Glucose Trend Determination Using Sensor Tolerance Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing analyte monitoring systems face challenges in tolerating a wide range of sensor sensitivities and efficiently processing data for continuous glucose monitoring, requiring improved methods for data communication and control to ensure accurate glucose trend determination.
Innovation Solution
A method and system that receive signals from an in vivo analyte sensor, process stored signals to determine a tolerance parameter, and compare it to a predetermined range to determine glucose trend information, incorporating features like digital anti-aliasing filtering and outlier verification to enhance data quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a wide range of sensor sensitivities is tolerated to provide manufacturing flexibility, then manufacturing precision requirements are relaxed, but data processing complexity increases to ensure accurate glucose trend determination
Solution Approach 1:
The system performs preliminary verification of sensor signals against expected sensitivity ranges during the monitoring process. By pre-establishing tolerance parameters and verification criteria, the system can accommodate manufacturing variations without requiring complex real-time adjustments, thus resolving the contradiction between manufacturing flexibility and processing complexity
Solution Approach 2:
The system dynamically adjusts processing based on the verified sensor sensitivity. By making the processing algorithm adaptive to the specific sensor characteristics within the tolerated range, the system maintains accuracy while accommodating manufacturing variations, balancing ease of manufacture with controlled processing complexity
2Measurement precision
If more stored signals are retrieved and processed to improve glucose trend accuracy, then measurement precision improves, but loss of time increases due to additional processing
Solution Approach 1:
The system retrieves and processes a predetermined number of stored signals that is sufficient to achieve acceptable glucose trend accuracy without excessive processing. By optimizing the number of signals processed to meet minimum accuracy requirements, the system balances measurement precision with time efficiency
Solution Approach 2:
The system replaces complex mechanical processing with efficient digital signal processing algorithms. By using digital filtering and verification methods instead of more intensive computational approaches, the system achieves accurate glucose trend determination with reduced processing time
3Reliability
If digital anti-aliasing filtering and outlier verification are implemented to enhance data quality, then reliability improves, but device complexity increases
Solution Approach 1:
The system introduces digital filtering and verification as intermediary processing steps between signal acquisition and glucose trend determination. These intermediaries improve data quality by removing noise and verifying signal validity, while maintaining relatively simple overall system architecture through the use of standard digital signal processing techniques
Data Source
AI summary
Methods and apparatus for providing data processing and control for use in a medical communication system are provided.