Analyte Sensor Data Processing for Glucose Estimation
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Solution Overview
Problem
Current analyte monitoring systems, particularly for glucose monitoring, face challenges in accurately estimating glucose levels and managing medication delivery, especially in dynamic physiological conditions, due to limitations in data processing and communication between sensors and infusion devices.
Innovation Solution
A computer-implemented method and apparatus that receive parameters associated with medication delivery profiles and physiological conditions, updating these parameters based on real-time data from analyte sensors and infusion devices, using filtering techniques such as rate variance filtering and Kalman filters to improve glucose level estimation and medication management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data processing and communication between sensors and infusion devices is implemented, then the accuracy of glucose level estimation and medication delivery is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system divides data processing into multiple stages: raw data acquisition from sensors, preliminary filtering and processing at the sensor level, selective transmission of processed data via RF communication, and final analysis at the receiver/monitor unit. This segmentation reduces the processing burden on individual components while maintaining overall accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a bridge between the analyte sensor and infusion device. This intermediary performs data validation, filtering, and preliminary analysis before transmitting information, reducing the complexity burden on both the sensor and infusion device while improving glucose level estimation accuracy.
2Productivity
If more parameters associated with medication delivery profile and physiological condition are monitored and processed, then the responsiveness and precision of medication management is improved, but the quantity of data to be processed and communicated increases
Solution Approach 1:
The system extracts and prioritizes only the most critical parameters for real-time processing and transmission, such as glucose levels, rate of change, and urgent alarm conditions. Less critical parameters are processed asynchronously or transmitted at lower frequencies, reducing the overall data volume while maintaining medication management responsiveness.
Solution Approach 2:
The patent implements periodic data transmission and processing at different intervals based on parameter urgency and physiological stability. During stable conditions, data is transmitted at standard intervals, while during rapid changes or alarm conditions, the system increases transmission frequency for critical parameters only, optimizing responsiveness without continuously maximizing data volume.
Data Source
AI summary
Methods and systems including receiving exogenous measurement data, receiving current analyte sensor measurement data from an analyte sensor configured to detect signal levels representative of analyte levels, and iteratively determining a plurality of present predicted state estimates using the exogenous measurement data and a past state estimate, wherein each subsequent present predicted state estimate is provided as input as the past state estimate. Thereafter, determining a present corrected state estimate using the iteratively determined plurality of present predicted state estimates and the current analyte sensor measurement data, the present corrected state estimate including an estimated analyte level and generating and outputting a medication delivery profile based on the estimated analyte level, the medication delivery profile comprising at least one of medication delivery rate or a medication delivery amount.