Glucose Sensor Calibration Using Delay Correlation
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Solution Overview
Problem
Current glucose monitoring and insulin delivery systems face challenges in accurately calibrating glucose monitoring sensors and delivering insulin in a closed-loop system, particularly due to delays and noise in sensor measurements, which affect the precision of blood glucose level estimation and insulin infusion.
Innovation Solution
The system employs methods such as correlating blood glucose reference samples with sensor measurements using matched filters, Wiener filters, and Bayesian techniques to determine a function for estimating blood-glucose concentration, accounting for delays and noise, and uses subcutaneous current sensors to infuse insulin based on these estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used without accounting for delays, then the calibration process is simpler, but the measurement precision of blood glucose level estimation deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-determining the delay parameter through correlation analysis between sensor measurements and reference glucose levels. This delay characterization is performed in advance and stored for use in the calibration function, allowing the system to compensate for delays without adding real-time complexity during actual glucose monitoring operations.
Solution Approach 2:
The patent introduces an intermediary calibration function that incorporates the delay parameter as a separate computational step. This function acts as a mediator between raw sensor data and final glucose estimates, allowing delay compensation to be integrated into the calibration process without directly complicating the core measurement mechanism. The delay parameter serves as an intermediary variable that captures temporal offset effects.
2Reliability
If sensor measurements are used directly without delay compensation, then the system operation is simpler, but the reliability of insulin delivery control deteriorates
Solution Approach 1:
The patent implements feedback by using the determined delay parameter to adjust the calibration function, which in turn improves the accuracy of glucose level estimates that drive insulin delivery decisions. The delay-compensated calibration results feed back into the control algorithm, creating a closed-loop system where delay information continuously improves control reliability without requiring complex real-time adjustments.
Solution Approach 2:
The delay parameter determination is performed as a preliminary action during the calibration phase rather than during real-time operation. This pre-characterization of delay allows the system to incorporate temporal compensation into the calibration function itself, maintaining simple real-time operations while improving reliability through预先 determined delay characteristics.
3Productivity
If manual calibration procedures are used, then the device complexity is lower, but the productivity of system setup and maintenance deteriorates
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine the delay parameter through correlation analysis of sensor measurements and reference glucose levels. This automated delay characterization eliminates the need for manual calibration adjustments, allowing the system to perform its own calibration optimization without external intervention, thereby improving setup and maintenance efficiency.
Solution Approach 2:
The patent utilizes parameter changes by dynamically determining the delay parameter based on actual sensor performance characteristics rather than using fixed manual calibration values. This data-driven approach to parameter determination allows the calibration function to adapt to specific sensor and patient conditions, improving system efficiency through automated parameter optimization.
Data Source
AI summary
Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, a blood-glucose concentration in a patient may be determined based, at least in part, on one or more probability models, one or more functions for estimating blood-glucose concentrations, and/or blood glucose reference sample-sensor measurement pairs.