Improving Accuracy and Precision in Glucose Monitoring Sensors

Enhancing the accuracy of glucose sensors and detecting sensor faults using secondary physiological measurements involves comparing glucose readings from the sensor with secondary metrics such as ketone levels and heart rate. This method helps confirm true hypo/hyperglycemia versus false conditions. Additionally, secondary measurements are used to correct glucose readings during sensor attenuation. By leveraging secondary sensors, this approach distinguishes between physiological and false glucose trends and optimizes lag correction.

A method for accurately determining continuous glucose levels from a glucose sensor without a separate blood glucose reference involves subtracting the time-dependent zero-signal level of the sensor from the continuous sensor signal. This compensates for drift and interference, resulting in a more accurate representation of the actual glucose level in the body fluid.

Continuous glucose monitoring system that reduces user pain and provides more accurate readings compared to invasive devices like lancets. The system uses both invasive and non-invasive glucose sensors to measure blood sugar. The invasive sensor takes an initial measurement, and the non-invasive sensor continuously monitors changes. This provides a more complete picture of glucose levels. The invasive sensor's initial reading is sent to a server along with the non-invasive sensor's continuous data. The server correlates the data to predict future glucose levels and trends. This allows users to proactively manage their diabetes without frequent invasive tests.

Improving the accuracy of continuous glucose monitoring systems by combining algorithms to compensate for lag and noise. The system monitors glucose levels over time and generates lag-corrected signals to improve point-wise accuracy. It also generates smoothed signals to reduce noise. These signals are then used separately to estimate glucose concentration and rate of change. This two-step process balances responsiveness and noise reduction.

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.

A blood glucose monitoring system that adapts measurement intervals based on user events to provide more accurate readings. The system detects glucose levels at regular intervals. When an event occurs like exercise, food intake, sleep, hormone change, etc., it increases the glucose measurement frequency. This captures rapid glucose changes after events. By using dynamic intervals, the system can better track user glucose trends and alert them to potentially dangerous levels.

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.

Enhancing glucose monitoring during exercise to improve the accuracy of continuous glucose monitoring (CGM) devices in people with diabetes. The method involves using signals from wearable devices that track physical activity during exercise to compensate for the error in CGM glucose estimation during exercise. The wearable signals are analyzed to calculate an enhancement factor to adjust the CGM glucose readings during exercise. This improves accuracy during exercise compared to relying solely on the CGM sensor.

Dynamic glucose monitoring in patients adaptively determines the frequency of fingerstick glucose tests based on factors like sensor reliability, glucose variability, and current level. It aims to reduce unnecessary fingersticks while still providing appropriate monitoring for individual patients. The system uses continuous glucose monitoring to track patients' levels, and based on factors like sensor reliability, glucose variability, and current level, determines the time for the next fingerstick. This allows more frequent checks when needed, and less frequent checks when stable.

Reducing noise in continuous glucose monitoring (CGM) systems by applying time-varying filtering to the signals during the monitoring period. The filtering smooths out the signals to counteract increasing noise levels as the CGM sensor degrades over time. The filter parameters are adjusted during the monitoring period to compensate for sensor degradation and other component changes that cause noise.

A body attachable unit for continuous glucose monitoring that can be inserted and attached to the skin using an applicator. The attachable unit contains the glucose sensor, electronics, and communication components. The user inserts the sensor into their skin through the applicator. After attachment, the user manually makes contact between the sensor and PCB to initiate operation. This allows precise sensor insertion timing, avoids contamination, and improves accuracy compared to pre-assembled sensors.

Redundant glucose sensors and disease management systems for diabetes management provide continuous monitoring when one sensor is in a warmup, stabilization, or end-of-life period. The system uses two glucose sensors on a patient simultaneously. If one sensor is in a non-operational period, the other sensor provides glucose readings for insulin administration. This ensures uninterrupted glucose monitoring for closed-loop insulin delivery. The system can also include redundant insulin pumps, applicators, and sensors housed together in a single unit to simplify management.

Real-time detection of early sensor signal attenuation (ESA) in continuous glucose monitoring (CGM) systems to accurately detect episodes of low sensor sensitivity that cause clinically significant errors. The method involves estimating the probability of ESA based on analyte sensor current signals, and then confirming ESA presence using a single blood glucose measurement. This provides accurate ESA detection with minimal false alarms.

Improving the accuracy of continuous glucose monitoring by calibrating the sensor data using the most frequently occurring signal points as a reference for the normal physiological glucose level. The method involves collecting glucose sensor data over time, identifying the signal data points that correspond to known physiological levels, and deriving glucose levels from the collected data using those identified points as a reference. This calibration improves accuracy by accounting for the sensor's drift and variability compared to the true physiological levels.

Compensating for errors during continuous glucose monitoring (CGM) to improve accuracy and reduce lag. The method involves computing sensor progression parameters (SPPs) based on ratios between current and previous glucose signals. These SPPs are used in a gain function to adjust the glucose determination gain and compensate for errors like signal drift and ISF lag. By referencing past signals to present ones, the method leverages the continuous nature of CGM to extract information from the data continuum that can improve glucose determination accuracy.

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.