Measurement Precision in CGM Device Production

A glucose sensor with long-term stable and consistent sensitivity for accurate glucose detection. The sensor has an enzyme electrode coated with glucose oxidase and a cationic conductive copolymer. An external electronic mediator, like Ru(NH3)6Cl3, is added to the sample and replaces oxygen in the enzyme reaction. This provides a sustained electron transfer pathway for consistent sensitivity over time. The mediator is added externally instead of immobilizing it on the electrode to prevent loss or film layer issues.

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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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Retrospective algorithm to improve accuracy and precision of continuous glucose monitoring (CGM) data by exploiting reference blood glucose (BG) measurements and a new constrained regularized deconvolution method. The algorithm takes CGM data, BG references, a sensor accuracy degradation model, and a glucose kinetics model as input. It outputs a retrofitted quasi-continuous glucose concentration signal that is more accurate and precise than the original CGM data, along with a calibrated CGM time series and flagged outliers. The algorithm uses BG references sparsely and frequently to correct the noisy, biased CGM data.

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Accurately determining glucose levels from continuous glucose monitors without requiring calibration with fingerstick blood tests involves subtracting the sensor's zero-signal level—obtained from calibration data—from the continuous sensor signal. This adjustment eliminates baseline drift, enabling direct determination of glucose levels from the sensor readings alone.

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Improving the accuracy of continuous glucose monitoring (CGM) devices by using sensor progression parameters to correct for errors and delays in glucose measurements. The method involves calculating ratios between current and previous glucose signals from a CGM sensor. These ratios are stored in a gain function and used to adjust the CGM glucose value at the current point. This reduces errors from sources like sensor drift and interstitial fluid delays.

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More accurate and efficient method for determining glucose levels in real-time using a continuous glucose monitoring (CGM) system. The method involves estimating the current glucose level in the blood based on interstitial fluid glucose measurements using a Kalman filter. It improves the estimation accuracy compared to previous methods by leveraging the dynamics of glucose diffusion between blood and tissue. The filter accounts for the time delay between blood and interstitial glucose changes. This allows more precise estimation of blood glucose levels from interstitial measurements.

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Continuous glucose monitoring (CGM) system that enables accurate, stable, and uninterrupted glucose monitoring without the need for frequent fingerstick calibrations. The system involves overlapping sensor placements during sensor swaps. After calibrating the first sensor, a second sensor is placed while the first is still in the body. The second sensor's calibration is based on data from the first sensor, eliminating the need for fingerstick calibrations. This allows continuous calibration and monitoring without gaps as sensors are replaced.

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Real-time blood glucose monitoring system that provides accurate, trended, and real-time blood glucose data to help users make better diabetes management decisions. The system uses a noninvasive sensor to continuously monitor blood glucose levels. It analyzes the data in real time to detect anomalies like rapid changes or trends outside normal ranges. This allows quick identification of potential issues before they become critical. The system provides alerts and indicators in real time to alert users when blood glucose levels are outside normal ranges or trends. This helps users catch potential issues early before they become critical.

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Improving continuous glucose monitoring (CGM) systems by using multiple machine learning models trained for specific conditions to generate more reliable sensor glucose values. The models are trained separately for factors like sensor data availability, accuracy, and probabilistic reliance. During CGM, the models are applied and outputs averaged to generate the sensor glucose value. For outlier conditions, signatures in sensor data are identified and models adjusted to prioritize those associated with the signature. This allows customization of models for situations like limited data or high variability to improve accuracy.

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Monitoring glucose levels from interstitial fluid using continuous glucose monitoring systems to overcome noise and lag issues. The method involves calculating glucose levels using a lag correction algorithm that minimizes errors between interstitial and blood glucose. It also calculates smoothed glucose levels using a smoothing algorithm to reduce noise. The lag-corrected and smoothed signals are then weighted and combined to provide accurate glucose levels and rates of change.

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Continuous glucose monitoring system and method that improves the accuracy of measuring current glucose levels in the body, particularly in blood, by estimating past glucose levels using a moving horizon estimation technique. The method involves estimating past glucose levels over a moving time window, optimizing the window length, and using the estimated past levels for calibration to compensate for the time delay and attenuation between blood and tissue glucose. This provides more accurate current glucose measurements compared to traditional calibration methods.

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Correcting glucose sensor measurements in the presence of interferents like acetaminophen without adding extra components to the sensor. The method involves detecting interferents in body fluids using electrochemical signals and rates of change from the glucose sensor. By modeling the sensor response to interferents, it can correct the glucose measurements to mitigate bias caused by interferents. The correction is based on historical data, user characteristics, and environmental factors.

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Dynamic glucose monitoring system that optimizes blood glucose testing frequency based on sensor reliability and current glucose level. It determines the time between fingerstick tests based on factors like sensor reliability, glucose trend, and current level versus target range. This allows tailoring testing to patient needs rather than fixed schedules.

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Continuous glucose monitoring (CGM) system that improves reliability and reduces calibration needs by detecting medication interferences and correcting glucose readings. The system uses two electrodes on a user, one with glucose oxidase (GOx) and one without, set to different voltages. Comparing signals from both electrodes allows detecting medication ingestion that affects GOx. This signal comparison is used to adjust CGM glucose readings. The second electrode serves as a background reference to isolate GOx interference.

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Long-term implantable glucose sensor for continuous monitoring of tissue glucose levels in subjects with diabetes. The sensor is fully implantable, compact, hermetically sealed, and wirelessly transmits glucose data outside the body. It uses a detector array with enzyme-coated membranes, electronics, power source, and telemetry portal. The sensor is implanted subcutaneously for months to years and provides stable, accurate glucose readings for diabetes management. The sensor design allows long-term implantation by minimizing tissue irritation, variability in microvascular perfusion, and enzyme inactivation.

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A system for interpreting and managing bioanalyte measurements from devices like glucose sensors to provide useful information to users. The system involves collecting data from bioanalyte monitoring devices, interpreting it to account for delays between interstitial and capillary measurements, and sending the interpreted data to external devices with specialized software to provide actionable insights to users. This allows more accurate and clinically relevant interpretation of interstitial measurements compared to calibrated capillary measurements.

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Reducing the need for calibration requests in continuous glucose monitoring (CGM) systems by using sensor data fusion and electrochemical impedance spectroscopy (EIS) to autonomously diagnose and optimize sensor performance. The method involves periodically measuring sensor current, voltage, and EIS data, and pairing it with calibration values. When calibration is valid, sensor glucose is calculated based on the paired data. This allows autonomous calibration-free glucose estimation without requiring finger sticks.

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Integrated diabetes management system that uses exogenous data to improve glucose monitoring accuracy and enhance diabetes management. The system processes glucose level trends along with exogenous factors like insulin delivery and meal intake to provide more reliable projected alarms. It also allows fine-tuning of patient-specific insulin data like basal rates, carb ratios, and sensitivity based on historical glucose profiles. This leverages continuous glucose monitoring (CGM) data to improve diabetes management compared to just using the CGM alone.

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Analyte detection and treatment dosing system for monitoring and regulating analyte levels in bodily fluids like blood. The system uses a pump to withdraw samples for analysis, a sensor to measure analyte concentration, and a treatment pump to infuse medication based on the concentration. It aims to provide accurate, personalized dosing of drugs like insulin to maintain glycemic control. The system also has features like calibration, interferent compensation, and user feedback to improve accuracy and safety.

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Compensating for errors in continuous glucose monitoring (CGM) by leveraging sensor progression parameters derived from past glucose measurements. The method involves computing ratios of current glucose to past glucose levels, which are used as sensor progression parameters. These parameters are then fed into a gain function along with the current glucose level to calculate a compensated glucose value. This compensated value has reduced error compared to the raw CGM reading. The gain function allows adjusting for factors like sensor drift and interstitial fluid lag. By compensating for errors at each point using historical data, the overall CGM accuracy can be improved.

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