Analyte Stability Parameter Analysis for Sensor Self-Calibration
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Solution Overview
Problem
Existing methods for continuous monitoring of glucose concentration in body fluids require resource-consuming calibration of sensors, especially for implanted sensors, due to changing sensitivities over time, which complicates the prediction of future glucose levels and timely insulin administration.
Innovation Solution
A method that analyzes series of measuring data to determine stability parameters characterizing the dynamics of analyte concentration changes over time intervals, allowing for disease-related particularities to be recognized without the need for absolute concentration values or frequent sensor calibration, using a system with a sensor and analytical facility to process data and provide therapeutic recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor calibration is performed frequently to maintain measurement accuracy, then measurement precision is improved, but loss of time and resource consumption increase
Solution Approach 1:
The system performs self-calibration by using the sensor's own measurement data and stability parameters to automatically adjust and maintain accuracy without external intervention. The analytical facility processes the sensor data autonomously to detect disease-related particularities and compensate for drift, enabling the sensor to service itself and eliminate manual calibration requirements.
2Measurement precision
If sensor calibration is performed frequently to maintain measurement accuracy, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
The analytical facility autonomously processes sensor data to perform self-calibration, eliminating the need for resource-consuming manual calibration procedures. The system uses its own measurement data and stability parameter analysis to maintain accuracy automatically, significantly reducing the energy and resources required for maintaining sensor performance.
3Measurement precision
If absolute concentration values are used for diabetes management, then prediction accuracy is improved, but device complexity and calibration requirements increase
Solution Approach 1:
The invention extracts and utilizes only the stability parameter information from sensor measurements, removing the need for absolute concentration values and their associated calibration requirements. By focusing on the dynamic stability characteristics rather than absolute values, the system achieves effective diabetes management with significantly reduced device complexity and eliminated calibration needs.
Data Source
AI summary
Metabolism diseases are evaluated by using a sensor to measure concentration of a medically significant analyte in a human or animal body fluid. The measurement data are correlated with the concentration of a medically significant analyte in body fluid over a time period of at least eight hours. An analytical facility analyzes time intervals within the time period to determine a stability parameter that characterizes the analyte concentration dynamics of change. The analytical facility further analyzes the stability parameters to determine disease-related particularities of metabolism.