Glucose Sensor Calibration for Accurate Monitoring

Compensating for varying accuracy of medical sensors over their lifetime to improve performance of devices like insulin pumps. The technique involves adjusting sensor readings based on estimated accuracy levels at different points in the sensor's lifespan. This accounts for the fact that sensors can have lower accuracy early on and higher accuracy later. By taking into account the sensor age, the adjustment aims to provide more accurate readings to devices like insulin pumps to improve dosing decisions.

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Method for accurately determining glucose levels in a person's body using a continuous glucose monitoring system, with reduced computational resources and simpler implementation compared to existing methods. The method involves estimating glucose levels in the blood by first estimating glucose levels in the tissue surrounding the blood using a sensor, then using a model to convert the tissue glucose estimates to blood glucose levels. Kalman filters are used to account for measurement and process noise.

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Correcting time lag in measurements of analyte concentration, like glucose, in interstitial fluid, to improve accuracy. The method involves adjusting the lag correction based on the patient's analyte variability and range. This balances maximizing lag correction versus minimizing output noise. Higher variability patients benefit more from lag correction as it compensates for lag between interstitial and blood levels. But lower variability patients risk reduced accuracy due to noise amplification. So the lag correction is scaled based on variability.

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Method for constructing a calibration model for dynamic blood glucose monitoring that improves the accuracy of glucose level measurements using dynamic glucose sensors. The method involves fitting a nonlinear model between sensor current and glucose concentration using target data subsets representing different glucose levels. This initial calibration model is then updated with a time-varying parameter function to create a final calibration model that can adapt to changing glucose levels over time.

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An adaptive calibration system for continuous glucose monitoring (CGM) sensors that improves accuracy by optimizing the sensor's response to different blood glucose levels and eliminating attenuation effects. The calibration involves segmented processing of the sensor data, weighted least squares fitting, and multiple rounds of fine-tuning. It accounts for factors like temperature, concentration changes, and sensor batch variation. The segmented fitting allows localized optimization instead of global solutions.

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Optimizing and adjusting initialization sequences for glucose sensors based on parameters relevant to manufacturing the sensor and environmental conditions. The goal is to shorten the time from sensor insertion to accurate glucose readings. The initialization sequence involves applying specific voltages and durations tailored to the sensor and patient conditions. This allows faster hydration, electrical equilibrium, and stabilization. The sequence is calculated based on factors like platinum surface area, glucose oxidase activity, and current slope.

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Compensating for pharmaceutical agent dissolution to extend the life of glucose sensors used in continuous glucose monitoring (CGM) systems. The sensor has a working electrode and a secondary flexible member coated with a drug that dissolves over time. By monitoring electrochemical impedance spectroscopy (EIS) values at sensor startup and later times, changes in the EIS parameters are used to estimate the drug concentration. If the modeled sensor response exceeds a threshold, the glucose reading is adjusted to account for the drug's impact. This allows extending sensor life beyond when the drug has fully dissolved.

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A calibration system and method for continuous glucose monitoring that improves the accuracy of glucose readings from continuous glucose monitors (CGM) by correcting for the delay and hysteresis between interstitial fluid glucose and blood glucose. The system involves using a separate blood glucose meter along with the CGM to collect paired data, then calculating prediction models for each patient's unique glucose dynamics. This targeted modeling improves accuracy compared to generic models. The system divides patients into groups based on factors like age, BMI, etc, and calculates prediction models for each group. It also combines three calculation methods and adjusts weights based on real-time pairing to further improve accuracy.

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Continuous blood glucose monitoring systems can provide detailed time series data to help manage blood sugar levels for people with diabetes. However, the glucose sensors used in these systems can drift over time, causing inaccuracies. To address this, the disclosed method for calibrating continuous glucose monitors involves using weighted linear regression to calculate adjustment factors for the sensor's output. The method involves collecting multiple sets of blood samples and corresponding glucose readings from the monitor over a period of time. The readings are then used to calculate a weighted linear regression line to find the adjustment factors that minimize the error between the true blood glucose values and the monitor's readings. These factors are then applied to subsequent readings from the monitor to compensate for any drift and improve accuracy.

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Reducing blanking of sensor glucose signals in continuous glucose monitoring (CGM) systems by using multiple machine learning models trained for specific conditions. The CGM device inputs sensor data into multiple models, each trained for different data characteristics or abnormal conditions. The models generate predicted glucose values. The device combines the predictions to generate the displayed glucose value. This improves accuracy and reduces blanking compared to using a single model.

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Personalized calibration of glucose sensors to improve accuracy by estimating subject-specific in-vivo calibration parameters for sensors with different diffusion characteristics. The method involves obtaining glucose measurements from two sensors in a subject, estimating the in-vivo calibration parameters for each sensor based on the measurements, and using the estimated parameters to calculate blood glucose levels. The calibration parameters compensate for differences in sensor response times due to implant depths.

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Personalized calibration method for glucose sensing devices that improves accuracy by accounting for subject-specific differences in glucose diffusion between sensors. It involves using two glucose sensors at different depths in a subject to estimate personalized time constants for glucose diffusion from blood to each sensor site. This allows more accurate glucose level estimation using the sensors by accounting for the subject's unique interstitial glucose dynamics. The method involves obtaining glucose measurements from both sensors over a time interval, estimating the personalized time constants based on the measurements, and using the estimated time constants to calculate glucose levels from the sensors.

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Calibrating continuous glucose monitors more accurately by using monitor electrodes with the same chemistry stacks as the working electrodes to compensate for environmental effects on the working electrodes. The monitor electrodes are calibrated before installation and the changes in their operating parameters are used to determine environmental effects on the working electrode chemistry stacks. These effects are then applied to the working electrode calibration to correct glucose readings.

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Automatic blood sugar correction for continuous glucose monitoring (CGM) devices to improve accuracy over time as sensor performance degrades. The method involves regularly collecting the transient and steady-state output currents of the sensor. By analyzing the transient current characteristics, the sensitivity coefficient of the sensor is determined. Then, the steady-state output current is corrected using the sensitivity coefficient and used to calculate blood glucose levels. This allows automatic and efficient blood sugar correction as sensor performance degrades over time.

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Calibrating glucose sensors using a barcode to improve patient experience and convenience. The method involves binding each sensor with a unique barcode, testing the sensors to generate calibration data, and storing the association between barcode and calibration info. When using the sensor, the barcode is scanned to retrieve the calibration data, which is then applied to compensate for any sensor drift or error. This allows calibration to be done before use, eliminating the need for fingerstick calibration. The barcode provides a way to track and associate sensor performance data for calibration purposes.

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Calibrating glucose sensors using electrical parameters to improve accuracy and reduce variability. The technique involves measuring electrical parameters like voltage, current, or impedance from the sensor in vitro. These parameters are used to cluster the sensors based on similar electrical characteristics. Each cluster represents a configuration for calibrating the sensor. When the sensor is implanted, it is configured using the identified cluster's calibration parameters. This allows customizing sensor calibration based on its electrical properties, improving accuracy and consistency compared to generic calibration methods.

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Method for calibrating non-invasive biometric data like glucose levels measured by devices that don't require blood samples. The calibration involves comparing the non-invasive readings with continuous readings from a device that uses a sensor inserted under the skin. By learning the relationship between invasive and non-invasive measurements over time, the non-invasive readings can be personalized and corrected for individual users. This allows more accurate determination of biometric trends without needing invasive sensors.

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Calibrating analyte data from medical sensors using artificial intelligence to provide more accurate readings and extend calibration intervals. The method involves using an AI model to predict correction values over time based on historical data. This allows calibration to be done less frequently since the AI can compensate for drift. By receiving initial calibration data, storing it, then using it along with current sensor data to predict and apply corrections, the AI calibration can handle time-dependent errors better than fixed mapping tables.

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Robust glucose monitoring system using orthogonally redundant sensors for improved accuracy and reliability compared to single sensor systems. The system has two glucose sensors with distinct technologies, like electrochemical and optical, implanted in the body. The sensors measure glucose levels separately and the optical sensor's output is calibrated based on the electrochemical sensor's reading. This allows accounting for environmental effects and sensor anomalies. The redundant sensors provide true redundancy with unique failure modes that don't intersect.

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Method for stabilizing continuous glucose monitoring systems to improve accuracy of measured blood sugar levels. The method involves dynamically adjusting the stabilization time required before displaying the sensor-transmitter data at the communication terminal. Instead of using a fixed stabilization time, it allows variable stabilization times based on the sensor data received. The stabilization steps include an initial stabilization phase set by the terminal, followed by a second phase where stabilization can occur for any time within the initial phase. If the second phase stabilization is not complete, a third stabilization phase is initiated. This allows the sensor data to stabilize without forcing users to wait an arbitrary fixed time. If stabilization is not achieved within the third phase, the terminal terminates the connection with the sensor.

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