Glucose Sensor Data Processing with Dynamic Rate Filtering
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Solution Overview
Problem
Existing glucose monitoring systems face challenges in processing a wide range of analyte sensor sensitivities and providing reliable data processing and control for medical telemetry systems, particularly in continuous glucose monitoring.
Innovation Solution
A method and system that includes receiving glucose-related data from an in vivo analyte sensor, determining filtered values based on different time periods, calculating a rate of change, generating a weighted average, and determining a filtered glucose value using a predetermined parameter, which enables robust data processing and control for medical telemetry systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional filtering methods are used for glucose data processing, then the system structure remains simple, but the system cannot effectively handle varying sensor sensitivities and provides less accurate glucose values
Solution Approach 1:
The patent implements dynamic filtering by adjusting filter parameters based on the calculated rate of change of glucose values. When the rate of change exceeds a threshold, the system switches between different filter strengths or types, allowing the filtering mechanism to adapt to varying glucose dynamics rather than using a static filter configuration.
Solution Approach 2:
The system changes processing parameters dynamically by calculating the rate of change from received glucose data and using this information to adjust filtering parameters. This allows the same hardware to handle varying sensor sensitivities by modifying software-based processing parameters rather than requiring hardware changes.
2Measurement precision
If multiple filtering parameters are calculated and processed, then glucose value accuracy improves, but processing time and computational load increase
Solution Approach 1:
The system calculates multiple filtering parameters (first filtered value with first time period, second filtered value with second time period) but only combines them selectively based on the rate of change condition. This partial action approach computes more parameters than traditionally needed but only processes them when necessary, balancing accuracy with processing efficiency.
Solution Approach 2:
The system pre-calculates multiple filtered values using different time periods before determining the final glucose value. By having these filtered values ready in advance based on rate of change analysis, the system avoids complex real-time calculations when determining the final weighted average, reducing actual processing time.
3Adaptability or versatility
If the system accommodates a larger range of sensor sensitivities, then manufacturing flexibility improves, but data processing complexity increases
Solution Approach 1:
The patent creates a universal data processing algorithm that can handle varying sensor sensitivities through dynamic rate of change analysis and adaptive filtering. The same processing circuitry and software routine accommodate different sensor types and sensitivities by adjusting filter parameters based on calculated glucose dynamics rather than requiring sensor-specific processing paths.
Solution Approach 2:
The system performs self-adjustment by automatically calculating the rate of change from received data and using this information to select appropriate filtering parameters. This self-service mechanism allows the system to adapt to different sensor sensitivities without external calibration or manual configuration, reducing the burden on manufacturing while maintaining versatility.
Data Source
AI summary
Methods and apparatus for providing data processing and control for use in a medical communication system are provided.