Analyte Sensor Calibration Lag Error Compensation
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Solution Overview
Problem
Analyte monitoring systems face errors due to lag between monitored data and blood glucose measurements, leading to inaccurate calibration of analyte sensors.
Innovation Solution
A method and system for dynamically updating calibration parameters based on detected analyte values, using a calibration counter to incrementally adjust sensor sensitivity, incorporating filters for noise reduction and real-time data processing to minimize lag errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If periodic blood glucose measurements are used to calibrate the sensor, then calibration can be performed, but lag error is introduced between monitored data and measured blood glucose values
Solution Approach 1:
The system performs lag compensation by calculating a compensation value based on the rate of change of sensor data before applying calibration. This preliminary action corrects the lag error proactively rather than reactively, allowing the system to account for the time delay between sensor measurement and blood glucose measurement in advance of the calibration process.
Solution Approach 2:
The system uses feedback from the rate of change of sensor data to dynamically adjust the compensation value. By continuously monitoring how quickly the sensor data is changing and using this information to modify the lag compensation, the system creates a closed-loop control mechanism that adapts to varying physiological conditions and minimizes lag error throughout the monitoring period.
2Reliability
If calibration parameters are updated dynamically in real-time, then lag errors are minimized, but system complexity increases
Solution Approach 1:
The system transitions from static calibration parameters to dynamic calibration parameters that are continuously updated based on incoming sensor data. The calibration parameter is recalculated at each time point using the current sensor data and the compensated values, allowing the system to adapt to changing physiological conditions while maintaining a relatively simple update mechanism.
Solution Approach 2:
The system changes the calibration parameter over time based on the relationship between compensated sensor data and blood glucose measurements. By allowing the calibration parameter to vary dynamically rather than remaining fixed, the system can account for drift and changes in sensor performance while using a straightforward parameter update approach that doesn't significantly increase complexity.
Data Source
AI summary
Methods and apparatuses for determining an analyte value are disclosed.