Advancements in Calibrating and Recalibrating Glucose Monitoring Sensors

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.

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.

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.

Calibrating blood glucose measurements from a continuous glucose monitoring system to improve accuracy and reliability. The calibration method selects between two modes based on the difference between blood glucose values measured by the continuous monitor and a reference device. In the first mode, the reference value must fall within a calculated range based on the continuous measurement to calibrate. In the second mode, multiple reference values are used to calibrate if the first mode is not applicable. This prevents inaccurate calibration when single reference values are outliers.

Closed-loop insulin infusion systems using orthogonally redundant glucose sensors for improved accuracy and reliability. The system has two glucose sensors, one optical and one electrochemical, to provide orthogonal redundancy. An algorithm combines the sensor data to improve accuracy and reliability. If one sensor fails, the other can provide glucose values. The sensors have features like distributed electrodes and membrane barriers to reduce drift and fouling. The system uses on-demand calibration rather than frequent fingersticks.

Optional calibration of calibration-free glucose sensors using internal sensor data and electrochemical impedance spectroscopy (EIS) to reduce or eliminate the need for external calibration references. The method involves measuring the sensor's current signals and performing EIS procedures to calculate glucose values. By fusing the results from multiple predictive models and applying filtering, a calibrated glucose value is obtained without a fingerstick. The sensor's data provides internal calibration and stability feedback.

Glucose sensor for continuous monitoring that doesn't require frequent calibration. The sensor has two electrodes, one measuring glucose and the other measuring a constant analyte like oxygen. By monitoring the constant analyte over time, changes in solute transport through the sensor membrane can be detected. This indicates sensor stability. If stability falls below a threshold, calibration is suspended and readings are filtered. If stability rises, calibration is performed. This prevents calibration when glucose transport is unstable, improving sensor accuracy.

Calibration technique for glucose sensors that improve accuracy by deriving a non-linear mapping between sensor signal values and actual blood glucose concentrations based on temporal pairings of sensor readings with reference glucose measurements. The non-linear mapping is derived as a piece-wise function with linear and non-linear portions. For the non-linear portion, it can be a polynomial expression with coefficients determined from the pairings, or an exponential expression with parameters derived from the pairings. Applying the derived mapping allows more accurate glucose sensing when the sensor output doesn't correlate linearly with blood glucose levels.

Rapid calibration method for glucose sensors using a calibration kit that enables quick and accurate calibration in under 10 minutes. The method involves preparing calibration solutions containing both alpha and beta forms of glucose instead of just alpha. This provides an equilibrium glucose mixture that stabilizes sensor readings faster. The kit has compartments for water, glucose, and an optional slow-release capsule. Inserting the sensor into the water compartment, breaking the barrier, and taking readings provide a 3-point calibration. The kit enables easy sterile calibration and reduces time compared to making separate glucose solutions.

Calibrating a handheld diabetes managing device to accurately estimate glucose levels from continuous glucose monitor (CGM) readings. The calibration involves specific conditions on the CGM data quality. If the CGM readings have low variability (low SD) and a median close to the mean, it indicates reliable data. The calibration equation is determined using a single blood glucose measurement and the CGM readings when these conditions are met. This allows estimating glucose levels from the CGM when the quality criteria are satisfied.

Improving accuracy of continuous glucose monitoring (CGM) sensors by considering insulin delivery information during calibration. The CGM sensor calibration routine factors in parameters like insulin dose, time since dose, and insulin sensitivity to adjust calibration sensitivity determination. This accounts for how insulin affects glucose levels and reduces calibration errors when insulin is being delivered. It delays calibration if rapid insulin changes are detected to avoid lag errors. The CGM system also modifies insulin targets during calibration to optimize accuracy.

Calibration technique for glucose sensors used in continuous glucose monitoring systems that improve accuracy by selectively excluding contaminated or anomalous blood samples during calibration. The calibration process involves obtaining blood glucose samples from the patient and using them to derive a relationship between sensor signal values and actual glucose levels. However, instead of using all samples, some are excluded based on criteria like deviating from the trend, exceeding a threshold difference, or containing contaminants. This helps ensure the calibration is based on clean, accurate samples and reduces errors from contamination or outliers.

Calibration methods for glucose monitors that improve accuracy and reduce the number of fingersticks needed to calibrate. The methods involve calculating calibration characteristics using reference glucose values and monitor data, then applying the characteristics to the monitor data to calibrate. This allows using historical data instead of relying solely on current fingerstick results. The calibration techniques account for factors like rate of change to improve accuracy during rapid glucose swings. The methods also involve filtering and verification to minimize false readings.

An automated calibration method for glucose sensors that allows accurate calibration without infusing excessive amounts of glucose into the patient. The calibration involves passing known glucose concentrations through the sensor without infusing them into the patient. This is done using separate fluid sources and pumps connected to the sensor. The calibration can use multiple known concentrations to account for drift and bias errors. The calibration fluid can be saline-based to match the patient's blood. This allows calibration without needing blood samples or infusions. The method provides a flexible and accurate calibration option for glucose sensors.

Calibration methods for glucose monitoring systems that improve accuracy and reduce fingerstick frequency. The methods involve calibrating glucose sensor readings using reference values from a separate glucose meter. The calibration factors are determined by regressing sensor data against meter readings. This allows adjusting sensor readings to match the meter more closely. The calibration factors are calculated using linear or non-linear regression techniques.

A quick and convenient method for calibrating glucose sensors to improve accuracy and reduce errors. The method involves multipoint calibration techniques using techniques like sequential addition of calibration solutions, separate chambers with dividers, timed-release capsules, and rupturable chambers. These methods enable step-by-step calibration points to be obtained for a sensor without needing separate calibration devices. The calibration factors are calculated using the sensor signals from each step to accurately calibrate the sensor. This allows on-the-spot calibration before use to ensure accurate glucose measurement.

Calibration techniques for implantable sensors like glucose sensors adaptively recalibrate over time to maintain accuracy as the sensor degrades. The technique involves a two-step calibration process. First, the sensor output is adjusted based on data points compared to blood glucose readings. Second, the calibration curve is adjusted piecewise to fit the data points. This iterative calibration method accounts for sensor nonlinearities and aging effects.

Accurately calibrating continuous glucose monitoring devices without invasive blood draws. The method involves measuring glucose levels concurrently in multiple regions like blood and tissue. This accumulated data is correlated and processed to generate calibration data for the device. By measuring glucose variations between regions and tracking offsets, it provides accurate calibration without invasive blood draws.

Calibration methods for glucose monitoring systems that use continuous sensor data to improve accuracy over time. The methods involve calculating calibration factors using reference glucose values from a separate meter and comparing them to sensor readings. The factors are then applied to the sensor data to correct for drift and errors. This adaptive calibration adapts to sensor performance over time and compensates for drift without requiring frequent finger pricks. It uses sensor data itself to calibrate instead of discrete fingerstick readings.

Improving the performance of implantable glucose sensors by reducing bio interference from the body. The sensors are modified to have a compartment containing catalase and other reactive oxygen species scavengers. This compartment surrounds the glucose sensing electrode and reduces the diffusion of reactive oxygen species like hydrogen peroxide out of the sensor. Keeping the sensor-generated reactive oxygen species at low levels, prevents biofouling, cell attraction, and tissue encapsulation around the sensor that can degrade its performance.