Personalized Glucose Sensor Calibration via Dual-Depth Time Constants
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Solution Overview
Problem
Existing glucose monitoring systems face challenges in accurately calibrating glucose sensors due to patient-to-patient variability in the time delays between blood and interstitial glucose levels, leading to errors in glucose prediction.
Innovation Solution
A method involving the use of two glucose sensors implanted at different depths to estimate individualized in-vivo time-constants, allowing for personalized calibration by minimizing the difference between signal histories from both sensors, thereby improving blood glucose estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single glucose sensor is used for monitoring, then the device complexity is low, but the measurement precision deteriorates due to inability to account for subject-dependent diffusion time-constants
Solution Approach 1:
The patent divides the monitoring system into multiple glucose sensors placed at different tissue depths (e.g., dermis and subcutaneous tissue). Each sensor provides independent measurements that are processed separately to estimate individual diffusion time-constants, enabling more accurate blood glucose prediction by accounting for subject-specific physiological variations.
Solution Approach 2:
The patent adds the dimension of tissue depth by placing sensors at different depths in the tissue. This spatial differentiation allows the system to capture the diffusion dynamics of glucose from multiple depths, providing additional information for calculating subject-dependent time-constants and improving overall measurement accuracy.
2Measurement precision
If population-averaged time-constants are used for calibration, then the calibration process is simple, but the measurement precision deteriorates due to patient-to-patient variability
Solution Approach 1:
The patent transitions from using fixed population-averaged time-constants to dynamically estimating subject-specific time-constants based on actual sensor measurements. The system calculates individual diffusion time-constants by analyzing the relationship between measurements from multiple sensors at different depths, adapting the calibration parameters to each patient's unique physiological characteristics.
Solution Approach 2:
The system uses feedback from multiple sensor measurements to continuously refine and update the estimated diffusion time-constants. By comparing measurements from sensors at different depths and using these measurements to adjust the time-constant estimates, the system adapts to individual patient variations and improves prediction accuracy over time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise calibration of glucose sensors, reducing mean absolute relative difference (MARD) and enhancing the accuracy of blood glucose level estimation by accounting for subject-dependent diffusion time-constants, thus providing a true personalized calibration for each patient.
Implementation Method 1
The interstitial glucose concentration may be related to the blood glucose concentration by various mechanisms, including diffusion.
Data Source
AI summary
A system and method for personalized calibration for glucose sensing. In some embodiments, the method includes obtaining a plurality of first glucose measurements from a subject, at a plurality of first sampling times during a time interval, using a first glucose sensor; obtaining a plurality of second glucose measurements from the subject, at a plurality of second sampling times during the time interval, using a second glucose sensor; and estimating a first in-vivo calibration parameter for the first glucose sensor and the second glucose sensor, based on the first glucose measurements and the second glucose measurements, wherein a first in-vivo time-constant, relating blood glucose to an output of the first glucose sensor, is different from a second in-vivo time-constant, relating blood glucose to an output of the second glucose sensor.