a unified state-based framework.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS10099056B2System for stimulation therapy of the vagus nerve by implementation of a state transition model operating at multiple temporal or spatial resolutions

Publication Date: 2018.10.16 SORIN CRM

AI summary

One system includes a stimulation device such as a vagus nerve stimulation lead, and a controller for controlling the stimulation device according to a set of stimulation parameters. A memory of the stimulation device contains a state transition model, and for each state defines a set of stimulation parameters and at least one expected response during the application of stimulation with the parameters. A matrix determines the transition rules between states based on physiological levels measured versus target levels. A state transition control unit determines, in an organized timely method, possible transitions between states according to the rules on physiological levels obtained in response to the implementation of the stimulation parameters of the current state, and a transition from a current state to a new state causes a corresponding change in the parameter set used for stimulation.