Continuous Analyte Sensor End-of-Life Detection From Drift and Noise
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Solution Overview
Problem
Conventional continuous analyte sensors, such as those used for glucose monitoring in diabetes management, degrade over time without a reliable method to determine their end of life, leading to inaccurate readings and potential health risks due to delayed detection of hyperglycemic or hypoglycemic conditions.
Innovation Solution
A method and system for determining the end of life of continuous analyte sensors by evaluating risk factors like downward drift in sensitivity, non-symmetrical noise, and noise duration, using logistic regression functions to map these factors to an end of life status, and providing an output for sensor termination when the weighted average exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If continuous analyte sensors are used for extended periods to reduce finger pricks, then convenience and comfort are improved, but sensor performance degrades leading to inaccurate readings
Solution Approach 1:
The system performs preliminary detection of end-of-life symptoms before the sensor completely fails. By continuously monitoring risk factors such as downward drift in sensitivity, non-symmetrical noise, and noise duration, the system predicts sensor degradation and alerts users to replace the sensor before accuracy is compromised, thus maintaining reliable readings throughout the extended wear period
2Duration of action of stationary object
If sensor lifetime is extended beyond approved duration, then fewer sensor replacements are needed, but undetected sensor failure causes delayed detection of dangerous blood sugar levels
Solution Approach 1:
The system implements continuous feedback monitoring of sensor performance by evaluating multiple risk factors including downward drift in sensitivity, non-symmetrical noise characteristics, and noise duration. This feedback mechanism enables real-time assessment of sensor health, allowing the system to detect end-of-life conditions and alert users to replace the sensor before it fails, thus maintaining reliable detection capability throughout extended use
3Measurement precision
If multiple risk factors are evaluated to determine end of life, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the end-of-life detection process into distinct risk factor evaluations: downward drift in sensitivity, non-symmetrical noise, and noise duration. Each risk factor is independently assessed and then integrated to form an overall end-of-life determination. This segmentation allows for systematic and manageable complexity while maintaining high detection accuracy through comprehensive multi-factor evaluation
Data Source
AI summary
Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes receiving a sensor signal from an analyte sensor. A plurality of risk factors associated with end of life symptoms of analyte sensors is evaluated. The risk factors include a downward drift in sensor sensitivity over time, an amount of non-symmetrical, nonstationary noise and a duration of noise. An end of life status of the analyte sensor is determined based at least in part on the evaluating. An output related to the end of life status of the analyte sensor is provided.