Noise Reduction in Glucose Sensors

Method for accurately estimating blood glucose levels using interstitial fluid glucose measurements. It involves a sensor to measure interstitial glucose levels, a Kalman filter to estimate blood glucose based on the sensor readings, and a state transition model to account for factors like diffusion. The method uses filtering, noise estimation, and outlier detection to improve accuracy. The state transition models adapt based on glucose rate changes.

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Filtering continuous glucose monitor (CGM) signals using a modified Kalman filter to improve accuracy and reduce downtime compared to conventional techniques. The filter adapts the noise covariance terms based on detected artifacts in the raw CGM signal. When artifacts like pressure or zero crossings are identified, the process and measurement noise covariances are updated. This allows the filter to better handle non-analyte related noise that can impact CGM performance.

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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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Calibrating a handheld diabetes managing device to accurately determine interstitial glucose levels from a continuous glucose monitor (CGM) without needing separate blood glucose measurements. The method involves subtracting the time-dependent zero-signal level of the CGM sensor from the continuous sensor signal to compensate for baseline drift. This allows the CGM to provide accurate glucose readings without needing separate calibration using blood glucose measurements.

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Monitoring blood glucose levels using a continuous glucose sensor with improved noise reduction techniques. The sensor filtering uses a Kalman filter with adaptive noise covariance estimates based on innovation and residual signals. This allows better noise reduction without long filtering gaps. Artifact detection is also implemented to trigger noise covariance updates. The technique adapts the filter parameters in real-time based on signal characteristics to better track glucose levels with reduced error.

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Adjusting blood glucose algorithms in a continuous glucose monitoring (CGM) system using motion sensors to provide more accurate glucose readings and alerts based on patient activity levels. The CGM system with motion sensors detects activity like sleeping or exercising. It then adjusts the glucose algorithms to account for the activity-related glucose fluctuations. For example, it recalculates low glucose alerts for sleeping patients to prevent false alarms.

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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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Improving the accuracy and reliability of continuous glucose monitoring (CGM) systems by using machine learning to detect and correct errors in complex sensor data in real time. The system trains a machine learning model to identify outlier measurements based on sensor data behavior signatures informed by criteria like iCGM. If an outlier is detected, the sensor data is blanked or not displayed. The system also trains a model to identify erroneous sensor use conditions based on error patterns. The model determines resolutions to correct the errors.

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Improving the reliability of continuous glucose monitoring (CGM) devices, particularly for detecting the presence of acetaminophen medication and correcting the sensor glucose value. The method involves activating a glucose oxidase (GOx) electrode and a non-GOx electrode on the user. They are set to different voltages and signals are compared to detect acetaminophen ingestion. If acetaminophen is detected, the sensor glucose value is adjusted using a predetermined model. This allows for reducing finger sticks for calibration and improving CGM reliability by compensating for medication interference.

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Glucose sensing device that reduces the effect of noise in measuring glucose concentration. The device has a unique sensing method to minimize noise compared to conventional glucose sensors. The device generates a sensing pulse signal using just the current from the second sensing state when the voltage is different from the reference voltage. This pure current is used to generate the sensing pulse width, which is then compared to a reference pulse width to generate digital measurement data. Subtracting the noise current in the first state reduces noise in the final measurement.

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Redundant glucose sensing and disease management system that provides continuous monitoring when one sensor is in warmup, stabilization, or end-of-life periods. Multiple glucose sensors and insulin pumps attach to a patient simultaneously and communicate with each other. If a sensor goes offline, another sensor takes over. This ensures continuous glucose monitoring for closed-loop insulin administration systems even if a sensor fails.

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Improving accuracy of continuous glucose monitoring (CGM) devices by using short-term prediction to reduce random noise and calibration errors. The method involves substituting the current CGM reading with a predicted glucose value a short horizon ahead. This compensates for delays in the BG-to-IG kinetics and improves accuracy, especially at hypoglycemic levels. A Kalman filter is used for the prediction.

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Real-time detection of declining sensitivity in continuous glucose monitors (CGMs) to improve accuracy and prevent false alarms. The detection involves estimating the probability of sensitivity decline based on current sensor signals, and confirming if a threshold is exceeded using a single blood glucose measurement. This allows accurate detection of declining sensitivity without false positives.

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Method to accurately determine blood glucose levels using a continuous glucose monitoring (CGM) system by leveraging the moving horizon estimation (MHE) technique. The method involves continuously measuring interstitial glucose levels using a sensor and applying MHE to estimate the blood glucose based on the interstitial measurements. This takes into account the time delay and diffusion between blood and interstitial glucose levels. The MHE allows adaptive estimation of noise variances and model parameters over time for improved accuracy compared to Kalman filtering or smoothing the interstitial signals.

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Blood glucose measuring device that adaptively adjusts calibration intervals to minimize pain and errors. The device compares glucose readings from the sensor versus blood to calculate sensor error. It then determines how long it takes for that error to reach a threshold, using the initial calibration interval. The device then sets the calibration interval based on that time, allowing more frequent calibration if sensor error is high. This allows personalized calibration intervals based on sensor performance.

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Communication protocol for data communication between a continuous glucose monitoring transmitter and receiver. It involves generating a radio frequency (RF) data stream based on the monitored glucose data using a low-power, low-noise logic circuit consisting of finite-state machines and digital circuits. This avoids a high-power ASIC and enables accurate glucose monitoring for diabetes treatment. The transmitter sends glucose data in a synchronized window over RF to the receiver, which identifies the transmitter and displays the glucose levels.

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Monitoring the reliability of a glucose sensor used in closed-loop insulin pumps to determine when to switch to manual or open-loop operation. The reliability is assessed based on trends in the sensor data. If the trends indicate reduced sensitivity, anomalies, drift, or noise, it indicates the sensor is less reliable. A metric representing the reliability is calculated based on these trends. If the metric falls below a threshold, it triggers a switch to manual or open-loop operation to prevent relying on potentially inaccurate sensor readings for closed-loop insulin delivery.

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Implantable glucose sensor that reduces calibration needs compared to traditional glucose sensors. The sensor has two electrodes, one inside the enzymatic part of the membrane and one outside. By measuring both signals, it detects changes in non-glucose electroactive compounds like urea that affect sensor performance. This allows adaptive calibration based on stability instead of frequent calibrations. It also helps prevent false readings by filtering when glucose transport stability falls. The sensor design enables bifunctionality with enzyme and non-enzyme electrodes.

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Estimating actual glucose levels in a diabetic person when sensor noise or failure is present. The method involves using a probability analysis tool to determine the accuracy of the glucose sensor based on the measured results. The glucose results are then analyzed weighted by the sensor accuracy. A recursive filter estimates the actual glucose level using the weighted results. This allows more reliable glucose level estimation in the presence of sensor noise or failure.

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Reliability analysis of glucose sensor signals in closed loop glucose control systems to determine if the sensor is stable and accurate enough to trust for critical decisions like insulin dosing. The analysis involves evaluating metrics of potential sensor signal trends to detect significant changes in responsiveness over time. If the metrics exceed thresholds, it indicates sensor instability and may trigger alerts or adjustments to the closed loop system. This proactive sensor reliability monitoring helps mitigate issues like drifting, noise, and artifacts that could compromise patient safety.

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