Glucose Sensor Reliability Metric and Drift Detection
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Solution Overview
Problem
Closed-loop glucose control systems rely on blood glucose sensors for accurate insulin and glucagon infusion, but these sensors can become unreliable due to factors like noise, sensor drift, and reduced sensitivity, posing risks to patient health and safety.
Innovation Solution
A method and system to determine a reliability metric for blood glucose sensors, using observed trends such as sensor drift and noise, to assess their accuracy and reliability, which can trigger transitions from closed-loop to open-loop operation or request additional calibration samples to extend sensor life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blood glucose sensors are used for continuous monitoring in closed-loop systems, then glucose level control accuracy is improved, but sensor reliability deteriorates over time due to drift and noise
Solution Approach 1:
The system performs preliminary calibration of the sensor using reference glucose measurements before full closed-loop operation begins. This preliminary action establishes accurate baseline parameters that compensate for sensor drift and noise, ensuring measurement precision is maintained throughout the monitoring period.
Solution Approach 2:
The system continuously monitors sensor output and compares it against expected physiological ranges and reference measurements. When deviations indicating drift or noise are detected, the system provides feedback to adjust calibration parameters or trigger recalibration, thereby maintaining sensor reliability over time.
2Duration of action of moving object
If sensor operation is extended to reduce replacement frequency, then operational convenience is improved, but measurement accuracy deteriorates due to accumulated drift and noise
Solution Approach 1:
The system implements periodic recalibration using reference glucose measurements at predetermined intervals during sensor operation. This periodic action resets accumulated drift and noise effects, maintaining measurement accuracy throughout the extended operational life of the sensor without requiring frequent replacements.
Solution Approach 2:
The system dynamically adjusts calibration parameters based on reference measurements taken during operation. By changing these parameters periodically, the system compensates for sensor degradation and maintains measurement precision throughout the sensor's operational life, enabling extended use without accuracy loss.
3Measurement precision
If additional calibration samples are requested to maintain accuracy, then measurement precision is improved, but system complexity and user burden increase
Solution Approach 1:
The system automatically manages the calibration process by selecting optimal times for reference measurements, processing the calibration data, and adjusting parameters without user intervention. This self-service approach maintains measurement precision while minimizing user burden and system complexity.
Solution Approach 2:
The system uses feedback from reference measurements to determine when recalibration is needed and automatically executes the calibration process. This feedback-driven approach ensures precision is maintained while simplifying user interaction, as the system intelligently manages calibration timing and procedures without requiring complex user input.
Data Source
AI summary
Disclosed are a system and method for determining a metric and/or indicator of a reliability of a blood glucose sensor in providing glucose measurements. In one aspect, the metric and/or indicator may be computed based, at least in part, on an observed trend associated with signals generated by the blood glucose sensor.