Abstract:
Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.
Abstract:
Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe retrieving a machine learning model that is trained to classify CGM sensor data and blanking the CGM sensor data based on an outlier classification from the machine learning model. The system may terminate sensors for which there is an aggregation of blanked CGM sensor data. The methods, systems, and devices described herein may additionally comprise a machine learning model that is trained to detect and correct for erroneous sensor use conditions based on error patterns in sensor data. The system may determine resolutions for correcting the detected erroneous sensor use conditions.
Abstract:
Methods and systems for sensor calibration and sensor glucose (SG) fusion are used advantageously to improve the accuracy and reliability of orthogonally redundant glucose sensor devices, which may include optical and electrochemical glucose sensors. Calibration for both sensors may be achieved via fixed-offset and/or dynamic regression methodologies, depending, e.g., on sensor stability and Isig-Ratio pair correlation. For SG fusion, respective integrity checks may be performed for SG values from the optical and electrochemical sensors, and the SG values calibrated if the integrity checks are passed. Integrity checks may include checking for sensitivity loss, noise, and drift. If the integrity checks are failed, in-line sensor mapping between the electrochemical and optical sensors may be performed prior to calibration. The electrochemical and optical SG values may be weighted (as a function of the respective sensor's overall reliability index (RI)) and the weighted SGs combined to obtain a single, fused SG value.
Abstract:
Methods and systems for sensor calibration and sensor glucose (SG) fusion are used advantageously to improve the accuracy and reliability of orthogonally redundant glucose sensor devices, which may include optical and electrochemical glucose sensors. Calibration for both sensors may be achieved via fixed-offset and/or dynamic regression methodologies, depending, e.g., on sensor stability and Isig-Ratio pair correlation. For SG fusion, respective integrity checks may be performed for SG values from the optical and electrochemical sensors, and the SG values calibrated if the integrity checks are passed. Integrity checks may include checking for sensitivity loss, noise, and drift. If the integrity checks are failed, in-line sensor mapping between the electrochemical and optical sensors may be performed prior to calibration. The electrochemical and optical SG values may be weighted (as a function of the respective sensor's overall reliability index (RI)) and the weighted SGs combined to obtain a single, fused SG value.
Abstract:
Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe retrieving a machine learning model that is trained to classify CGM sensor data and blanking the CGM sensor data based on an outlier classification from the machine learning model. The system may terminate sensors for which there is an aggregation of blanked CGM sensor data. The methods, systems, and devices described herein may additionally comprise a machine learning model that is trained to detect and correct for erroneous sensor use conditions based on error patterns in sensor data. The system may determine resolutions for correcting the detected erroneous sensor use conditions.
Abstract:
Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.
Abstract:
Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.
Abstract:
Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.
Abstract:
A pseudo-orthogonally redundant glucose sensor device may include one or more electrochemical peroxide-based glucose sensor(s) and one or more electrochemical oxygen-based sensor(s). The electrochemical peroxide-based glucose sensor(s) may operate as traditional peroxide-based sensor(s), which may include a chemistry stack with glucose oxidase as a catalytic agent. The electrochemical oxygen-based sensor(s) may be used to measure oxygen, as well as to measure glucose by computing differences in oxygen between two working electrodes. In embodiments of the invention, one of the oxygen-based sensors may be used directly as a diagnostic to determine whether each peroxide-based glucose sensor is functioning properly, as well as to determine which modality of sensing to use. Because of the internal oxygen-based reference, the glucose sensor device provides oxygen-resistant glucose sensing, as well as near-orthogonal redundancy.
Abstract:
Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.