Glucose Sensor Variability Reduction via Machine Learning Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Continuous glucose monitoring (CGM) systems face challenges with long run-in times and sensor variability due to interference from electroactive species and the need for stabilization of sensor signals.
Innovation Solution
A system utilizing real-time data from glucose sensors, processed by machine learning models, to estimate and correct glucose sensitivity by normalizing sensor current (Isig) trends and stabilizing calibration ratios, independent of glucose fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional amperometric glucose sensors are used to measure hydrogen peroxide, then glucose sensitivity is achieved, but interfering species such as acetaminophen, ascorbate, and urate cause signal confounding and reduce sensor sensitivity
Solution Approach 1:
The patent applies local quality by creating distinct microenvironments at different electrode locations. The first electrode measures glucose in the interstitial fluid, while the second electrode measures interferents in the blood plasma layer. This spatial differentiation allows selective measurement of glucose while excluding interferent species through local sampling zones with different composition characteristics.
Solution Approach 2:
The patent introduces a membrane as an intermediary layer between the measurement site and the interferent source. This membrane selectively permits passage of certain species while blocking others, acting as a mediator that allows glucose to reach the first electrode while preventing interferents from reaching the measurement zone, thus resolving the contradiction between glucose detection and interferent exclusion.
2Measurement precision
If sensor signals are allowed to stabilize naturally, then measurement accuracy improves, but run-in time is extended
Solution Approach 1:
The patent implements preliminary action by performing a calibration measurement during the run-in period that establishes a baseline relationship between sensor signals and reference glucose values. This preliminary calibration allows the system to compensate for ongoing signal drift without requiring complete stabilization, thereby reducing the effective run-in time while maintaining measurement accuracy through continuous reference-based correction.
Solution Approach 2:
The patent employs feedback by continuously comparing sensor measurements with reference glucose values and using this information to adjust and correct sensor output in real-time. This feedback mechanism allows the system to achieve accurate measurements during the run-in period by dynamically compensating for signal instability rather than waiting for natural stabilization.
3Measurement precision
If sensor current is corrected for interferent species, then measurement accuracy improves, but system complexity increases
Solution Approach 1:
The patent merges the functions of interferent measurement and glucose measurement into a single integrated sensor assembly. Both electrodes are positioned in close proximity and share common structural elements, including the membrane and housing. This merging allows interferent correction to be implemented without requiring a completely separate measurement system, thereby reducing the increase in overall device complexity while still achieving improved measurement precision.
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 reduces sensor variability, shortens run-in times, and improves CGM accuracy, enabling non-adjunctive labeling on day 1 and potential compliance with integrated continuous glucose monitoring (iCGM) standards.
Implementation Method 1
The hydrogen peroxide reacts electrochemically as shown in Equation 2, and the current can be measured by a potentiostat
Implementation Method 2
The glucose oxidase is used to catalyze the reaction between glucose and oxygen to yield gluconic acid and hydrogen peroxide
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system for reducing sensor variability includes a sensor configured to generate real-time data relating to glucose sensitivity. The system causes performance of accessing the real-time data from the sensor relating to glucose sensitivity and inputting the real-time data into a machine learning model. The system also causes performance of estimating by the machine learning model an expected glucose sensitivity based on the real-time data and correcting the glucose sensitivity based on the expected glucose sensitivity.