Analyte Sensor Time Lag Compensation
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Solution Overview
Problem
Analyte monitoring systems face errors due to time lag between sensor measurements and blood glucose values, requiring a method to calibrate sensors and compensate for these lag errors to provide accurate data.
Innovation Solution
A method and system for dynamically updating calibration parameters based on detected analyte values, using a calibration counter to initiate reference data acquisition and adjust sensor sensitivity, incorporating filters to reduce noise and estimate lag time constants for real-time correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analyte sensor calibration is used without time lag compensation, then the calibration process is simple, but measurement precision deteriorates due to lag errors between sensor readings and actual blood glucose values
Solution Approach 1:
The system performs preliminary lag compensation calculations using historical sensor data and reference measurements before final calibration is applied. By pre-calculating lag time constants and compensation factors from stored data, the system reduces measurement errors without requiring complex real-time computations during calibration, thus improving accuracy while maintaining manageable complexity
Solution Approach 2:
The calibration process incorporates feedback mechanisms where lag compensation parameters are continuously refined based on the difference between sensor readings and reference blood glucose measurements. The system uses this feedback to adjust calibration parameters dynamically, improving measurement precision through iterative optimization without substantially increasing operational complexity
2Measurement precision
If dynamic updating of calibration parameters is implemented to compensate for time lag, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system pre-calculates lag time constants and stores historical sensor data and reference measurements for later use. By performing preliminary data collection and basic processing during normal operation, the system reduces the computational burden during dynamic calibration updates, enabling improved accuracy through parameter updating while keeping processing complexity manageable
Solution Approach 2:
The calibration parameters are updated dynamically based on changing conditions and new reference measurements, allowing the system to adapt to varying physiological states and sensor drift. This dynamic approach improves measurement precision over time while using efficient algorithms that balance computational requirements with performance benefits
3Measurement precision
If lag time constant estimation is performed using historical data and filters, then measurement precision improves through better compensation, but device complexity increases due to additional data processing
Solution Approach 1:
The system collects and stores historical sensor data and reference measurements in advance, organizing them for efficient retrieval and analysis. By pre-processing data and maintaining structured historical records, the system enables accurate lag time constant estimation without requiring complex real-time data management, thus improving compensation accuracy while keeping data processing complexity manageable
Solution Approach 2:
The system creates simplified representations or models of the lag behavior based on historical data, such as estimated time constants and compensation factors. These copied or modeled parameters allow the system to implement accurate lag compensation without directly processing all raw historical data in real-time, reducing computational complexity while maintaining precision
Data Source
AI summary
Methods and devices and systems for determining an analyte value are disclosed.