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: 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: 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: Disclosed are methods and corresponding systems and devices for providing an estimation model for use with one or more instances of a particular sensor. In some aspects, an estimation model usable for estimating a value of a physiological condition is determined based at least in part on simulated measurements. The simulated measurements are generated for a first sensor, through applying a translation model to convert historical measurements associated with a second sensor into measurements that would have been produced by the first sensor. The second sensor has a different design or configuration than the first sensor. The historical measurements represent changes in the physiological condition as observed by different instances of the second sensor. The estimation model can be made available to one or more electronic devices, including at least one device configured to apply the estimation model to a measurement from a corresponding instance of the first sensor.

Abstract: Medical devices and related systems and methods are provided. A method of estimating a physiological condition involves determining a translation model based at least in part on relationships between first measurement data corresponding to instances of a first sensing arrangement and second measurement data corresponding to instances of a second sensing arrangement, obtaining third measurement data associated with the second sensing arrangement, determining simulated measurement data for the first sensing arrangement by applying the translation model to the third measurement data, and determining an estimation model for a physiological condition using the simulated measurement data, wherein the estimation model is applied to subsequent measurement output provided by an instance of the first sensing arrangement to obtain an estimated value for the physiological condition.

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: 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: Medical devices and related patient management systems and parameter modeling methods are provided. An exemplary method of operating a sensing device associated with a patient involves obtaining current operational context information associated with the sensing device, obtaining a parameter model associated with the patient, calculating a current parameter value based on the parameter model and the current operational context information, obtaining one or more signals from a sensing element configured to measure a condition in a body of the patient, and providing an output that is influenced by the calculated current parameter value and the one or more signals.

Abstract: A continuous glucose monitoring system may employ complex redundancy to take operational advantage of disparate characteristics of two or more dissimilar, or non-identical, sensors, including, e.g., characteristics relating to hydration, stabilization, and durability of such sensors. Fusion algorithms, Electrochemical Impedance Spectroscopy (EIS), and advanced Application Specific Integrated Circuits (ASICs) may be used to implement use of such redundant glucose sensors, devices, and sensor systems in such a way as to bridge the gaps between fast start-up, sensor longevity, and accuracy of calibration-free algorithms. Systems, devices, and algorithms are described for achieving a long-wear and reliable sensor which also minimizes, or eliminates, the need for BG calibration, thereby providing a calibration-free, or near calibration-free, sensor.

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: 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: 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, 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: 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: A method for optional external calibration of a calibration-free glucose sensor uses values of measured working electrode current (Isig) and EIS data to calculate a final sensor glucose (SG) value. Counter electrode voltage (Vcntr) may also be used as an input. Raw Isig and Vcntr values may be preprocessed, and low-pass filtering, averaging, and/or feature generation may be applied. SG values may be generated using one or more models for predicting SG calculations. When an external blood glucose (BG) value is available, the BG value may also be used in calculating the SG values. A SG variance estimate may be calculated for each predicted SG value and modulated, with the modulated SG values then fused to generate a fused SG. A Kalman filter, as well as error detection logic, may be applied to the fused SG value to obtain a final SG, which is then displayed to the user.

Abstract: A method for optional external calibration of a calibration-free glucose sensor uses values of measured working electrode current (Isig) and EIS data to calculate a final sensor glucose (SG) value. Counter electrode voltage (Vcntr) may also be used as an input. Raw Isig and Vcntr values may be preprocessed, and low-pass filtering, averaging, and/or feature generation may be applied. SG values may be generated using one or more models for predicting SG calculations. When an external blood glucose (BG) value is available, the BG value may also be used in calculating the SG values. A SG variance estimate may be calculated for each predicted SG value and modulated, with the modulated SG values then fused to generate a fused SG. A Kalman filter, as well as error detection logic, may be applied to the fused SG value to obtain a final SG, which is then displayed to the user.

Abstract: Medical devices and related systems and methods are provided.

Abstract: Medical devices and related systems and methods are provided. A method of estimating a physiological condition involves determining a translation model based at least in part on relationships between first measurement data corresponding to instances of a first sensing arrangement and second measurement data corresponding to instances of a second sensing arrangement, obtaining third measurement data associated with the second sensing arrangement, determining simulated measurement data for the first sensing arrangement by applying the translation model to the third measurement data, and determining an estimation model for a physiological condition using the simulated measurement data, wherein the estimation model is applied to subsequent measurement output provided by an instance of the first sensing arrangement to obtain an estimated value for the physiological condition.

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.