Dynamic Algorithm Selection for Continuous Glucose Sensor Data
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Solution Overview
Problem
Conventional continuous glucose sensors face inconsistencies and instabilities in glucose measurements due to time lags and the static nature of algorithms, which hinder timely and accurate monitoring of blood glucose levels in diabetic patients, leading to potential hyper- or hypoglycemic conditions.
Innovation Solution
A method for estimating analyte values from continuous analyte sensors involves receiving and processing data streams, selecting appropriate algorithms based on analyte concentration, rate of change, and individual historical patterns, and applying physiological boundaries to improve data accuracy and stability, including the use of polynomial regression, autoregressive algorithms, Fourier transforms, and neural networks for dynamic estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static algorithms are used for glucose estimation, then device complexity is reduced, but measurement precision and reliability deteriorate due to inability to represent physiological trends
Solution Approach 1:
The patent implements dynamic algorithm selection that adapts to physiological conditions. The system transitions between different estimation algorithms (e.g., autoregressive models, polynomial regression) based on real-time analysis of data stream characteristics and physiological state, allowing the algorithm to dynamically represent changing physiological trends while maintaining measurement precision
Solution Approach 2:
The system changes algorithmic parameters based on physiological boundaries and data quality metrics. By adjusting model order, time constants, and selection criteria according to physiological constraints, the system optimizes measurement accuracy without requiring permanently complex algorithms
2Measurement precision
If reference glucose values are used for calibration, then measurement precision improves, but reliability deteriorates due to time lags between interstitial fluid and blood samples
Solution Approach 1:
The system performs preliminary analysis of reference glucose values to detect time lag effects before using them for calibration. By identifying and compensating for the physiological delay between interstitial fluid and blood glucose measurements in advance, the system maintains both calibration accuracy and data stability
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor the consistency between reference glucose values and sensor readings. When time lag or inconsistency is detected, the system adjusts calibration timing or selects alternative reference points, ensuring reliable and stable data output
3Productivity
If continuous monitoring is implemented, then productivity improves by providing real-time glucose data, but measurement precision deteriorates due to inconsistencies and instabilities in the data stream
Solution Approach 1:
The system dynamically adjusts data processing intensity and algorithm selection based on the quality and stability of the continuous data stream. During periods of physiological stability, simpler processing maintains precision, while during transitions or anomalies, more sophisticated dynamic algorithms resolve inconsistencies, allowing continuous monitoring without sacrificing precision
Data Source
AI summary
Systems and methods for dynamically and intelligently estimating analyte data from a continuous analyte sensor, including receiving a data stream, selecting one of a plurality of algorithms, and employing the selected algorithm to estimate analyte values. Additional data processing includes evaluating the selected estimative algorithms, analyzing a variation of the estimated analyte values based on statistical, clinical, or physiological parameters, comparing the estimated analyte values with corresponding measure analyte values, and providing output to a user. Estimation can be used to compensate for time lag, match sensor data with corresponding reference data, warn of upcoming clinical risk, replace erroneous sensor data signals, and provide more timely analyte information encourage proactive behavior and preempt clinical risk.