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 continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

Abstract: A method of estimating a value of a physiological condition may be performed by an electronic device including one or more processors. The method involves generating a simulated measurement using an actual measurement from a first sensor as input to a translation model. The actual measurement includes one or more measurement parameters output by the first sensor as an indication of the value of the physiological condition. The simulated measurement includes one or more measurement parameters that a second sensor would output given the same value of the physiological condition. The method further involves estimating the value of the physiological condition through inputting the simulated measurement to an estimation model. The estimation model is configured to map the one or more measurement parameters that the second sensor would output to an estimated value for the physiological condition.

Abstract: Embodiments of the invention provide optimized polymeric surfaces adapted for use with implantable medical devices as well as methods for making and using such polymeric surfaces. These polymer surfaces have a constellation of features that function to inhibit or avoid an inflammatory immune response generated by implantable medical devices. Typical embodiments of the invention include an implantable glucose sensor used in the management of diabetes having a polymer surface with the disclosed constellation of features.

Abstract: A method of optimizing operation of a glucose sensor includes performing an electrochemical impedance spectroscopy (EIS) procedure to obtain imaginary impedance values for an electrode of a glucose sensor, calculating a change value as a difference between a threshold reference for the imaginary impedance values and a most-recent imaginary impedance value, and obtaining measurements of the calibration factor for the glucose sensor. The method also includes comparing the change value to a first threshold and the calibration factor to a second threshold and determining, based on the comparison, whether sensor data from the glucose sensor is valid.

Abstract: Medical devices and related systems and methods are provided. A method of estimating a physiological condition using a first sensing arrangement involves obtaining a sensor translation model associated with a relationship between the first sensing arrangement and a second sensing arrangement, wherein the second sensing arrangement is different from the first sensing arrangement, obtaining one or more measurements from a sensing element coupled to the processing system of the first sensing arrangement, determining simulated measurement data for the second sensing arrangement by applying the sensor translation model to the one or more measurements from the sensing element of the first sensing arrangement, and determining an estimated value for the physiological condition by applying an estimation model associated with the second sensing arrangement to the simulated measurement data.

Abstract: A continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

Abstract: Embodiments of the invention provide optimized polymeric surfaces adapted for use with implantable medical devices as well as methods for making and using such polymeric surfaces. These polymer surfaces have a constellation of features that function to inhibit or avoid an inflammatory immune response generated by implantable medical devices. Typical embodiments of the invention include an implantable glucose sensor used in the management of diabetes having a polymer surface with the disclosed constellation of features.

Abstract: An active learning system can analyze a dataset of users with self-reported symptoms and associated data from wearable devices to train a baseline machine learning model to predict symptoms of a chronic health condition based on wearable device data. For example, symptoms can be predicted in terms of lost physical activity, increased sleep requirements, and changes in resting heart rate. Using the baseline model, the active learning system can train and refine individual user-specific models to predict the onset of chronic health condition symptoms over time. These models can be used to predict symptoms for inclusion in a log of symptoms for the target user (which may be used by a healthcare provider to personalize treatment for the target user) or to provide interventions to the user (for example, warning of a predicted severe symptom day). In some implementations individual chronic health condition models are maintained and updated using active learning techniques.

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.

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

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 processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.

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 method of automatically initializing an analyte sensor for a user is disclosed here. A first analyte sensor is operated in a first measurement mode to generate first sensor signals indicative of an analyte level of the user. A second analyte sensor is deployed to measure the analyte level of the user, and is operated in an initialization mode, concurrently with operation of the first analyte sensor in the first measurement mode, to receive sensor configuration data generated by the first analyte sensor. During operation of the second analyte sensor in the initialization mode, the second analyte sensor is calibrated with at least some of the received sensor configuration data. After the calibrating, operation of the second analyte sensor is transitioned from the initialization mode to a second measurement mode during which the second analyte sensor generates second sensor signals indicative of the analyte level of the user.

Abstract: A method for retrospective calibration of a glucose sensor uses stored values of measured working electrode current (Isig) to calculate a final sensor glucose (SG) value retrospectively. The Isig values may be preprocessed, discrete wavelet decomposition applied. At least one machine learning model, such as, e.g., Genetic Programing (GP) and Regression Decision Tree (DT), may be used to calculate SG values based on the Isig values and the discrete wavelet decomposition. Other inputs may include, e.g., counter electrode voltage (Vcntr) and Electrochemical Impedance Spectroscopy (EIS) data. A plurality of machine learning models may be used to generate respective SG values, which are then fused to generate a fused SG. Fused SG values may be filtered to smooth the data, and blanked if necessary.

Abstract: Techniques for sensor calibration involve determining a generative model using sensor measurements from different instances of a first glucose sensor together with corresponding reference glucose values. The generative model is configured to generate a simulated measurement representing a predicted output of the first glucose sensor under specific operating conditions. A set of simulated measurements is generated using operating conditions observed with respect to a second glucose sensor as inputs to the generative model. The second glucose sensor is a sensor of a different design. The operating conditions observed with respect to the second glucose sensor include reference glucose values obtained in connection with measurements made using the second glucose sensor. The simulated measurements are then used to determine an estimation model for the first glucose sensor. The estimation model is configured to estimate glucose level given one or more sensor measurements from the first glucose sensor.

Abstract: A fluid infusion system includes a housing configured to be adhesively coupled to an anatomy of a user, and a tube configured to extend from the housing for insertion into the anatomy of the user. The tube includes a plurality of conduits defined within the tube. The plurality of conduits include a fluid delivery conduit configured to facilitate a fluidic connection between a fluid source and the anatomy of the user, and one or more conduits configured to accommodate a plurality of electrodes for determining a physiological characteristic of the user.

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.