Transcutaneous Glucose Sensor Self-Calibration Under Signal Drift
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Solution Overview
Problem
Existing glucose sensors, both implantable and transdermal, face challenges in accurately monitoring blood glucose levels continuously over extended periods due to sensitivity and baseline drift, leading to inadequate real-time data for diabetic management.
Innovation Solution
A method for calibrating and compensating for drift in analyte concentration sensors using only sensor signals, employing repeatable events, slow-moving averages, and seed values to correlate and adjust sensor readings, thereby maintaining accurate glucose monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous sensor monitoring is used, then real-time glucose data is provided, but sensor drift causes accuracy degradation over time
Solution Approach 1:
The system performs preliminary calibration at sensor implantation to establish an initial accurate relationship between sensor signal and glucose concentration. This preliminary action sets up the foundation for accurate measurements before drift occurs during continuous monitoring.
Solution Approach 2:
The system uses feedback from repeatable events (such as known glucose levels during steady state) to continuously adjust and recalibrate the sensor readings. This feedback mechanism allows the system to compensate for drift by comparing expected values at repeatable events with actual sensor readings and adjusting accordingly.
2Measurement precision
If sensor recalibration is performed frequently, then measurement accuracy is maintained, but system complexity and operational burden increase
Solution Approach 1:
The system performs self-calibration using its own sensor data without requiring external reference measurements or user intervention. The sensor automatically identifies repeatable events in its own signal and performs recalibration autonomously, eliminating the need for complex external calibration procedures.
Solution Approach 2:
The system changes the calibration parameter dynamically based on detected repeatable events. Instead of using fixed calibration values, the system adjusts sensitivity and baseline parameters in response to changes in the sensor signal pattern, allowing accurate compensation for drift without complex manual recalibration procedures.
3Measurement precision
If manual calibration using reference measurements is used, then initial accuracy is achieved, but continuous accuracy maintenance requires user intervention
Solution Approach 1:
The system automatically identifies and utilizes repeatable events in its own signal to perform self-calibration. Instead of requiring users to provide reference measurements, the sensor autonomously detects patterns (such as steady state conditions) and uses its own readings to maintain accuracy, completely eliminating manual calibration operations.
Solution Approach 2:
The system continuously monitors its own performance by detecting repeatable events and using feedback from these events to automatically adjust calibration. This closed-loop feedback eliminates the need for external reference measurements and user intervention, as the system self-corrects drift using its intrinsic signal characteristics.
Data Source
AI summary
Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.