Noise Reduction in Glucose Sensors

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Accurately calibrating biometric signals from a continuous monitoring device like a glucose sensor in a way that compensates for instability in the sensor's performance over time. The method involves calculating calibration factors using reference biometric values, but if the sensor's calibration parameters are unstable, it corrects the calculated factor instead of using it directly. The correction is based on the difference between the calculated factor and the previous factor. This allows accurate calibration even when sensor stability is changing. The correction is further refined by considering the section of sensor usage where the reference biometric was taken, as stability varies over time.

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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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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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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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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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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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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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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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Continuous glucose monitoring device with improved noise filtering to enhance accuracy and reduce false alarms. The device uses a parameter estimation filter to process the glucose data. The filter estimates the filter parameters based on the signal-to-noise ratio to adaptively optimize the filtering. This allows the filter to better handle varying levels of noise in the glucose signals. It involves preprocessing the glucose data, estimating filter parameters, and applying the filter.

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Detecting erroneous measurements from continuous in-vivo blood analyte sensors like glucose monitors in medical applications. The method involves using a wavelet transform to analyze the sensor signals. It compares the sensor signal to time-shifted and frequency altered versions of a wavelet function to reveal unusual signal features. This detailed local analysis enables detecting signal irregularities during blood draw, clearance, and calibration segments that may indicate errors like dilution.

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