Medical Telemetry Signal Processing for Variable Sensor Sensitivity
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Solution Overview
Problem
Existing analyte monitoring systems face challenges in accommodating a wide range of sensor sensitivities and require improved data processing and control methods to enhance accuracy and flexibility.
Innovation Solution
A method and apparatus for receiving analyte level signals, retrieving stored signals, determining glucose trends, and updating trend information, incorporating features like digital anti-aliasing filtering, sensor insertion/removal detection, and ambient temperature compensation to improve data processing and control in medical telemetry systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide range of sensor sensitivities is accommodated, then flexibility and adaptability improve, but measurement precision and reliability deteriorate
Solution Approach 1:
The system dynamically adjusts processing parameters including filtering coefficients, sampling rates, and compensation factors based on the specific sensor's sensitivity characteristics. This allows each sensor to be optimized individually while maintaining overall system flexibility across different sensor types and sensitivity ranges.
Solution Approach 2:
The system implements feedback mechanisms where measured analyte signals are continuously processed and compared against reference ranges, with automatic adjustment of processing parameters based on signal quality and sensor performance. This feedback loop maintains measurement precision across varying sensor sensitivities by adapting processing in real-time.
2Measurement precision
If complex data processing is implemented to improve accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The data processing system is divided into distinct functional modules: signal acquisition, filtering stage, analyte determination stage, trend analysis stage, and compensation stage. Each module performs a specific function and can be independently optimized or disabled based on computational resources available, reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary filtering and preprocessing of sensor signals before full analysis is required. Anti-aliasing filters and initial signal conditioning are applied at the acquisition stage, preparing data for subsequent processing steps and reducing the computational burden on later stages.
3Reliability
If multiple processing stages are used to improve reliability, then reliability improves, but loss of time increases
Solution Approach 1:
The system implements continuous processing pipelines where signal acquisition, filtering, and preliminary analysis occur simultaneously rather than sequentially. Multiple processing operations are overlapped in time, with later stages beginning processing on earlier data while newer data is being acquired and preprocessed, maintaining continuous useful action throughout the processing chain.
Solution Approach 2:
The system uses periodic sampling and processing intervals optimized for the specific analyte and sensor characteristics. Rather than continuous full-processing of all data, the system processes data at strategically determined intervals, reducing total processing time while maintaining reliability through consistent periodic monitoring and analysis.
Data Source
AI summary
Methods and apparatus for providing data processing and control for use in a medical communication system are provided.