Glucose Sensor Calibration Using Multiple Methods
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Solution Overview
Problem
Conventional glucose sensors using a single linear calibration method experience increased errors as glucose levels deviate from the calibration point, leading to inaccuracies in hypoglycemic and hyperglycemic ranges, necessitating improved accuracy across all glucose regions.
Innovation Solution
A system and method utilizing multiple calibration methods with a consensus glucose concentration estimate, where in vivo updates calculate multiple calibration parameters, applying a smooth crossover function to select the most accurate method based on glucose levels, and weighting estimates to optimize accuracy across different glucose regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single linear calibration method is used, then the calibration process is simple, but sensor accuracy degrades as glucose levels move further from the calibration point
Solution Approach 1:
The patent divides the calibration process into multiple segments by using several different calibration methods (linear, curvilinear, power-law) instead of relying on a single calibration approach. Each calibration method is optimized for different glucose concentration ranges, allowing the system to segment the measurement domain and apply the most appropriate calibration model for each region, thereby maintaining accuracy across the full glucose range while managing complexity through modular calibration options.
Solution Approach 2:
The patent changes the mathematical parameters of the calibration equation by offering multiple calibration models with different functional forms (linear: y=mx+b, curvilinear: y=ax²+bx+c, power-law: y=ax^b). By selecting and switching between different parameter sets based on glucose concentration levels, the system adapts the calibration characteristics to match the specific measurement range, resolving the contradiction between simple calibration and accurate measurement across varying glucose levels.
2Measurement precision
If multiple calibration methods are used, then sensor accuracy is improved across all glucose regions, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic calibration selection mechanism that automatically switches between different calibration methods based on the current glucose concentration estimate. The system dynamically evaluates which calibration model (linear, curvilinear, or power-law) provides the best fit for the current measurement range and applies that model accordingly. This dynamic adaptation allows the system to maintain high accuracy across all glucose regions while managing complexity through intelligent, context-dependent model selection rather than requiring all models to operate simultaneously.
Solution Approach 2:
The patent introduces an intermediary consensus mechanism that reconciles results from multiple calibration methods. Instead of directly combining complex calibration models, the system uses a consensus algorithm that evaluates the outputs of different calibration approaches and synthesizes a final accurate estimate. This intermediary layer simplifies the integration of multiple calibration methods by providing a unified decision-making framework that selects or combines results based on performance criteria, thereby managing system complexity while achieving improved accuracy.
3Ease of manufacture
If a linear calibration equation is used, then the calibration is easy to implement, but the magnitude of sensor errors increases as the distance from the calibration point increases
Solution Approach 1:
The patent creates a universal calibration system that incorporates multiple calibration models (linear, curvilinear, power-law) within a single calibration framework. The system can universally apply any of these calibration methods depending on the measurement requirements and glucose concentration range. This multi-functional calibration approach allows the system to maintain the simplicity of linear calibration when appropriate while automatically transitioning to more complex models when higher accuracy is needed across extended glucose ranges, thus achieving both ease of implementation and reduced error magnitude.
Data Source
AI summary
The present invention relates to a system and method for improving glucose sensor accuracy by utilizing multiple calibration methods and selecting the most accurate method depending on a consensus glucose concentration estimate. Embodiments of the present invention comprise the steps of performing at least one in vivo update of surrounding glucose to acquire glucose values; calculating multiple updated calibration estimates using the updated glucose values; calculating an initial consensus glucose estimate from sensor output using each updated calibration estimate; applying a smooth crossover function to the multiple calibration estimates based on the value of the initial consensus glucose estimate; and adding weights to the multiple calibration estimates to acquire a consensus glucose estimate.