Glucose Sensor Error Isolation for Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional glucose monitoring systems face inaccuracies due to measurement errors, dynamic diffusional lags, and random errors, which are not accounted for in correlational methods, leading to substantial dispersion and reduced accuracy in estimating blood glucose concentrations from tissue glucose sensors.
Innovation Solution
The system employs an estimator that isolates and subtracts measurement, process, and random errors from tissue glucose sensor signals to produce accurate blood glucose estimates by using a data processing device with a processor and memory, including an estimator module that processes time-series values from glucose sensors and reference blood glucose concentration values to determine residual errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If correlational methods are used to estimate blood glucose from tissue sensor signals, then the system is simple to implement, but measurement precision deteriorates due to unaccounted errors and dispersion
Solution Approach 1:
The patent segments the total error into distinct components: measurement error (from tissue oxygen and perfusion variations), process error (diffusional lag), and random error. By isolating and modeling each error source separately, the system can compensate for them individually, thereby improving measurement precision without excessive complexity increase.
Solution Approach 2:
The patent implements feedback mechanisms where the estimated blood glucose values are continuously refined by comparing with actual measurements and adjusting the error models accordingly. This feedback loop enables the system to learn and adapt, improving accuracy over time while maintaining a manageable system structure.
2Measurement precision
If error compensation methods are implemented to improve blood glucose estimation accuracy, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent performs preliminary error characterization and model development during system calibration and initialization phases. By pre-computing error models and compensation parameters before actual glucose estimation, the system reduces the computational burden during real-time operation, balancing accuracy improvement with processing complexity.
3Measurement precision
If dynamic error modeling is used to account for diffusional lags and perfusion variations, then measurement precision improves, but the complexity of error isolation and processing increases
Solution Approach 1:
The patent employs parameter changes by modeling error characteristics as dynamic variables that change with physiological conditions. By representing measurement error, process error, and random error as adjustable parameters rather than fixed values, the system can adapt to varying tissue perfusion and glucose diffusion rates, improving dynamic measurement accuracy while using standardized modeling techniques.
Data Source
AI summary
Disclosed are systems, devices and methods for estimating blood glucose parameters, including blood glucose concentration glucose sensor signals. In some aspects, a method for estimating blood glucose concentration from signals of glucose sensors includes obtaining a set of time-series values that includes tissue glucose sensor values from a glucose sensor and reference blood glucose concentration values associated with a subject; generating a set of matched blood glucose reference values by determining a temporal matching of the tissue glucose sensor values and the reference blood glucose concentration values; isolating error associated with the matched blood glucose reference values to determine a residual error time series, wherein the isolated error includes a composite error comprising a measurement error, a process error, and random error; and producing estimated blood glucose values for true blood glucose of the subject by adding the reference blood glucose concentration values to the residual error time series.