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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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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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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Improving the accuracy of continuous in-body analyte monitoring by calibrating the sensor readings using known physiological levels. The method involves collecting sensor data over a period, identifying the most frequently occurring signal points, and using those as reference points to derive calibrated analyte levels. This leverages the fact that the body naturally maintains stable analyte levels for most of the time. By identifying the most frequently occurring signals, it can determine the normal physiological level and use that as a reference to calibrate the readings.

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In vivo glucose monitoring system with error handling and data storage to improve accuracy and reliability. The system has a glucose sensor implanted under the skin to continuously monitor glucose levels. The sensor signals are processed by electronics on the body. When an error occurs with the sensor, instead of losing data, it is stored in a dedicated error memory location. A flag is also stored indicating an error. When requested, the system sends both the normal data and the error data in a single packet to prevent missing any data due to sensor issues. This ensures complete glucose level records are available even if the sensor has errors.

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Intelligent real-time dynamic blood glucose monitoring system using cloud big data to improve accuracy of implantable glucose sensors. The system compensates for individual variations in sensor signals by leveraging user's historical blood glucose data. It calculates a regression equation between current sensor signals, impedance measurements, and user's past glucose values. This equation is used to correct and compensate the sensor output to improve accuracy. By leveraging user's historical data, the system mitigates issues like sensor drift, skin reactions, and user-specific differences between subcutaneous and blood glucose levels.

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