Integration of in vivo predictive model output features for CGM algorithm performance improvement

Abstract

The technology disclosed herein relates to glucose level measurement and / or management. In some embodiments, the techniques involve obtaining in vivo characteristics of a glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristics including in vivo sensitivity, in vivo intercept, or a combination thereof; receiving sensor measurement data measured by the glucose sensor, the sensor measurement data including sensor current (Isig), back voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age limit of the glucose sensor, or a combination thereof; and estimating a sensor glucose (SG) value using a sensor SG model, where input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

Description

Technical Field

[0001] The present disclosure generally relates to glucose level measurement and / or management. Background Art

[0002] In a healthy person, the pancreas produces insulin in response to elevated glucose levels and releases it into the bloodstream. More specifically, the beta cells (β-cells) in the pancreas produce insulin as needed and secrete it into the bloodstream. If the β-cells become incapable or die (known as type 1 diabetes), the diabetic's body may need to be supplied with insulin using, for example, a syringe, pen, or infusion pump for a blood sugar level management system to maintain health or life.

[0003] Some blood sugar level management systems can be closed-loop systems, which can include automatic or semi-automatic control to deliver insulin to the patient's pump. Some blood sugar level management systems may need to manually administer insulin based on the dosage determined by the insulin calculator. The time and amount of insulin delivered to the patient can be controlled, and these time and amount are determined based on the amount of carbohydrates such as the patient's intake and / or the real-time measurement of glucose levels by a glucose sensor. Glucose sensors can detect and / or quantify the amount of glucose (e.g., interstitial glucose or blood sugar) in the patient's body, which enables patients and medical staff to monitor the physiological conditions in the patient's body. In many cases (e.g., diabetic patients), continuous monitoring of glucose levels may be beneficial. Such information may help monitor and / or adjust the treatment regimen that generally includes administering insulin to the patient. For the purpose of continuous glucose monitoring (CGM) or determining the operating command of an infusion pump, continuous sampling and monitoring of the user's blood sugar level that may damage battery life is not adopted, but intermittently sensed glucose data samples can generally be used. Summary of the invention

[0004] The present disclosure as a whole relates to glucose level measurement and / or management. More specifically, the technology disclosed herein relates to estimating blood glucose levels using measurement data from a subcutaneous sensor and in vivo characteristics of the subcutaneous sensor predicted based on manufacturing process measurement data. The technology disclosed herein can be practiced in a variety of ways, such as using a processor-implemented method, a system including one or more processors and one or more processor-readable media, and / or one or more (non-transient) processor-readable media.

[0005] According to certain embodiments, these techniques involve obtaining in vivo characteristics of a glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristics including in vivo sensitivity, in vivo intercept, or a combination thereof; receiving sensor measurement data measured by the glucose sensor, the sensor measurement data including sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof; and estimating a sensor glucose (SG) value using a sensor glucose (SG) model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is the output of the SG model.

[0006] This summary is neither intended to identify the main or essential features of the claimed subject matter nor is it intended to be used alone to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of the disclosure, any or all of the drawings, and each claim. The foregoing and other features and examples will be described in more detail in the following description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Exemplary embodiments are described in detail below with reference to the accompanying drawings.

[0008] Figure 1 An example of blood glucose level management according to certain embodiments is illustrated.

[0009] Figure 2 is a block diagram of an example of a glucose control system according to certain embodiments.

[0010] Figure 3 An example of a continuous glucose monitoring (CGM) system according to certain embodiments is illustrated.

[0011] Figure 4A An example of a glucose sensor according to certain embodiments is illustrated.

[0012] Figure 4B Another example of a glucose sensor according to certain embodiments is illustrated.

[0013] Figure 4C An exemplary cross-section of a working electrode of a glucose sensor according to certain embodiments is illustrated.

[0014] Figure 5 A schematic diagram of a glucose sensor according to certain embodiments is shown.

[0015] Figure 6 Included is a flow chart illustrating an example of a method of determining a sensor glucose value using measurement data from a glucose sensor according to certain embodiments.

[0016] Figure 7 An example of a sensor signature generator according to certain embodiments is illustrated.

[0017] Figure 8 Examples of using one or more models to determine sensor glucose values ​​in accordance with certain embodiments are illustrated.

[0018] Fig. 9 An example of determining a transformation model for mapping in vitro characteristics of a CGM sensor to in vivo characteristics of a CGM sensor is illustrated in accordance with certain embodiments.

[0019] Fig.10 Examples of using manufacturing process measurement data to determine in vitro and / or in vivo characteristics of a glucose sensor according to certain embodiments are illustrated.

[0020] Fig.11A An example of a zonal model including a set of regional models for modeling the relationship between sensor glucose (SG) values ​​and sensor measurement data is illustrated in accordance with certain embodiments.

[0021] Fig. 11B Another example of a zonal model including a set of regional models for modeling the relationship between SG values ​​and sensor measurement data is shown in accordance with certain embodiments.

[0022] Fig. 11C An example of a comprehensive model including a plurality of regional model sets for estimating SG values ​​based on sensor measurement data generated by a glucose sensor according to certain embodiments is illustrated.

[0023] Fig. 12A Included is a flow chart illustrating an example of a process for predicting an in vivo characteristic of a glucose sensor according to certain embodiments.

[0024] Fig. 12B Included is a flow chart illustrating an example of a process for estimating SG values ​​using in vivo characteristics of a glucose sensor and one or more SG models according to certain embodiments.

[0025] Fig.13A Included are diagrams illustrating examples of improving sensor performance by using predicted in vivo characteristics in SG value estimation according to certain embodiments.

[0026] Fig. 13B Included are diagrams illustrating examples of improving sensor performance by using predicted in vivo characteristics in SG value estimation according to certain embodiments.

[0027] Fig.14A and Fig. 14BDepicted are examples of integrated glucose sensor devices that can implement some of the examples disclosed herein, according to certain embodiments.

[0028] Fig.15 Depicted are examples of delivery devices that may implement some of the examples disclosed herein.

[0029] Fig.16 is a simplified block diagram of an example of an electronic device that may implement some of the examples disclosed herein.

[0030] The accompanying drawings depict embodiments of the present disclosure for purposes of illustration only. Those skilled in the art will readily recognize from the following description that alternative embodiments of the illustrated structures and methods may be employed without departing from the principles or claimed benefits of the present invention.

[0031] In the drawings, similar components and / or features may have the same reference numeral. In addition, various components of the same type may be distinguished by following the reference numeral with a dash and a second reference numeral that distinguishes the similar components. If only the first reference numeral is used in the specification, the description applies to any of the similar components having the same first reference numeral, without regard to the second reference numeral. DETAILED DESCRIPTION

[0032] The present disclosure as a whole relates to glucose level measurement and / or management. More specifically, the technology disclosed herein relates to estimating blood glucose levels using measurement data from a subcutaneous sensor and in vivo characteristics of the subcutaneous sensor predicted based on manufacturing process measurement data. The technology disclosed herein can be practiced in a variety of ways, such as using a processor-implemented method, a system including one or more processors and one or more processor-readable media, and / or one or more (non-transient) processor-readable media.

[0033] Diabetes is a disease of the patient's glucose regulation system, wherein due to insufficient insulin production and / or insulin resistance, naturally produced insulin in the body may not be enough to control the glucose level in the patient's bloodstream. Therefore, diabetic patients may need to receive insulin from a pump or another delivery device (such as an injection or infusion device (e.g., a syringe)) to control the glucose level in the patient's bloodstream. In order to control glucose levels, the diabetic's therapy routine may generally include a basal insulin dose and a bolus insulin dose. Basal insulin (also referred to as background insulin) may include a continuous or constant release of a small amount of insulin to maintain blood sugar levels at a consistent level during a long period of time. Bolus insulin may be taken specifically before mealtime, at mealtime, or after mealtime or at other times when blood sugar levels may rise rapidly.

[0034] The insulin dose to be delivered can be determined based on, for example, the carbohydrate count of the meal and / or the glucose level of the patient measured using a blood glucose monitor (such as a fingerstick blood glucose measurement device or a continuous glucose monitoring (CGM) sensor). The CGM sensor can use the measurement results (e.g., current, voltage level, resistance, etc.) of the sensor inserted into the subcutaneous layer of the user to estimate the user's blood glucose level based on the glucose level in the interstitial fluid (ISF). In some specific implementations, when the sensor is inserted and exposed to glucose and oxygen diffused from the interstitial fluid, the glucose sensor can generate a signal through a series of electrochemical reactions. For example, a glucose sensor may include a glucose limiting membrane (GLM) that limits the amount of glucose and oxygen delivered to the glucose oxidase (GOx) layer of the working electrode of the glucose sensor to ensure that the reaction is glucose-limited. The GOx layer or another active enzyme layer on the working electrode of the glucose sensor can decompose glucose and oxygen into gluconic acid and hydrogen peroxide. The generated hydrogen peroxide molecules can interact with the working electrode (e.g., in the case of applying a voltage level) to decompose hydrogen peroxide into two hydrogen ions, oxygen and two electrons at the surface of the working electrode. Due to the potential difference, the charge can be forced to move between the electrodes, thereby generating a sensor current signal (Isig) that can be measured by the sensor electronics. Other signals can also be measured, such as the counter voltage (Vcntr, the voltage potential difference between the counter electrode and the working electrode), electrochemical impedance spectroscopy (EIS) at different frequencies, etc. The signals measured using the sensor (including Isig, Vcntr, and EIS) can be processed (e.g., filtered, calibrated, or otherwise transformed) to generate some other signals or parameters, such as a filtered Isig signal, real and imaginary impedance at various frequencies, derivative signals, etc. The raw and / or processed signals measured using the sensor can be used in one or more sensor glucose (SG) models to determine a SG value, which can be an estimate of the user's blood glucose (BG) level.

[0035] The one or more SG models may include one or more trained machine learning models, equations, functions, etc. that may map input parameters (e.g., raw and / or processed sensor measurement data) to SG (or BG) values. For example, the SG model may be represented as:

[0036] SG=f(years,Isig,Vcntr,EIS),

[0037] Wherein the years can be the operating time of the glucose sensor after insertion. In a simplified example, the SG value can be calculated according to the following formula:

[0038] SG=α×years+β×Isig+γ×Vcntr+δ×EIS+Δ,

[0039] Wherein α, β, γ and δ can be coefficients associated with age, Isig, Vcntr and EIS, and △ is an offset value. In some embodiments, at least some of these coefficients may include vectors or tensors, and at least some of age, Isig, Vcntr and EIS may include vectors or tensors.

[0040] Sensor properties associated with glucose sensors, such as age (or time in use or time worn), battery level, calibration accuracy, sensing environment (e.g., oxygen level, temperature, humidity, and pH, etc.), physiological dynamics, production variability, and other properties, may affect enzymatic glucose level measurements in complex ways that may be difficult to model. As a result, the accuracy of many CGM sensors may not be optimal and may exhibit large differences in measurement results. The lower-than-expected performance of CGM sensors may be at least partially attributed to production variability from sensor to sensor, degradation of sensor performance over time, the complexity of modeling the in vivo behavior of CGM sensors (e.g., it may be difficult to model CGM sensors using the same model), etc.

[0041] In order to achieve the desired accuracy and reliability and reduce the effects of noise and other stray signals, CGM sensors may need to be calibrated in vitro and / or in vivo periodically or occasionally to improve the accuracy and / or reliability of the glucose sensor. For example, it may be necessary to calibrate the sensor data using a known good blood glucose value, which is typically obtained via a so-called "fingerstick measurement" using a blood glucose meter that measures blood glucose levels in capillaries. Performing such calibration measurements can increase patient burden and perceived complexity, and may be inconvenient, uncomfortable, or otherwise unpopular with patients. In addition, due to the time required for glucose to diffuse from capillaries to the interstitial space, ISF glucose measurements may lag behind blood glucose measurements, which may require certain signal processing (e.g., filtering) or other techniques to compensate for the physiological lag. Additionally, various factors may cause instantaneous changes in sensor output, which may affect the accuracy of calibration and measurement. Deterioration of sensor performance over time (which may also vary across sensors due to, for example, production differences between sensors) may further complicate these challenges.

[0042] According to certain embodiments disclosed herein, some in vivo characteristics of a glucose sensor (e.g., in vivo sensor sensitivity and intercept) can be predicted before sensor insertion and can be used in one or more SG models to estimate SG values ​​based on other input data (such as Isig, Vcntr, EIS, age, and features derived therefrom Isig, Vcntr, EIS, and / or age). For example, the in vivo characteristics of a glucose sensor can be predicted using in vitro characteristics of a glucose sensor and an in vitro to in vivo transformation model, wherein the in vitro characteristics of a glucose sensor can be predicted using manufacturing process measurement data associated with the glucose sensor or can be measured in vitro. In some embodiments, the in vivo sensor characteristics can be directly predicted using manufacturing process measurement data associated with the glucose sensor. The in vivo characteristics can be predicted as a function of time (e.g., age of the glucose sensor). In this way, at least some production variability and degradation of the glucose sensor can be considered in the SG calculation, and thus the SG value can be more accurately determined based on the original sensor measurement data (e.g., Isig, Vcntr, EIS, and age).

[0043] In one example, manufacturing process measurement data associated with the electrochemical glucose sensor, such as, for example, process parameters, thickness of each layer of the sensor, material composition and / or concentration, certain impedance and / or capacitance measurements, etc., can be obtained during and / or after manufacturing and can be used to determine in vitro characteristics of the electrochemical glucose sensor (such as in vitro sensitivity and intercept that can be used to estimate in vitro SG values ​​based on Isig). The in vivo characteristics of the electrochemical glucose sensor (e.g., in vivo sensitivity and intercept) can then be determined using the in vitro characteristics of the electrochemical glucose sensor, which are determined using the manufacturing process measurement data and one or more transformation models. Both the in vitro and in vivo characteristics of the electrochemical glucose sensor can vary with the age of the electrochemical glucose sensor and can therefore be time-varying (a function of time).

[0044] Based on the in vivo characteristics of the electrochemical glucose sensor (e.g., in vivo sensitivity and in vivo intercept), one or more SG models may be used to determine the SG value, the one or more SG models utilizing the in vivo characteristics as part of the input in addition to the sensor measurement data. The one or more SG models may include, for example, one or more machine learning models, equations, etc. For example, one SG model may be represented as:

[0045] SG=f(age,Isig,Vcntr,EIS,sensitivity 体内 ,intercept 体内 ).

[0046] In a simplified example, the SG value can be determined according to the following formula:

[0047]

[0048] Where α, β, γ, δ, and θ are coefficients that may or may not be time-varying and may include individual values, vectors, or matrices. In some embodiments, an offset value may also be used in the above equation. As a result, the effects of manufacturing process variations and sensor degradation over time on sensor measurement data may be at least partially explained by using the time-varying intrinsic characteristics of the sensor in the SG model. Therefore, the determined SG value may be a more accurate estimate of the blood glucose level.

[0049] In many cases, a single SG model may not accurately estimate blood glucose levels because various sensor properties may affect blood glucose level measurements in complex ways that may be difficult to characterize using a single SG model. According to certain embodiments, different SG models may be used for different input parameter ranges, such as age, Vcntr, or different ranges of other sensor measurement data. Each SG model may be optimized for a corresponding region of the input parameter space and is therefore able to more accurately characterize the sensor's response to glucose in the corresponding region.

[0050] Aspects of the present disclosure and each embodiment can be practiced with one or more types of insulin (e.g., rapid-acting insulin, intermediate-acting insulin and / or slow-acting insulin). Unless the context indicates, terms such as "dose", "insulin", "basal" and "bolus" may not represent a specific type of insulin. For example, rapid-acting insulin can be used for both basal dose and bolus dose. The term "basal" may refer to and include insulin that is intended to be delivered in an amount and frequency corresponding to a healthy body releasing insulin between meals and during sleep. The term "bolus" may refer to and include insulin that is intended to be delivered in an amount and timing corresponding to a healthy body releasing insulin to offset a high glucose level such as caused by dietary consumption. Meal may include food or beverage consumption of any type or amount, including breakfast, lunch, dinner, snacks and beverages, etc.

[0051] In the following description, for the purpose of explanation, specific details are set forth in order to provide a thorough understanding of the examples of the present disclosure. However, it is apparent that various examples can be practiced without these specific details. For example, devices, systems, structures, assemblies, methods and other components may be shown as components in the form of block diagrams, so as not to obscure the examples with unnecessary details. In other examples, well-known devices, processes, systems, structures, operations and techniques may be shown or may not be shown without the necessary details, so as to avoid obscuring the examples. The drawings and descriptions are not intended to be restrictive. The terms and expressions used in the present disclosure are used as descriptive terms rather than restrictive terms, and any equivalents of the features shown and described or parts thereof are not intended to be excluded when using these terms and expressions. The word "example" is used herein to mean "serving as an example, instance or illustration". Any embodiment or design described as "example" herein is not necessarily interpreted as being preferred or advantageous compared to other embodiments or designs.

[0052] Carbohydrates in food intake can be broken down into glucose (sugar) in the stomach and / or intestines, and can be absorbed into the bloodstream at the small intestine and / or large intestine. The bloodstream can transport glucose to the body's capillaries, some of which can diffuse into the interstitial fluid between cells (e.g., fat or muscle cells), which can use glucose as energy. The human endocrine system (working with the nervous system) that directs and regulates the body's functions and activities may secrete chemicals that send messages to tissues and organs. For example, an endocrine gland or organ may release hormones into the bloodstream for transport to target cells with receptors. In one example, insulin can be produced by the β-cells of the pancreas, and can, for example, increase glucose intake, enhance glucose utilization, stop liver glucose production, stimulate glycogen formation in the liver and skeletal muscle, promote protein synthesis, and increase fat storage.

[0053] Insulin acts as a key that unlocks cells and helps glucose move into cells where it can be used for energy. Without insulin, glucose may not be able to enter cells for use as energy and may accumulate in the interstitial fluid and bloodstream. A healthy pancreas continuously releases small amounts of regular human insulin 24 hours a day, including between meals and during sleep. This small amount of insulin matches the glucose released by the liver. A healthy pancreas may also secrete more insulin after eating to match the amount of food consumed.

[0054] The human liver may take in excess glucose from the digestive process and convert the excess glucose into glycogen for storage so that the glucose can be used when needed. For example, when blood sugar levels are low, the α-cells of the pancreas may secrete glucagon. Glucagon may cause the liver to release a stored form of glucose (glycogen) into the bloodstream to help increase glucose levels. The balance between insulin secreted by β-cells and glucagon secreted by α-cells can help maintain normal blood sugar levels, such as within a range of about 80 mg / dL to 120 mg / dL before a meal.

[0055] Due to insufficient insulin production and / or insulin resistance, the naturally produced insulin in the diabetic body may not be enough to control the glucose level in the patient's bloodstream. Therefore, the diabetic may need to receive insulin from a pump or another delivery device (such as an injection or infusion device) to control the glucose level in the patient's bloodstream. In order to control the glucose level, the diabetic's therapy routine may generally include a basal insulin dose and a bolus insulin dose. The dose of insulin to be delivered can be determined based on, for example, the carbohydrate count of a meal and / or the glucose level of the patient measured using a glucose monitor (such as a continuous glucose monitor (CGM)).

[0056] In some embodiments (e.g., in a closed-loop system), the insulin delivery device may communicate with a sensor device (including but not limited to a CGM) or otherwise use the sensor device to perform various measurements for the patient. In one example, the sensor device may include subcutaneous implanted electrodes to simultaneously monitor the patient's response to a meal and insulin introduced by the insulin delivery device. The sensor device and the insulin delivery device may be in a communication network (wired or wireless) with one or more processors and / or patient devices (such as a patient's smartphone equipped with an application or other software) to create an overall system for monitoring the patient's disease state and for promoting its treatment.

[0057] Figure 1An example of a blood glucose level management system 100 according to certain embodiments is illustrated. The blood glucose level management system 100 can be used to monitor and adjust the blood glucose level of a patient 101. In the illustrated example, the blood glucose level management system 100 may include a delivery device 102, a monitoring device 104, a computing device 106, and an optional remote or cloud computing system 108. The delivery device 102, the monitoring device 104, and the computing device 106 may be embodied in various ways, including being disposed in one or more device housings. For example, in some embodiments, all of the devices 102 to 106 may be disposed in a single device housing. In some embodiments, each of the devices 102 to 106 may be disposed in a separate device housing. In some embodiments, two or more of the devices 102 to 106 may be disposed in the same device housing. In some embodiments, a single device 102, 104, or 106 may have two or more parts disposed in two or more housings. For example, the monitoring device 104 may include an on-body portion and a display and control portion that communicates with the on-body portion by wire or wireless means. The delivery device 102 may include an on-body site (eg, including a cannula) and portions including a reservoir, a pump, and a control unit. These and other embodiments and combinations thereof are contemplated to be within the scope of the present disclosure.

[0058] The blood glucose level management system 100 may include multiple communication links, such as communication links 112 to 118. Each of the communication links 112 to 118 may be a wired connection and / or a wireless connection. In an embodiment where the two devices are located in the same housing, the communication link may include, for example, wires, cables, and / or a communication bus located on a printed circuit board, etc. In an embodiment where the two devices are separated from each other in different device housings, the communication link may be a wired connection and / or a wireless connection. The wired connection may include, for example, an Ethernet connection, a universal serial bus (USB) connection, and / or another type of physical connection. The wireless connection may include, for example, a cellular connection, a Wi-Fi connection, A connection, a mesh network connection, and / or another type of connection using a wireless communication protocol. Some embodiments of communication links 112 to 118 may use a direct connection such as The communication links 112 to 118 may be connected and / or may use connections routed through one or more networks or network devices (not shown), such as Ethernet networks, Wi-Fi networks, cellular networks, satellite networks, intranets, extranets, the Internet, and / or other types of networks such as Internet backbone networks. Various combinations of wired connections and / or wireless connections may be used for the communication links 112 to 118.

[0059] The delivery device 102 may be configured to deliver a therapeutic substance to the patient 101. The therapeutic substance may include, for example, insulin, HIV drugs, drugs for treating pulmonary hypertension, iron chelation drugs, analgesics, anti-cancer treatments, medicines, vitamins, hormones, nutritional supplements, dyes, tracing media, saline media, hydration media, etc. The delivery device 102 may be fixed to the patient 101 (e.g., fixed to the body or clothing of the patient 101) or may be at least partially implanted in the body of the patient 101. In some embodiments, the delivery device 102 may include a reservoir, an actuator, a delivery mechanism, and a cannula (not shown). The reservoir may be configured to store a certain amount of therapeutic substances. In some embodiments, the reservoir may be refillable or replaceable. The actuator may be configured to drive the delivery mechanism. In some examples, the actuator may include a motor, such as an electric motor. The delivery mechanism may be configured to move the therapeutic substance from the reservoir through the cannula. In some examples, the delivery mechanism may include a pump and / or a plunger. The cannula may facilitate fluid connection between the reservoir and the body of the patient 101. The cannula and / or needle may facilitate delivery of a therapeutic substance to a tissue layer, vein, interstitial fluid, or body cavity of the patient 101. During operation, the actuator may drive the delivery mechanism in response to a signal (e.g., a command signal), thereby moving the therapeutic substance from the reservoir, through the cannula, and into the body of the patient 101.

[0060] The components of the delivery device 102 described above are provided as examples only. The delivery device 102 may include other components, such as, but not limited to, a power source, a communication transceiver, one or more processors or other computing resources, a memory device, and / or a user interface (e.g., buttons, keys, displays, etc.), among others. In some implementations, the delivery device 102 may host an App (e.g., an insulin calculator) that can calculate the desired amount of therapeutic substance to be delivered to the patient 101. Those skilled in the art will recognize various implementations of the delivery device 102 and components of such implementations. All such implementations and components are contemplated to be within the scope of the present disclosure.

[0061] The monitoring device 104 may be configured to detect a physiological condition (e.g., a glucose concentration level) of the patient 101, and may also be configured to detect other matters. The monitoring device 104 may be secured to the body of the patient 101 (e.g., secured to the skin of the patient 101 via an adhesive) and / or may be at least partially implanted in the body of the patient 101. Depending on the particular location or configuration, the monitoring device 104 may be in contact with biological matter (e.g., interstitial fluid and / or blood) of the patient 101.

[0062] The monitoring device 104 may include one or more sensors (not shown), such as but not limited to electrochemical sensors, electrical sensors and / or optical sensors. As will be appreciated by those skilled in the art, the electrochemical sensor may be configured to generate an electrical signal based on, for example, the potential, conductance, current and / or impedance of the electrical path through the electrode to respond to the interaction or combination of the biomarker with the electrode. The electrode may include a material selected to interact with a specific biomarker such as glucose. The potential, current, conductance and / or impedance may be related to the concentration of the specific biomarker. In one example, the electrochemical sensor may include a glucose limiting membrane (GLM) that limits the amount of glucose and oxygen delivered to the glucose oxidase (GOx) layer of the working electrode of the sensor to ensure that the reaction is glucose-limited. The GOx layer or another active enzyme layer on the working electrode of the sensor may decompose glucose and oxygen into gluconic acid and hydrogen peroxide. When a voltage signal is supplied to the working electrode, the generated peroxide molecules may interact with the working electrode to decompose hydrogen peroxide into two hydrogen ions, oxygen and two electrons at the surface of the working electrode. Charge can be forced to move between electrodes (e.g., between a working electrode and a counter electrode), thereby generating a sensor current signal (Isig) that can be measured by the sensor electronics. Other signals can also be measured, such as a counter voltage (Vcntr, the voltage potential difference between the counter electrode and the working electrode), electrochemical impedance spectroscopy (EIS) at different frequencies, etc. The signals measured using the sensor (including Isig, Vcntr, and EIS) can be processed (e.g., filtered or transformed) to generate some other signals or parameters, such as a filtered Isig signal, real impedance and imaginary impedance at various frequencies, etc. These signals and / or processed parameters can be used in one or more sensor glucose (SG) models (e.g., machine learning models or mathematical models) to determine a SG value, which can be an estimate of the patient's blood glucose (BG) level.

[0063] As will be appreciated by those skilled in the art, the electrical sensor may be configured to respond to the electrical biosignal by generating an electrical signal based on the amplitude, frequency, and / or phase of the electrical biosignal. The electrical biosignal may include a change in the current generated by the sum of the potential differences across the tissues of the patient 101 (such as the nervous system). In some embodiments, the electrical biosignal may include a portion of the potential change (e.g., recorded as an electrocardiogram) generated by the heart of the patient 101 over time that may indicate the patient 101's 101 glucose level. The optical sensor may be configured to respond to the interaction or binding of the biomarker with the substrate, for example, by generating an electrical signal based on a change in the brightness of the substrate. In one example, the substrate may include a material selected to fluoresce in response to contact with a selected biomarker (such as glucose). The fluorescence may be proportional to the concentration of the selected biomarker.

[0064] In some embodiments, the monitoring device 104 may include other types of sensors that may be worn, carried, or coupled to the patient 101 to measure activities of the patient 101 that may affect the glucose level or glycemic response of the patient 101. For example, the sensor may include an accelerometer configured to detect acceleration of the patient 101 or a portion of the patient 101 (such as a person's hand or foot), whose position changes may be associated with the activities of the patient 101. For example, the acceleration or movement (or lack of acceleration or movement) of the body part of the patient 101 may indicate exercise, sleep, or food / drink consumption activities of the patient 101, which may affect the glycemic response of the patient 101. In some embodiments, the sensor may measure heart rate and / or body temperature, which may indicate the amount of physical exercise experienced by the patient 101. In some embodiments, the sensor may include a global positioning system (GPS) receiver that may detect GPS signals to determine the location of the patient 101.

[0065] The above sensors are provided as examples only. Other sensors or other types of sensors for monitoring physiological conditions, activities and / or positions, etc. will be recognized by those skilled in the art and can be envisioned within the scope of the present disclosure. For any sensor, the signal provided by the sensor may be referred to as a "sensor signal" herein. As used herein, the term "sensed data" may mean and include information represented by a sensor signal or by a preprocessed sensor signal. In some embodiments, the sensed data may include a glucose level in the patient 101, an acceleration of a part of the patient 101, a heart rate of the patient 101, a temperature of the patient 101, and / or a geographic location (e.g., GPS location) of the patient 101, etc. The monitoring device 104 may communicate the sensed data to the delivery device 102 via the communication link 112 and / or communicate the sensed data to the computing device 106 via the communication link 114. The use of the sensed data by the delivery device 102 and / or the computing device 106 is described in more detail below.

[0066] In some embodiments, the monitoring device 104 may include components and / or circuits configured to pre-process sensor signals. Pre-processing may include, for example, amplification, filtering, attenuation, scaling, isolation, normalization, transformation, sampling and / or analog-to-digital conversion, etc. In some embodiments, the monitoring device 104 may host an App for processing sensor signals. In some embodiments, the monitoring device 104 may include a wired or wireless transceiver for sending sensor signals or receiving commands or instructions as described above. Those skilled in the art will recognize that each specific implementation of such pre-processing includes, but is not limited to, specific implementation using a processor, a controller, an integrated circuit, an application specific integrated circuit (ASIC), hardware, firmware, a programmable logic device and / or a machine executable instruction, etc. The types of pre-processing and their specific implementation are provided only as examples. It is conceivable that other types of pre-processing and specific implementation are within the scope of the present disclosure. In some embodiments, the monitoring device 104 may not perform pre-processing.

[0067] The computing device 106 may provide processing capabilities and may be implemented in a variety of ways. In some embodiments, the computing device 106 may be a consumer device such as a smartphone, a computerized wearable device (e.g., a smartwatch), a tablet computer, a laptop computer, or a desktop computer, etc., or may be a dedicated device (e.g., a portable control device) provided by, for example, a manufacturer of the delivery device 102. In some embodiments, the computing device 106 may be a processing circuit that may be integrated with another device such as the delivery device 102. In some embodiments, the computing device 106 may be secured to the patient 101 (e.g., secured to the body or clothing of the patient 101), may be at least partially implanted in the body of the patient 101, and / or may be held by the patient 101.

[0068] For each of the embodiments of computing device 106, computing device 106 may include various types of logic circuits, including but not limited to microprocessors, controllers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), central processing units (CPUs), graphics processing units (GPUs), programmable logic devices, memories (e.g., random access memories, volatile memories, non-volatile memories, etc.), or other discrete or integrated logic circuits, and combinations of such components. The term "processing circuitry" may generally refer to any of the foregoing logic circuits, alone or in combination with other logic circuits, or any other circuitry for performing computations.

[0069] Some aspects of the delivery device 102, the monitoring device 104, and the computing device 106 have been described above. One or more of the devices 102 to 106 may include a user interface (not shown) that presents information to the patient 101 and / or receives information from the patient 101. The user interface may include a graphical user interface (GUI), a display device, a keyboard, a touch screen, a speaker, a microphone, a vibration motor, a button, a switch, and / or other types of user interfaces. Those skilled in the art will recognize that various types of user interfaces can be used, and it is conceivable that all such user interfaces are within the scope of the present disclosure. For example, in the case where the computing device 106 is a consumer device such as a smart phone, a tablet computer, a laptop computer, etc., the user interface will include a display device, a physical and / or virtual keyboard, and / or an audio speaker, etc. provided by such a consumer device. In some embodiments, the user interface may notify the patient 101 of sensed data (e.g., glucose level) and / or insulin delivery data (e.g., historical, current, or future insulin delivery rate) and may present an alarm to the patient 101. In some embodiments, the user interface may receive input from the patient 101, which may include, for example, requested insulin delivery setting changes and / or meal instructions, etc. The above description and embodiments of user interfaces are provided merely as examples, and other types and other uses of user interfaces are contemplated as being within the scope of the present disclosure.

[0070] In one specific example, the communication between devices 102 to 106 and the collaboration between devices 102 to 106 may be used for insulin delivery. Figure 1As depicted and described above, devices 102 to 106 may communicate with each other via communication links 112 to 116. In some embodiments, computing device 106 may control the operation of delivery device 102 and / or monitoring device 104. For example, computing device 106 may generate one or more signals (e.g., command signals) that cause delivery device 102 to deliver insulin to patient 101, for example, at a basal dose and / or a bolus dose. In some embodiments, computing device 106 may receive data associated with insulin delivery (e.g., insulin delivery data) from delivery device 102 and / or sensed data (e.g., glucose levels) from monitoring device 104, and may perform calculations based on the insulin delivery data, sensed data, and / or other data to control delivery device 102. Insulin delivery data may include, but is not limited to, the type of insulin delivered, historical insulin delivery rate and / or amount, current insulin delivery rate and / or amount, insulin delivery time, and / or user input that affects insulin delivery. As will be appreciated by those skilled in the art, in the closed-loop mode of operation, computing device 106 may communicate dosage commands to delivery device 102 based on, for example, the difference between the current glucose level in the body of patient 101 (e.g., received from monitoring device 104) and the target glucose level (e.g., determined by computing device 106 or set on delivery device 102). The dosage commands may indicate the amount of insulin to be delivered and / or the rate (or time) of insulin delivery, and may adjust the current glucose level toward the target glucose level.

[0071] The remote / cloud computing system 108 can be any proprietary remote / cloud computing system or commercial cloud computing system including one or more server computing devices. When the computing resources of the client computing device (e.g., computing device 106) are insufficient, the remote / cloud computing system 108 can provide alternative or additional computing resources as needed. The computing device 106 and the remote / cloud computing system 108 can communicate with each other via a communication link 118, which can traverse one or more communication networks (not shown). The communication network may include, for example, an Ethernet network, a Wi-Fi network, a cellular network, a satellite network, an intranet, an extranet, the Internet, and / or an Internet backbone network. Those skilled in the art will recognize the specific implementation of the remote / cloud computing system 108 and how to interact with such a system through various types of networks. For example, the remote / cloud computing system 108 may include a processing circuit array and can execute machine-readable instructions. It is conceivable that such specific implementations, interfaces, and networks are within the scope of the present disclosure.

[0072] In some embodiments, the remote / cloud computing system 108 can make a therapeutic determination (e.g., an amount of insulin or an adjusted amount of insulin) and can communicate the therapy to the delivery device 102 via the computing device 106. In some embodiments, the computing device 106 can make a therapeutic determination and communicate the therapeutic determination to the delivery device 102. In some embodiments, the monitoring device 104 can make a therapeutic determination and communicate the therapeutic determination to the delivery device 102 directly or through an intermediary such as the computing device 106.

[0073] Figure 2 2 is a block diagram of an example of a glucose control system 200 according to certain embodiments. In the illustrated example, the glucose control system 200 may include a glucose sensor subsystem 210, a controller 220, an insulin delivery subsystem 230, a glucose delivery subsystem 240, and a glucagon delivery subsystem 250. The glucose sensor subsystem 210 may generate sensor glucose (SG) signals (e.g., SG levels) (these SG signals may be estimates of blood glucose levels in the body 260), and may provide the SG signals to the controller 220. The controller 220 may receive the SG signals and generate commands to the insulin delivery subsystem 230, and in some implementations, to the glucose delivery subsystem 240 and / or the glucagon delivery subsystem 250. The insulin delivery subsystem 230 may receive commands from the controller 220 and deliver insulin to the body 260 in accordance with the commands. In some embodiments, the glucose delivery subsystem 240 may receive commands from the controller 220 and provide glucose to the body 260 in accordance with the commands. In some embodiments, glucagon delivery subsystem 250 may receive commands from controller 220 and deliver glucagon into body 260 in accordance with the commands.

[0074] In some implementations, the glucose sensor subsystem 210 may include a glucose sensor, a sensor electronics configured to generate a SG signal, a sensor communication system configured to transmit the SG signal to the controller 220, and a housing for the sensor electronics and the sensor communication system. The glucose sensor may measure blood glucose levels, for example, directly from the bloodstream or indirectly via interstitial fluid using a subcutaneous sensor, as described in more detail below.

[0075] The controller 220 may include electrical components and software that generate commands for the insulin delivery subsystem 230, the glucose delivery subsystem 240, and / or the glucagon delivery subsystem 250. The controller 220 may include a controller communication system to receive sensor signals and provide commands to the insulin delivery subsystem 230, the glucose delivery subsystem 240, and / or the glucagon delivery subsystem 250. In some specific implementations, the controller 220 may implement a glucose calculator. In some specific implementations, the controller 220 may include a user interface and / or an operator interface (not shown), which includes a data input device and / or a data output device. Such a data output device may, for example, generate a signal to initiate an alarm and / or include a display or printer to display the status of the controller 220 and / or the patient's vital signs. Such a data input device may include a dial, button, pointing device, manual switch, alphanumeric key, touch-sensitive display, a combination thereof, etc. for receiving user input and / or operator input. For example, such a data input device may be used to schedule and / or initiate an insulin bolus injection for a meal. It should be understood, however, that these are merely examples of input and output devices that may be part of an operator and / or user interface and that claimed subject matter is not limited in these respects.

[0076] The insulin delivery subsystem 230 may include, for example, an infusion device and / or an infusion tube to infuse insulin into the body 260. Similarly, the glucose delivery subsystem 240 may include, for example, an infusion device and / or an infusion tube to infuse glucose into the body 260. Similarly, the glucagon delivery subsystem 250 may include, for example, an infusion device and / or an infusion tube to infuse glucagon into the body 260. In some embodiments, a shared delivery system and / or infusion tube may be used to infuse insulin, glucagon, and / or glucose into the body 260. In some embodiments, an intravenous system for providing fluids to a patient (e.g., in a hospital or other medical environment) may be used to infuse insulin, glucagon, and / or glucose. However, it should be understood that certain exemplary embodiments may include the insulin delivery subsystem 230 without the glucagon delivery subsystem 250 and / or without the glucose delivery subsystem 240. In some embodiments, each of the insulin delivery subsystem 230, the glucose delivery subsystem 240, and the glucagon delivery subsystem 250 may include an infusion electrical component that activates an infusion motor based on commands from the controller 220, an infusion communication system that receives commands from the controller 220, and a delivery subsystem housing.

[0077] In some embodiments, the controller 220 may be housed in a delivery subsystem housing, and the infusion communication system may include electrical traces or wires that carry commands from the controller 220 to the delivery subsystem. In some embodiments, the controller 220 may be housed in a sensor system housing, and the sensor communication system may include electrical traces or wires that carry sensor signals from sensor electrical components to controller electrical components. In some embodiments, the controller 220 may have its own housing or may be included in an auxiliary device. In some embodiments, the controller 220 may be co-located with the delivery subsystem and the sensor system in a single housing. In some embodiments, the sensors, controllers, and / or infusion communication systems may utilize cables; wires; fiber optic lines; RF, IR, or ultrasonic transmitters and receivers; combinations thereof; and / or the like in place of electrical traces, to name just a few examples.

[0078] In some embodiments, glucose control system 200 may also include a meal intake monitoring subsystem 215. Meal monitoring subsystem 215 may be used to record user food intake, or may automatically detect user food intake. For example, in some specific implementations, a user may input an estimated amount of carbohydrates in food items and / or meals during a meal. In some specific implementations, meal intake monitoring subsystem 215 may include sensors (e.g., cameras or accelerometers) that may automatically detect an estimated amount of carbohydrates in food items and / or meals during meal events, meals. The estimated amount of carbohydrates in the meal may be transmitted to controller 220, which may determine the appropriate amount of meal push and generate an order for insulin delivery subsystem 230 to deliver meal push. In some specific implementations, meal intake monitoring subsystem 215 may not be used in glucose control system 200, and may determine the dosage of meal push based on the glucose level measured.

[0079] Figure 3 310 is a perspective view of an example of a CGM system 300 according to certain embodiments. In the illustrated example, the CGM system 300 may include a subcutaneous sensor kit 302 that is provided for subcutaneously placing an active portion of a flexible sensor 310 or the like at a selected site within a user's body. The subcutaneous or transcutaneous portion of the sensor kit 302 includes a hollow slotted insertion needle 326 having a sharp tip 312 and a cannula 322. The sensor kit 302 may facilitate accurate placement of the flexible sensor 310 into the user's body. The glucose sensing portion 318 of the flexible sensor 310 is located inside the cannula 322. The glucose sensor portion 318 includes one or more glucose sensor electrodes 316 that may be exposed to a user's bodily fluid 390, for example, through a window 314 formed in the cannula 322. The sensor electrodes 316 may include, for example, a counter electrode, a reference electrode, and one or more working electrodes.

[0080] The proximal portion of the flexible sensor 310 may be mounted in a mounting base 305 suitable for placement on the skin of a user. In some embodiments, the mounting base 305 may be a pad having a lower surface coated with a suitable pressure-sensitive adhesive layer 334, wherein a release paper tape 336 is typically provided to cover and protect the adhesive layer 334 until the sensor kit 302 is ready for use. The mounting base 305 may include an upper layer 330 and a lower layer 331, wherein a connecting portion 328 of the flexible sensor 310 is sandwiched between the upper layer 330 and the lower layer 331. The connecting portion 328 may include a front section coupled to the glucose sensing portion 318 of the flexible sensor 310, which may be folded at an angle to extend downward through a hole 320 formed in the lower layer 331 of the mounting base 305. Optionally, the adhesive layer 334 (or another portion of the device that contacts the tissue in the body) may include an anti-inflammatory agent for reducing an inflammatory response and / or an antimicrobial agent for reducing the chance of infection. The insertion needle 326 may be adapted to be received by a snug fit through a needle port 324 formed in an upper layer 330 of the mounting base 305 and through a lower hole 320 in a lower layer 331 of the mounting base 305. In some embodiments, the insertion needle 326 may be withdrawn after insertion to leave the glucose sensing portion 318 (including the glucose sensor electrode 316) and / or the cannula 322 in place at a selected insertion site.

[0081] The flexible sensor 310 of the subcutaneous sensor kit 302 can be connected to the sensor electronics 304, which can be referred to as a transmitter. For example, the mounting base 305 can be designed so that the glucose sensing portion 318 can be coupled to the connecting portion 328, which terminates at a conductive contact pad, etc. The connecting portion 328 and the contact pad can be adapted to be electrically connected to the sensor electronics 304 to determine the user's glucose level in response to a signal from the sensor electrode 316. The connecting portion 328 can be electrically connected to the sensor electronics 304 through a connector block 338. The sensor electronics 304 can include, for example, a housing 350 supporting a printed circuit board 344, a battery 354, and an antenna 346. In some embodiments, the housing 350 can include an upper housing 356 and a lower housing 352, which are sealed by, for example, ultrasonic welding to form a waterproof (or water-resistant) seal, thereby allowing cleaning by immersion (or wiping) with water, detergent, alcohol, etc. In some embodiments, the upper housing 356 and the lower housing 352 can be formed of medical grade plastic. In alternative embodiments, the upper housing 356 and the lower housing 352 may be connected together and bonded together by other methods such as snap fit, sealing rings, RTV (silicone sealant), etc., or formed of other materials such as metals, composites, ceramics, etc. In other embodiments, the components may be encapsulated in an epoxy or other moldable material that is compatible with the electronics and reasonably moisture resistant. In some embodiments, the sensor electronics 304 may be connected to the flexible sensor 310 via the connector block 338 and the connector 340, and may be mounted to the mounting base 305. In some embodiments, the sensor electronics 304 may be connected to the flexible sensor 310 via the connector block 338, the connector 340, and the cable 342. In some embodiments, the bottom surface of the lower housing 352 may be coated with a suitable pressure sensitive adhesive layer 358, wherein a release tape 348 is typically provided to cover and protect the adhesive layer 358 until the sensor electronics 304 are ready for use.

[0082] The battery 354 may include a rechargeable battery or a non-rechargeable battery. In some embodiments, the battery 354 may include three silver oxide battery cells connected in series. In other examples, different battery chemistries may be used, such as lithium-based chemistries, alkaline batteries, nickel metal hydride, etc., and different numbers of batteries may be used. The battery 354 may provide power to the sensor assembly 302 via the sensor electronics 304 through, for example, the connector block 338, the connector 340, and / or the cable 342.

[0083] The sensor electronics 304 may include a processor, a transceiver unit, a signal processing / conditioning circuit, and a memory device on a printed circuit board 344. The processor may be configured to receive a current and / or voltage signal from a sensor electrode 316 of the flexible sensor 310. The sensor electrode 316 may generate a sensor signal indicating the concentration of an analyte (e.g., glucose) being measured. In one example, the sensor signal may include a current measured at a working electrode, which may include glucose oxidase (GOx) to catalyze a reaction with glucose. The sensor signal may also include a voltage signal measured at a counter electrode of the sensor electrode 316 or between the working electrode and the counter electrode (or reference electrode). In some examples, other electrical parameters such as capacitance, resistance, impedance, etc. may also be measured using the sensor electrode 316 inserted into the user's body.

[0084] Examples of impedance parameters measured using sensor electrodes 316 may include electrochemical impedance spectroscopy (EIS) values. EIS can provide additional information in the form of sensor impedance and impedance-related parameters at different frequencies. In addition, for certain frequency ranges, impedance and / or impedance-related data may be substantially independent of glucose. This glucose independence can enable the use of a variety of EIS-based markers or indicators to produce robust, highly reliable sensor glucose values ​​(e.g., by fusion methods), and also substantially independent of glucose-related current signals to assess the condition, health, age, and efficiency of individual electrodes and overall sensors. In one example, analysis of glucose-independent impedance data can provide information about the efficiency of a glucose sensor, i.e., the speed at which the glucose sensor is hydrated and ready for data acquisition. In addition, glucose-independent impedance data (e.g., using the value of 1kHz actual impedance) can provide information about potential occlusions that may exist on the sensor membrane surface, where occlusions may temporarily prevent glucose from entering the sensor, and thus cause a signal drop. In addition, for example, using the values ​​of phase angles and / or imaginary impedances at 1kHz and higher frequencies, glucose-independent impedance data can provide information about sensor sensitivity loss during long-term wear.

[0085] The processor of the sensor electronic device 304 may receive the sensor signal and calibrate the sensor signal using a calibration function, which may be determined, for example, using a reference value. In some embodiments, the parameters of the calibration function (e.g., calibration factor or sensor sensitivity) may be determined (e.g., using calibration data) or may be predetermined and stored in a memory for use by the processor. For example, in some embodiments, the processor may implement or otherwise execute a calibration application module that calculates or otherwise determines calibration parameters based on measured values ​​and reference values ​​(e.g., using a blood glucose value measured by, for example, a finger-stick glucometer). Based on the sensor signal and the determined calibration function, the processor may estimate the blood glucose level of the user. In some embodiments, the processor may store glucose measurements in a memory. The sensor measurement results may be displayed on a display of the sensor electronic device 304 or may be transmitted to a receiver 306 for display.

[0086] The memory of the sensor electronics 304 can be any type of memory device and can be configured to store raw sensor signals from the flexible sensor 310 and / or glucose measurements (e.g., SG values) generated by the processor, calibration parameters for determining glucose measurements from the sensor signals, or other data used and / or generated by the processor. The memory can also store software and / or firmware that can be executed by the processor. In some embodiments, the memory can also include configuration information for configuring the operation of the sensor electronics 304.

[0087] The signal processing / conditioning circuitry of the sensor electronics 304 may include one or more amplifiers, filters, current-to-voltage converters, integrators, analog-to-digital converters, potentiostats, etc. The signal processing / conditioning circuitry may filter the signal, apply a constant reference voltage level, amplify the current or voltage signal, sample the analog current or voltage signal and convert the analog current or voltage signal to a digital data value, etc.

[0088] The transceiver unit of the sensor electronics 304 may include a wireless transceiver that can transmit the glucose measurements determined by the processor and stored in the memory to the receiver 306 using a suitable wireless communication protocol such as Bluetooth (e.g., Bluetooth Low Energy (BLE)), Wi-Fi, WiMax, etc. For example, the transceiver unit can transmit a radio signal indicating the glucose level at a regular time period (e.g., once every 5 minutes) to provide a real-time sensor glucose (SG) value. The transceiver unit can also receive data from the receiver 306, such as configuration data and calibration data (e.g., reference glucose values). In some embodiments, the transceiver unit of the sensor electronics 304 can transmit data to the receiver 306 via a cable or wire.

[0089] The receiver 306 may include a monitor 335 for monitoring physiological characteristic readings and / or generating warning signals, and a display 332 for displaying physiological characteristic readings and / or warning signals. For example, the SG value / graph may be displayed on the display 332 of the receiver 306 so that the user can monitor blood glucose levels and administer insulin using an insulin pump. In various embodiments, the receiver 306 may be implemented in a desktop computer, a pager, a television including communication capabilities, a notebook computer, a server, a network computer, a personal digital assistant (PDA), a smart phone, an infusion pump including a display, a glucose sensor including a display, a combination infusion pump / glucose sensor, etc.

[0090] Figure 4A An example of a glucose sensor 400 according to certain embodiments is illustrated. The glucose sensor 400 may be an example of the flexible sensor 310 described above. In the illustrated example, the glucose sensor 400 may include a sensing portion 410, which includes one or more electrodes, such as a first working electrode (WE1) 412, a reference electrode (RE) 414, a second working electrode (WE2) 416, and a counter electrode (CE) 418, wherein these electrodes may be electrically isolated by an isolation layer. The sensing portion 410 may be an in vivo portion (also referred to as a distal end), which may be inserted into the subcutaneous layer of a user using a hollow needle (such as an insertion needle 326). The glucose sensor 400 may include a bending region 420, which may be bent, for example, up to about 90° before or after insertion. The glucose sensor 400 may also include a contact pad region 430, which may be an out-of-body portion of the glucose sensor 400. As illustrated, the contact pad region 430 may include one or more contact pads electrically connected to one or more electrodes in the sensing portion 410 by conductive traces (not shown). The contact pads may include, for example, a contact pad 438 connected to the counter electrode 418, a contact pad 434 connected to the reference electrode 414, and two contact pads 432 and 436 connected to the first working electrode 412 and the second working electrode 416, respectively. The contact pads in the contact pad region 430 of the glucose sensor 400 may include, for example, a metal layer and may be connected to one or more contact pads on a printed circuit board of the sensor electronics. In the illustrated example, the contact pad region 430 of the glucose sensor 400 may also include a pair of guide holes 440 that may cooperate with a pair of guide pins on the printed circuit board to align the contact pads on the contact pad region 430 with the contact pads on the printed circuit board so that the voltage required for glucose sensing can be applied and a sensor current proportional to the interstitial glucose concentration can be measured by the sensor electronics on the printed circuit board.

[0091] Figure 4BAnother example of a glucose sensor 402 according to certain embodiments is illustrated. The glucose sensor 402 can be another example of the flexible sensor 310 described above. In the illustrated example, the glucose sensor 402 may include a sensing portion 450, which includes one or more electrodes, such as a reference electrode (RE) 452, one or more working electrodes (WE) 454, and one or more counter electrodes (CE) 456, wherein these electrodes may be electrically isolated by an isolation layer. The sensing portion 450 may be an in vivo portion that can be inserted into the subcutaneous layer of a user using a hollow needle (such as the insertion needle 326). The glucose sensor 402 may include a bending region 460 that can be bent, for example, up to about 90° before or after insertion. The glucose sensor 402 may also include a contact pad region 470, which may be an out-of-body portion of the glucose sensor 402. As illustrated, the contact pad region 470 may include one or more contact pads electrically connected to one or more electrodes in the sensing portion 450 by conductive traces (not shown). The contact pads may include, for example, a contact pad 472 connected to the reference electrode 452, a contact pad 474 connected to the working electrode 454, and a contact pad 476 connected to the counter electrode 456. The contact pads in the contact pad region 470 of the glucose sensor 402 may include, for example, a metal layer and may be connected to one or more contact pads on a printed circuit board of the sensor electronics. In the illustrated example, the contact pad region 470 of the glucose sensor 402 may also include a pair of guide holes 478 that may cooperate with a pair of guide pins on the printed circuit board to align the contact pads on the contact pad region 470 with the contact pads on the printed circuit board so that the voltage required for glucose sensing can be applied and a sensor current proportional to the interstitial glucose concentration can be measured by the sensor electronics on the printed circuit board.

[0092] Figure 4CThe cross section of the example of the working electrode 404 of the glucose sensor according to certain embodiments is illustrated. As illustrated, the working electrode 404 may include a substrate or base layer 480 (e.g., comprising polyimide), which provides the basis and rigidity of the flexible substrate. An overlying electroplated metal layer 482 (e.g., comprising chromium and / or gold) may be deposited on the base layer 480. The chromium layer may promote adhesion between the polyimide and the gold layer, which enables the sensor to transmit charge when a bias is applied. An electroplated layer 484 (e.g., comprising platinum) may be formed on the metal layer 482, such as formed in a groove formed in an insulating layer 486 (e.g., comprising polyimide). The insulating layer 486 may help improve sensor integrity by providing electrical insulation between the working electrode, the counter electrode, and the reference electrode, and may also serve as a support barrier for the electrode and the sensor flexible chemical substance. The electroplated layer 484 may be an active electrode for analyte (glucose) sensing, wherein glucose may be sensed at the working electrode by generating charge through oxidation of hydrogen peroxide. The working electrode is also responsible for maintaining the bias potential V applied by the sensor electronics. 设定 For example, a glucose oxidase (GOx) layer 488 is formed on the insulating layer 486 by depositing a glucose oxidase solution in a groove formed in the insulating layer 486 (e.g., via slot coating). A human serum albumin (HSA) layer 490 is formed on the glucose oxidase layer 488. An adhesion promoter layer 492 may be used on the HSA layer 490 to attach a glucose limiting membrane (GLM) layer 494 to the working electrode 404. Note that Figure 4C A simplified representation of one example of a working electrode is depicted, and actual embodiments may include any number of additional layers and / or alternative layers (e.g., high density amine (HDA) layers, etc.). Thus, the subject matter described herein is not intended to be limited to Figure 4C The reference electrode or counter electrode may have a structure similar to Figure 4C The structure of the structure shown in , but some modifications may be made. For example, in some embodiments, the reference electrode may include an Ag layer instead of a platinum layer, and may not include a glucose oxidase layer.

[0093] The glucose oxidase in the GOx layer 488 may include an active enzyme of the sensor. In the presence of oxygen, GOx may decompose glucose into gluconic acid and hydrogen peroxide. The HSA layer 490 may decompose hydrogen peroxide into water. The adhesion promoter layer 492 may include, for example, 3-aminopropyltriethoxysilane (APTES), which is a compound that provides a better bonding surface between the HSA layer 490 and the GLM layer 494. The GLM layer 494 may limit the amount of glucose and oxygen delivered to the GOx layer 488 of the working electrode 404 to ensure that the reaction is glucose-limited. The GOx layer 488 or another active enzyme layer on the working electrode 404 may decompose glucose and oxygen into gluconic acid and hydrogen peroxide. The generated peroxide molecules may interact with the working electrode 404 (e.g., biased at about 0.2V to about 0.7V) to decompose hydrogen peroxide into two hydrogen ions, oxygen, and two electrons at the surface of the working electrode 404. Charge can be forced to move between the electrodes, thereby generating a sensor current signal (Isig) that can be measured by the sensor electronics. For example, when the GOx enzyme is used as a catalyst in a glucose sensor, if oxygen is present near the enzyme and the sensor electrodes, current can flow from the counter electrode to the working electrode. Other signals can also be measured, such as the counter voltage (Vcntr, e.g., the voltage potential difference between the counter electrode and the working electrode), electrochemical impedance spectroscopy (EIS) signals at different frequencies, etc.

[0094] In some embodiments, the measurement of the parameters of the electrodes of the above-mentioned electrochemical glucose sensor can be performed during and / or after the manufacturing process. For example, the imaginary impedance of the working electrode (and the counter electrode) can be measured after the platinum electroplating process of the electroplating layer 484. The GOx solution activity measurement can be performed during or after the GOx solution preparation process before deposition, and the thickness of the GOx layer 488 can be measured after the slot coating and selective patterning process on the working electrode 404. The HSA concentration can be measured during or after the solution preparation process before coating the HSA layer 490, and the thickness of the GLM layer 494 can be measured after the formation of the GLM layer 494. In some embodiments, these measurements can be performed using sacrificial or monitoring instances of glucose sensors manufactured on wafers. In some embodiments, additional manufacturing process measurements (such as surface roughness or other morphology measurements) can be performed on the electrodes during or after the manufacturing process (e.g., via interferometry).

[0095] Figure 5 Schematic diagram of an example of a glucose sensor 500 according to certain embodiments is shown. The glucose sensor 500 may include a potentiostat 530 electrically connected to electrodes of the glucose sensor 500. As illustrated, the potentiostat 530 may include an operational amplifier (op amp) 532 (or other servo control device) including two inputs V 设定 and V 测量. V 测量 V may be the measured voltage between the reference electrode 512 and the working electrode 510 of the sensor flex circuit as described above. 设定 The output of the operational amplifier 532 may be connected to the counter electrode 514 to apply a voltage on the counter electrode 514. The Op amp 532 may utilize feedback through the sensor electrode to attempt to maintain the desired voltage between the reference electrode 512 and the working electrode 510 by adjusting the voltage at the counter electrode 514. The counter electrode 514 may balance the chemical reaction occurring at the working electrode 510. As described above, due to the electrochemical reaction between glucose and the sensor chemical substance of the working electrode 510, a current 520 may flow between the counter electrode 514 and the working electrode 510. The current may indicate the level of glucose in the interstitial fluid of the user, and may be measured by the ammeter 540 as a sensor current (Isig), or may be output to the processing circuit of the sensor electronics for measurement. The sensor electronics of the glucose sensor may also be used to measure other signals, such as the counter voltage (Vcntr, e.g., the voltage potential difference between the counter electrode and the working electrode), electrochemical impedance spectroscopy (EIS) values ​​at different frequencies, and the like. Based on these measured raw sensor signals, one or more models may be used to determine the user's blood glucose level.

[0096] Figure 6 Included is a flowchart 600 illustrating an example of a method for determining a sensor glucose value using input data from a glucose sensor according to certain embodiments. Figure 6In the illustrated example, the method may include sensor feature generation at box 610, optional blood glucose calibration at box 620, sensor glucose modeling at box 630, and optional conditional blanking and termination at box 640. In some embodiments, the sensor feature generation at box 610 may use the raw gap current signal, electrochemical impedance spectroscopy signal, and reverse voltage signal to extract input features used by a downstream SG model (e.g., a machine learning model). The optional blood glucose calibration at box 620 may include adjusting the input sensor features from box 610 based on calibration parameters determined using a reference blood glucose value. The sensor glucose modeling at box 630 may include applying machine learning techniques to the input features to convert the input features into sensor glucose values. The optional conditional blanking and termination at box 640 may include applying various tests and logic to determine, for example, when to stop displaying the sensor output signal or terminate the sensor to reduce the probability of displaying noise or erroneous information to a user or receiving device. In some embodiments, terminating the sensor may include stopping sending sensor data from the sensor device. In some embodiments, input data (e.g., Isig, Vcntr, EIS, and / or BG values ​​of gap measurements) may pass through blocks 610 to 640 and be transformed into SG values. Table 1 below shows examples of inputs and outputs for each of the four blocks of flowchart 600. In some embodiments, the process of flowchart 600 may be implemented using one or more machine learning models.

[0097] Table 1. Information Transmission in Flowchart 600

[0098] information Box 610 Box 620 Box 630 Box 640 Isig, Vcntr, EIS, BG enter not applicable not applicable enter Calibration-free fundamental and derived sensor characterization Output enter enter enter Fundamental and derived sensor characteristics requiring BG calibration not applicable Output enter enter Initial estimate of SG value not applicable not applicable Output enter Final estimate of SG value not applicable not applicable not applicable Output

[0099] Figure 7 An example of a sensor feature generator 700 according to certain embodiments is illustrated. The sensor feature generator 700 may be used to perform Figure 6The operation of sensor signature generation at block 610 of the sensor signature generator 700. In the illustrated example, the input signal 710 to the sensor signature generator 700 may include an Isig signal 712 (e.g., at intervals of about 1 minute), a Vcntr signal 714 (e.g., at intervals of about 5 minutes), and an EIS signal 716 (e.g., at intervals of about 15 minutes to 30 minutes). At block 720, the Isig signal 712 may be collected over, for example, 5 minutes or more, and the collected Isig signal 712 may be used to generate a filtered Isig signal 722 (within a 5 minute time window). For example, the Isig signal 712 for the most recent 8 minutes may be filtered using, for example, a seventh-order finite impulse response (FIR) filter to produce a filtered Isig signal 722. In some specific implementations, for example, if the value of the Isig signal 722 for the last 8 minutes is greater than twice the average value of the Isig signal 712 for 8 minutes or less than half the average value of the Isig signal 712 for 8 minutes, any calculated invalid filtered Isig signal 712 may be discarded. In some embodiments, artifact detection may be performed to identify large and small drops in the Isig signal 712 or the filtered Isig signal 722. The EIS signal 716 may include measurements made using signals of various frequencies (e.g., a frequency set between about 0.1 Hz and about 8 kHz). The EIS signal 716 may be verified to generate a verified EIS signal 730. In some embodiments, the verified EIS signal 730 may be converted to a 5-minute sampling using a zero-order hold. In some embodiments, the frequency of the measurement signal may be changed according to the hardware design. For example, Vcntr may be measured once every 1 minute, while EIS may be measured more frequently, depending on factors such as battery life and memory size limitations.

[0100] The filtered Isig signal 722, the verified EIS signal 730, and the Vcntr signal 714 may be used to generate a 5-minute feature 740, wherein the features for each 5-minute time period may be included in the corresponding data packet. The derived features 750 may be added to the data packet based on the 5-minute feature 740 in the previous data packet and the previous 5-minute feature 740 in the previous data packet. The final features 760 including the 5-minute feature 740 and the derived features 750 may include, for example, the filtered Isig signal 722, the previous filtered Isig signal, the cumulative average of the Isig signal, the moving average of the Isig signal, the filtered Isig signal, the Vcntr signal 714 of the current data packet, the Vcntr signal of the previous data packet, the age of the sensor, the real and imaginary components of the EIS signal at various frequencies, the changes in the real and imaginary components of the EIS signal at various frequencies, etc. In some embodiments, the sensor feature generator 700 can process the Isig signal, the Vcntr signal, and the EIS signal based on different time intervals (e.g., shorter or longer than 5 minutes). A data packet including all or some of the 5-minute features 740 and the derived features 750 can be used for optional blood glucose calibration at block 620, sensor glucose modeling at block 630, and optional conditional blanking and termination at block 640.

[0101] Due to production variations and aging of glucose sensors, some glucose sensors may not provide accurate readings based on the measured signals and derived signal characteristics. Therefore, glucose sensors may require occasional or periodic in vitro and / or in vivo calibration to improve the accuracy and / or reliability of the glucose sensors. Figure 6 As shown in the example of , the sensor signal can be calibrated at box 620 using a calibration function that is determined using a known good blood glucose value (such as a finger stick measurement from a blood glucose meter that measures blood glucose levels in capillaries). For example, a calibration ratio CR (or calibration factor) can be determined according to CR=BG / (Isig+offset). The calibration ratio and / or offset can then be used to calibrate an input signal such as an Isig signal. In some embodiments, a weighted calibration ratio can be determined based on a previous calibration ratio and the time of the previous calibration and applied to all subsequent data packets until the next calibration is performed.

[0102] At block 630, one or more SG models may be used to derive sensor glucose values ​​from the generated features. The SG model may include, for example, a function, an equation, a machine learning model, or other transformation model that may map the generated features, Isig signals, Vcntr signals, and / or EIS signals to sensor glucose (SG) values ​​indicating estimated blood glucose levels. For example, the features generated at blocks 610 and / or 620 may be input to one or more SG models as feature vectors, and the SG value may be determined as a dot product of the feature vector and a coefficient vector having corresponding coefficients (e.g., weights). For example, the SG model may be represented as:

[0103] SG=f(years,Isig,Vcntr,EIS).

[0104] In a simplified example, the SG value can be calculated as follows:

[0105] SG=α×years+β×Isig+γ×Vcntr+δ×EIS+Δ,

[0106] Where α, β, γ, and δ can be coefficients associated with age, Isig, Vcntr, and EIS, and △ is an offset value. In some embodiments, at least one of Isig, Vcntr, or EIS can include a vector, and at least one of β, γ, or δ can be a coefficient vector. In some embodiments, at least one of α, β, γ, and / or δ can be a function of time (e.g., sensor age). In some embodiments, different SG models can be used for different input parameter ranges, such as different ranges of age, Vcntr, or other sensor measurement data. A model can be selected from one or more models based on the range in which the input parameter falls.

[0107] In embodiments where one or more models include a machine learning model, the machine learning model may include a neural network or other machine learning model. For example, a machine learning model may include a neural network including a large number of nodes (neurons) arranged in one or more layers. Each node of the neural network may be connected to one or more other nodes of the neural network, wherein these connections may have associated coefficients or weights. In some embodiments, each individual node may implement a multiplication and accumulation function that combines the weighted values ​​of all its inputs based on the associated weights. In some embodiments, each connection (or node) may have a threshold function so that only signals exceeding the threshold may be propagated to other nodes. The neural network may be trained or self-learned using training samples rather than being explicitly programmed. In some embodiments, the neural network may include multiple layers, each layer including multiple nodes, wherein the input signal may traverse multiple layers to generate a final output. In some embodiments, a technique such as backpropagation technique may be used to train the neural network, such as determining coefficients or weights associated with the connection between nodes. The data used to train the machine learning model may include, for example, sensor data (e.g., Isig, Vcntr, EIS, etc.) and corresponding known SG or BG values.

[0108] In some embodiments, the machine learning model may receive a multidimensional feature vector as input. For example, the input to the machine learning model may include features such as EIS features, Isig trend features, Isig rate of change features, previous Isig values, previous Vcntr values, etc. Some examples of input features and models for determining SG values ​​can be found in, for example, U.S. Patent Application No. 16 / 773,422, entitled "Methods, Systems, and Devices for Continuous Glucose Monitoring", which is incorporated herein by reference in its entirety. The output of the machine learning model may depend on a combination of input features. In some embodiments, one or more models may include multiple (e.g., N) models (e.g., functions, equations, machine learning models, etc.). These models may use the same or different input features and / or may use different weights to weight the input features differently. In some embodiments, one or more models may be selected based on the input data and the SG value may be determined using the one or more models. For example, in some embodiments, the outputs of two or more models selected from a plurality of models may be combined to generate a final SG value.

[0109] Figure 8 Examples of using one or more models to determine sensor glucose values ​​according to certain embodiments are illustrated. Figure 8As shown, the input 810 to one or more models may include Isig, EIS, Vcntr or other input signals. In some embodiments, the input 810 may additionally include previously modeled (e.g., trend) information. In the illustrated example, the signal modeling and feature engineering engine 820 may include N models, such as models 822, 824, ..., 826, which may be trained or otherwise determined for various combinations of inputs or scenarios. As described above, these models may include machine learning models and may be trained to use different inputs or weight the inputs differently. The model controller 830 may perform selection, averaging, sorting, weighting or other processing on the sensor glucose values ​​generated by the models 822, 824, ..., 826. For example, the model controller 830 may select a model to determine the SG value based on the input 810 or other information (such as the age of the sensor) (e.g., from models 822, 824, ..., 826). In some embodiments, the model controller 830 may average the outputs of two or more models in the models 822, 824, ..., 826 to generate a final sensor glucose value 840. In some embodiments, the model controller 830 may take the average, weighted average, median, maximum, minimum, etc. of the outputs of a subset of models 822, 824, ..., 826. In some embodiments, the model controller 830 may rank or weight models 822, 824, ..., 826, and may select a high ranking model. In some embodiments, a confidence score associated with the output of each model in the models 822, 824, ..., 826 may be generated to indicate that the output is a correct confidence level, and the model controller 830 may adjust the model output to support one or more models associated with the highest confidence level. The final SG value 840 may be displayed to the user or may be transmitted to an insulin pump or insulin calculator. In some embodiments, a confidence score associated with the SG value 840 may be generated to indicate that the final SG value 840 is a correct confidence level.

[0110] At box 640, conditional blanking and termination may be performed on the SG values ​​generated by the SG model. For example, if the SG value is determined to be an outlier or otherwise erroneous, some SG values ​​generated by one or more SG models may be blanked and not transmitted or displayed to the user. In some embodiments, the conditional blanking and termination at box 640 may be performed by one or more models (e.g., the machine learning model used at box 630 or other machine learning models). For example, the machine learning model may classify the SG value as an outlier / non-outlier, having a large negative deviation / large positive deviation / nominal accuracy, having poor accuracy / medium accuracy / good accuracy, etc. The SG value may be blanked based on the output from the machine learning model. For example, if the output of the machine learning model indicates that the SG value is not an outlier (e.g., within a specific threshold range), the SG value may not be blanked. Otherwise, the SG value may be blanked. Similarly, if the output of the machine learning model indicates that the SG value has medium, normal, or good accuracy, the SG value may not be blanked. Otherwise, the SG value may be blanked. In some embodiments, when a large number of abnormal values ​​are generated, for example, using a sensor measurement signal from a sensor, the sensor can be terminated (e.g., stop calculating or sending any sensor data from the sensor and / or stop displaying any sensor data from the sensor).

[0111] As described above, sensor properties associated with glucose sensors (such as age (or time in use or time worn), battery level, calibration accuracy, sensing environment (e.g., oxygen level, temperature, humidity, and pH, etc.), physiological dynamics, production variability, and other properties) may affect enzymatic glucose level measurements in complex ways that may be difficult to model. As a result, the accuracy of many CGM sensors may not be optimal and may exhibit large differences in measurement results. The lower-than-expected performance of CGM sensors may be at least partially attributed to production variability from sensor to sensor, degradation of sensor performance over time, the complexity of modeling the in vivo behavior of CGM sensors (e.g., it may be difficult to model CGM sensors using the same model), etc.

[0112] In order to achieve the desired accuracy and reliability and reduce the effects of noise and other stray signals, CGM sensors may need to be calibrated in vitro and / or in vivo periodically or occasionally to improve the accuracy and / or reliability of the glucose sensor. For example, it may be necessary to calibrate the sensor data using a known good blood glucose value, which is typically obtained via a so-called "fingerstick measurement" using a blood glucose meter that measures blood glucose levels in capillaries. Performing such calibration measurements can increase patient burden and perceived complexity, and may be inconvenient, uncomfortable, or otherwise unpopular with patients. In addition, due to the time required for glucose to diffuse from capillaries to the interstitial space, ISF glucose measurements may lag behind blood glucose measurements, which may require certain signal processing (e.g., filtering) or other techniques to compensate for the physiological lag. Additionally, various factors may cause instantaneous changes in sensor output, which may affect the accuracy of calibration and measurement. Deterioration of sensor performance over time (which may also vary across sensors due to, for example, production differences between sensors) may further complicate these challenges.

[0113] According to certain embodiments disclosed herein, some in vivo characteristics of a glucose sensor (e.g., in vivo sensor sensitivity and intercept) can be predicted before sensor insertion and can be used in one or more SG models to estimate SG values ​​based on other input data (such as Isig, Vcntr, EIS, age, and features derived therefrom Isig, Vcntr, EIS, and / or age). For example, the in vivo characteristics of a glucose sensor can be predicted using in vitro characteristics of a glucose sensor and an in vitro to in vivo transformation model, wherein the in vitro characteristics of a glucose sensor can be predicted using manufacturing process measurement data associated with the glucose sensor or can be measured in vitro. In some embodiments, the in vivo sensor characteristics can be directly predicted using manufacturing process measurement data associated with the glucose sensor. The in vivo characteristics can be predicted as a function of time (e.g., age of the glucose sensor). In this way, at least some production variability and degradation of the glucose sensor can be considered in the SG calculation, and thus the SG value can be more accurately determined based on the original sensor measurement data (e.g., Isig, Vcntr, EIS, and age).

[0114] In one example, manufacturing process measurement data associated with an electrochemical glucose sensor, such as, for example, process parameters, thickness of various layers of the sensor, material composition and / or concentration, certain impedance and / or capacitance measurements, etc., may be obtained during and / or after manufacturing and may be used to determine in vitro properties of the electrochemical glucose sensor, such as in vitro sensitivity (sensitivity 体外 ) and the intercept ( 体外 ), which can be roughly characterized by the following formula: 体外 SG value in vitro体外 The relationship between:

[0115] SG 体外 =Isig 体外 / Sensitivity 体外 +Intercept 体外 .

[0116] In vivo properties of electrochemical glucose sensors (e.g., in vivo sensitivity (sensitivity 体内 ) and the intercept ( 体内 )) can then be determined using in vitro characteristics of the electrochemical glucose sensor, which are determined using manufacturing process measurement data and according to one or more transformation models such as the following:

[0117] Sensitivity 体内 =f(sensitivity 体外 ),and

[0118] intercept 体内 =f(intercept 体外 ).

[0119] In some embodiments, the in vitro to in vivo transformation model can use other in vitro properties as input. For example, the in vivo sensitivity can be a function of the in vitro sensitivity and other parameters (such as the in vitro intercept). Both the in vitro and in vivo properties of the electrochemical glucose sensor can vary with the age of the electrochemical glucose sensor and can therefore be time-varying (a function of time).

[0120] Based on the in vivo characteristics of the electrochemical glucose sensor (e.g., in vivo sensitivity and in vivo intercept), one or more SG models may be used to determine the SG value, the one or more SG models utilizing the in vivo characteristics as part of the input in addition to the sensor measurement data. The one or more SG models may include, for example, one or more machine learning models, equations, etc. For example, one SG model may be represented as:

[0121] SG=f(age,Isig,Vcntr,EIS,sensitivity 体内 ,intercept 体内 ).

[0122] In a simplified example, the SG value can be determined according to the following formula:

[0123]

[0124] Where α, β, γ, δ, and θ are coefficients that may or may not be time-varying and may include individual values, vectors, or matrices. In some embodiments, the above equation may include an offset value. In some embodiments, Isig, Vcntr, EIS, Sensitivity 体内 or θ × intercept体内 At least one of may include a vector. In some embodiments, α, β, γ, δ, and θ may have different values ​​for different input parameter ranges (e.g., different years and / or Vcntr ranges). As a result, the effects of manufacturing process variations and sensor degradation over time on sensor measurement data may be at least partially explained by using the time-varying internal characteristics of the sensor in the SG model. Therefore, the determined SG value may be a more accurate estimate of the blood glucose level.

[0125] Fig. 9 An example of determining a transformation model for mapping an in vitro characteristic of a CGM sensor to an in vivo characteristic of a CGM sensor according to certain embodiments is illustrated. The transformation model can be determined based on a sensor signal measured in vitro and a sensor signal measured in vivo (e.g., clinical data). As illustrated, in vitro measurements and in vivo measurements can be performed at boxes 910 and 920, respectively. In vitro and in vivo measurements can be obtained from, for example, in vitro tests and clinical feasibility studies, respectively. In one example, an in vitro test can be performed by inserting a glucose sensor into a custom flow cell including a sugar analyte and measuring the glucose concentration in the flow cell by the glucose sensor. In some embodiments, in vitro measurements can be performed by each glucose sensor at different times (e.g., different years after first use), and in vivo measurements can also be performed by each glucose sensor at different times (e.g., different years after first use).

[0126] At box 912, in vitro characteristics of the sensor (such as in vivo sensitivity and in vivo intercept) may be determined based on the in vitro measurements and the relationship between the in vitro characteristics and the in vitro measurements (e.g., SG=Isig / sensitivity+intercept). At box 922, in vivo characteristics of the sensor (such as in vivo sensitivity and in vivo intercept) may be determined based on the in vivo measurements and the relationship between the in vivo characteristics and the in vivo measurements. At box 930, the in vitro characteristics and the in vivo characteristics may be associated, for example, using clustering techniques, regression techniques, or other machine learning techniques, to determine a transformation model 940 that maps the in vitro characteristics to the in vivo characteristics. In one example, the in vitro and in vivo characteristics may be associated based on a linear regression or multiple linear regression of the in vitro characteristics and the in vivo characteristics.

[0127] Fig.10 An example of using manufacturing process measurement data to determine in vitro and / or in vivo properties of a glucose sensor according to certain embodiments is illustrated. As illustrated, an electrochemical sensor array can be manufactured on a substrate 1002, which can then be cut into individual sensors, such as by slicing. As described above with respect to, for example, FIG. 4A to FIG. 4CAs described, each electrochemical sensor may include multiple electrodes, and each electrode may include multiple layers. In the illustrated example, the electrochemical sensor array may be manufactured simultaneously in the region 1004 on the substrate 1002, and the additional process control monitor (PCM) may be manufactured simultaneously in the region 1006 on the substrate 1002. The PCM may be adjacent to the region 1004 or otherwise near the region. In the illustrated example, the region 1004 may be arranged in a vertically oriented column on the substrate 1002, wherein the region 1006 is arranged in a vertically oriented column between the regions 1004. Each region 1006 may include a plurality of PCMs (or sacrificial sensors) arranged vertically throughout the length of the region 1006, and each region 1004 may include a plurality of electrochemical sensors arranged vertically throughout the length of the region 1004.

[0128] During and / or after the manufacturing process, the PCM manufactured in the region 1006 may be analyzed using a process measurement system 1010 (which may include one or more instruments or systems) to obtain manufacturing process measurement data. The process measurement system 1010 may be capable of measuring biological, chemical, electrical, and / or physical properties of the corresponding PCM. The manufacturing process measurement data obtained for each PCM or electrochemical sensor may include, but is not limited to, glucose oxidase (GOx) thickness, GOx solution activity, glucose limiting membrane (GLM) thickness, working electrode (WE) platinum imaginary impedance, counter electrode (CE) platinum imaginary impedance, human serum albumin (HSA) concentration, etc. It should be noted that the manufacturing process measurements are not limited to any specific type or number of manufacturing process measurements listed, and may include measurements of any number of different properties or characteristics (e.g., dielectric properties, permeability, diffusivity, etc.). Additionally or alternatively, in some embodiments, manufacturing process measurements may be obtained by directly measuring the characteristics of the electrochemical sensor in the sensing region 1004.

[0129] In some embodiments, some manufacturing process measurements may be performed during the manufacture of the electrochemical sensor. For example, the imaginary impedance of the working electrode (and similarly, the imaginary impedance of the counter electrode) may be measured after performing a platinum electroplating process to form the electroplated layer 484. GOx solution activity measurements may be performed during or after the GOx solution preparation process before deposition, and the GOx thickness (e.g., the thickness of the GOx layer 488) may be measured after the slot coating and selective patterning process on the working electrode 404. HSA concentration may be measured during or after the solution preparation process before spraying the HSA layer 490, and the GLM thickness (e.g., the thickness of the GLM layer 494) may be measured after applying the GLM layer 494. In some embodiments, these measurements may be performed relative to the electrodes of the sacrificial electrochemical sensor or PCM manufactured in the region 1006 on the substrate 1002. In some embodiments, additional manufacturing process measurements of the electrode, such as surface roughness or other topographical measurements, may be obtained during or after manufacture (e.g., via interferometry).

[0130] The manufacturing process measurements obtained by the process measurement system 1010 may be provided to the modeling system 1030. In some embodiments, the modeling system 1030 may interpolate and / or extrapolate the manufacturing process measurement data of different PCMs to obtain representative manufacturing process measurement data for a given electrochemical sensor manufactured on the substrate 1002. For example, the modeling system 1030 may maintain an association between the location (e.g., coordinates) of the corresponding PCM on the substrate 1002 and the corresponding manufacturing process measurement data obtained for the corresponding PCM. Thereafter, based on the location of the corresponding electrochemical sensor manufactured on the substrate 1002, the modeling system 1030 may: identify a subset of PCMs that are adjacent, neighboring, or otherwise close to the corresponding electrochemical sensor; obtain manufacturing process measurement data for the identified subset of PCMs; and then average, interpolate, or otherwise combine the manufacturing process measurement data of the different subsets of PCMs based on the relationship between the location of the corresponding electrochemical sensor and the location of the different PCMs to obtain representative manufacturing process measurement data for the location of the substrate 1002 where the corresponding electrochemical sensor is manufactured.

[0131] In some embodiments, a test system 1020 (which may include one or more instruments or subsystems) may be used to analyze each electrochemical sensor in the set of electrochemical sensors manufactured in area 1004 to obtain in vitro and / or in vivo measurements obtained in response to one or more analyte samples having a known glucose concentration. For example, in some embodiments, the electrochemical sensors manufactured in area 1004 may be exposed to an analyte sample having a known concentration of glucose, and the test system 1020 may include sensor electronics, such as a potentiostat (e.g., as described above with respect to Figure 5As described), a transmitter, a recorder, an ammeter, a voltmeter, or other suitable measuring instrument capable of measuring signals generated by or otherwise associated with an electrochemical sensor. The in vivo measurements obtained for each electrochemical sensor may include one or more currents output by the electrochemical sensor in response to a test sample having a known reference glucose concentration, an electrochemical impedance spectroscopy (EIS) value (for one or more frequencies) or other measurements indicating a characteristic impedance associated with the electrochemical sensor in response to the test sample, a counter electrode voltage (Vcntr) (e.g., the difference between the counter electrode potential and the working electrode potential), etc. For example, the sensor electronics may include potentiostat hardware and firmware configured to cooperatively collect current measurements corresponding to the current flowing through the working electrode and generated by the glucose oxidase layer of the working electrode in response to the test sample, and determine the counter voltage (Vcntr) as the difference between the measured counter electrode potential and the working electrode potential. The sensor electronics may also be configured to perform electrochemical impedance spectroscopy at various time intervals and using a plurality of drive signals of different frequencies. In some embodiments, a first electrochemical sensor may be used for in vitro measurements, while another electrochemical sensor adjacent to the first electrochemical sensor on substrate 1002 (and therefore may have similar in vitro / in vivo performance as the first electrochemical sensor) may be used for in vivo measurements.

[0132] The in vitro and / or in vivo measurements obtained by the testing system 1020 may also be provided to a modeling system 1030, which may maintain in vitro and / or in vivo measurements of the respective electrochemical sensors fabricated on the substrate 1002. The modeling system 1030 may also maintain an association between the in vitro and / or in vivo measurements and representative manufacturing process measurement data for each respective electrochemical sensor fabricated on the substrate 1002. Based on the relationship between the manufacturing process measurement data and the in vitro and / or in vivo measurements of the various electrochemical sensors, the modeling system 1030 may determine a model for predicting in vitro and / or in vivo properties (e.g., sensitivity and intercept or bias) of the electrochemical sensor based on one or more manufacturing process measurement parameters associated with the electrochemical sensor.

[0133] For example, in some embodiments, for each characteristic of the in vitro and / or in vivo characteristics of the electrochemical sensor, such as the sensitivity or intercept associated with Isig, the modeling system 1030 may utilize machine learning or other techniques to determine a combination of manufacturing process measurement parameters that can best correlate with the characteristic or can predict the characteristic, and also determine a corresponding equation, function, or another model and corresponding weights or coefficients for predicting the characteristic based on the combination of manufacturing process measurement parameters. The determined model may be able to map a specific combination of one or more manufacturing process measurement parameters to an in vitro or in vivo characteristic of the electrochemical sensor, such as the sensitivity or intercept associated with Isig, or another in vitro or in vivo characteristic associated with another signal of the electrochemical sensor, such as an EIS value, Vcntr, etc.

[0134] In one example, the modeling system 1030 may determine the in vitro sensitivity of the corresponding electrochemical sensor based on the known glucose concentration of the test sample used to test the corresponding electrochemical sensor and the measured in vitro current output of the corresponding electrochemical sensor in response to the test sample (e.g., according to the output sensor current Isig). Thereafter, the modeling system 1030 may use machine learning techniques to identify a subset of manufacturing process measurement parameters that may be related to or predict the in vitro sensitivity of the electrochemical sensor to glucose concentration, and then determine a corresponding equation, function, or another model (e.g., a neural network) for predicting the in vitro sensitivity of the electrochemical sensor based on the subset of manufacturing process measurement parameters. For example, the manufacturing process measurement parameters of each electrochemical sensor may be constructed into a corresponding matrix or vector and fed to the neural network as an input to the neural network, and the output of the neural network may be compared with the determined in vitro sensitivity to determine a loss (or cost) function. Back propagation techniques may be used to modify the weights and / or biases associated with each manufacturing process measurement parameter based on the loss function, thereby training the model to learn the appropriate input parameters and corresponding weights and / or biases to minimize the loss function. Manufacturing process measurement parameters that have little or no correlation with in vitro sensitivity (e.g., associated with very low weights in the neural network) can be pruned to simplify and / or optimize the model. Other characteristics of the in vitro and / or in vivo properties of the electrochemical sensor can be modeled in a similar manner using manufacturing process measurement parameters.

[0135] It should be noted that any number of different machine learning techniques or other optimization techniques can be used to determine a model for predicting in vitro and / or in vivo characteristics of an electrochemical sensor based on manufacturing process measurement data, including, for example, genetic programming, support vector machines, Bayesian networks, probabilistic machine learning models, principal component analysis, fuzzy logic, heuristic derivation combinations, etc., or combinations thereof. In addition, electrochemical sensors on multiple different substrates can be used for measurement and modeling to achieve the desired level of accuracy, reliability, and versatility of the resulting model. In addition, as described above, in vitro and / or in vivo characteristics (such as sensitivity and intercept) can vary over time. Therefore, in vitro and / or in vivo measurements of electrochemical sensors can be performed at different times during the life of the electrochemical sensor. Therefore, a model for predicting in vitro and / or in vivo characteristics (e.g., sensitivity and intercept or deviation) of an electrochemical sensor based on one or more manufacturing process measurement parameters associated with the electrochemical sensor can also use time (e.g., the age or operating time of the electrochemical sensor) as an input parameter.

[0136] In some embodiments, before the electrochemical sensor is shipped to a user (e.g., during or after factory manufacturing and / or assembly) or inserted into a user's body, the model and manufacturing process measurement parameters may be used to predict the in vitro characteristics of each electrochemical sensor as a function of the age of the electrochemical sensor, and then the predicted in vitro characteristics may be mapped to the in vivo characteristics based on the above-mentioned in vitro to in vivo transformation model, and the in vivo characteristics of each electrochemical sensor may be saved to a storage device of the sensor electronics of the electrochemical sensor so that the sensor electronics may use the in vivo characteristics to determine the SG value based on the sensor measurement data (e.g., Isig, Vcntr, EIS, etc.). When the in vivo portion of the electrochemical sensor is inserted into the user's body and the sensor electronics is activated, the in vivo characteristics of the electrochemical sensor may be retrieved from the storage device and used by the sensor electronics to estimate the SG value based on the sensor measurement data (e.g., Isig, Vcntr, EIS, etc.).

[0137] In some embodiments, manufacturing process measurement parameters and models for mapping manufacturing process measurement parameters to in vitro and / or in vivo characteristics (as well as models for mapping in vitro characteristics to in vivo characteristics) may be saved to a storage device of the sensor electronics of the electrochemical sensor, where the sensor electronics may use the models and manufacturing process measurement parameters to determine the in vivo characteristics. In some embodiments, the in vitro characteristics of each electrochemical sensor as a function of the age of the electrochemical sensor may be predicted using the manufacturing process measurement parameters and the model determined in the learning phase, and before the electrochemical sensor is shipped to the user or inserted into the user's body, these in vitro characteristics are saved together with the in vitro to in vivo transformation model to a storage device of the sensor electronics for the electrochemical sensor, and the sensor electronics may use the in vitro to in vivo transformation model and the in vitro characteristics to determine the in vivo characteristics. In some embodiments, the server may receive the manufacturing process measurement data and the predicted in vivo characteristics of the electrochemical sensor directly from the manufacturing process measurement data or indirectly from the in vitro characteristics predicted using the manufacturing process measurement data, and may transmit the predicted in vivo characteristics to the sensor electronics of the electrochemical sensor after the sensor electronics are activated. In some embodiments, the sensor electronics may transmit sensor measurement data (e.g., Isig, Vcntr, EIS, etc.) to a receiver device (such as a smart phone, insulin pump, or server), which may determine the SG value based on the in vivo characteristics of the electrochemical sensor (or model and manufacturing process measurement parameters) stored thereon or in a database.

[0138] Based on the predicted in vivo characteristics, one or more SG models may be used selectively or in combination to estimate the SG value according to the sensor measurement data, wherein one or more SG models may utilize the in vivo characteristics as part of the input. It should be noted that the SG model for SG estimation using the estimated in vivo characteristics of the sensor is not limited to the functions or equations described above with respect to the examples. In some embodiments, the SG model may include other linear or nonlinear functions or equations, or may include various machine learning models (e.g., various neural network models). In addition, the SG model may use features derived from the sensor measurement data as described above, such as the derivative (e.g., rate of change) or integral of the sensor measurement data, historical sensor measurement data, filtered sensor measurement data, product of parameters of the sensor measurement data, etc. In some embodiments, the SG model may use one or both of the in vivo sensitivity and the in vivo intercept as input. In some embodiments, the SG model may use other in vivo characteristics that may be affected by changes in the manufacturing process and may be estimated using manufacturing process measurement data.

[0139] As described above, since the in vivo sensitivity, in vivo intercept, and other in vivo characteristics can be estimated based on manufacturing process measurement data associated with a particular sensor and a model that directly or indirectly maps the manufacturing process measurement data to the in vivo characteristics, using the estimated in vivo characteristics of the sensor in the SG model to determine the SG value based on the in vivo sensor measurement data (e.g., Isig, Vcntr, and EIS) can at least partially account for the impact of production process variability on the sensor measurement data. Therefore, the determined SG value may be more accurate and more reliable. In addition, since the in vivo characteristics may be time-varying, the degradation of the sensor over time can be at least partially accounted for by using the time-varying in vivo characteristics of the sensor in the SG model. In addition, in vitro or in vivo calibration may not be required, which can reduce the burden on the user.

[0140] In many cases, a single SG model may not accurately estimate blood glucose levels because various sensor properties may affect blood glucose level measurements in complex ways, which may be difficult to model using a single SG model. According to certain embodiments, a set of SG models may be used to estimate SG values ​​based on SG measurement data generated by a sensor, wherein the input parameter space of the SG model set may be partitioned into multiple regions, and each SG model in the SG model set may be a regional model for input parameters in a corresponding region in multiple regions of the input parameter space. For example, in some embodiments, the input parameter space may be partitioned based on the age of the sensor, Vcntr, predicted in vivo sensitivity, predicted in vivo intercept, or any combination thereof. In one example, the input parameter space may be partitioned based on two parameters, wherein at least one of the two parameters may include in vivo characteristics, such as in vivo sensitivity or in vivo intercept described above. In some other examples, the input parameter space may be partitioned based on three or more parameters, wherein at least one of the three or more parameters may include in vivo characteristics, such as in vivo sensitivity or in vivo intercept. The input parameter space may be partitioned evenly or unevenly. The SG model for each region may be trained separately using different training data sets, or may be trained together using the same training data set. In some embodiments, the SG models may use the same input parameters and have a similar structure, but may have different coefficients or weights for the same input parameters. In some embodiments, the SG models for different regions of the input parameter space may use different input parameters and / or may include different types of models or different model structures.

[0141] Fig.11AAn example of a partition model 1100 including a set of regional models for modeling the relationship between SG values ​​and sensor measurement data according to certain embodiments is shown. In the illustrated example, the input parameter space can be partitioned based on the range of two parameters 1110 and 1120, wherein at least one of the two parameters may include in vivo sensitivity, in vivo intercept, or another predicted in vivo characteristic of the glucose sensor, and the other of the two parameters may include, for example, sensor age, Vcntr, another in vivo characteristic of the glucose sensor, another input parameter of the SG model, or an environmental property of the glucose sensor (e.g., humidity or temperature). Each region 1130 of the input parameter space can be associated with a unique regional SG model. The regions 1130 may have the same or different sizes. Each region 1130 may have a regular shape (e.g., a rectangle) or an irregular shape, wherein the boundary between two adjacent regions may be a straight line or may be a curve.

[0142] Fig. 11B Another example of a partition model 1102 including a set of regional models for modeling the relationship between SG values ​​and sensor measurement data according to certain embodiments is shown. In the illustrated example, the input parameter space can be partitioned based on the range of three or more parameters 1140, 1150 and 1160, wherein at least one of the three or more parameters may include in vivo sensitivity, in vivo intercept or another predicted in vivo characteristic of the glucose sensor, and other parameters of the three or more parameters may include, for example, sensor age, Vcntr, another in vivo characteristic of the glucose sensor, another input parameter of the SG model or an environmental property of the glucose sensor (e.g., humidity or temperature), or a combination thereof. Each region 1170 of the input parameter space can be associated with a unique regional SG model. The regions 1170 can have the same or different sizes. Each region 1170 can have a regular shape or an irregular shape.

[0143] like Fig.11A and Fig. 11BAs shown, the input parameter space can be partitioned into multiple regions, which together form a continuous input parameter space. In some embodiments, in order to partition the input parameter space, each region of the input parameter space in which the sensor may have similar characteristics or can be modeled using a similar model can be identified. In some embodiments, the stability of the sensor trend, the regularity of the sensor signal amplitude, or other characteristics of the sensor signal can be identified and used to partition the input parameter space. In some embodiments, machine learning techniques such as clustering, dimensionality reduction (e.g., principal component analysis (PCA)), etc. can be used to partition the input parameter space. In some embodiments, if the model used in one or more regions generates an erroneous SG value (e.g., with a large mean absolute relative difference (MARD)), if the model in the adjacent region outputs significantly different SG values ​​for similar input parameters, etc., the partition and / or model can be modified.

[0144] In some embodiments, different models may be used in different regions 1130 or 1170 of the input parameter space. The model may include, for example, a linear regression model, a neural network, another machine learning model, a function, an equation, or another model. In some embodiments, multiple candidate models for each region of the input parameter space may be ranked based on the complexity, accuracy, and / or other metrics of the model, and one or more models may be selected for the region based on the ranking. This process may be performed for each region. In some embodiments, a simpler model may be sufficient for some regions, while a more complex model may be required for other regions. Therefore, a combination of different types of models may be used.

[0145] In some embodiments, various smoothing or blending techniques may be used in the regional models of adjacent regions to avoid sudden changes in the output SG values ​​at the boundaries between adjacent regions due to different models used for adjacent regions. For example, in some embodiments, the output SG value of an input parameter near the boundary between two regions may be a weighted average of the output SG values ​​of the two models from the two adjacent regions. The output SG value of an input parameter at a region adjacent to three or more adjacent regions (e.g., a corner) may be a weighted average of the output SG values ​​of three or more models from the three or more adjacent regions. In other embodiments, other techniques for smoothing or blending the output SG values ​​of input parameters at or near region boundaries may be used.

[0146] Fig. 11C Another example of a comprehensive model including a set of multiple regional SG models for estimating SG values ​​based on sensor measurement data generated by a glucose sensor according to certain embodiments is illustrated. Fig. 11CAs shown, the input parameter space can be partitioned in different ways according to different partitioning schemes (e.g., based on different sets of input parameters, and / or partitioning the range of one or more input parameters in different ways), wherein at least one of the input parameters used in at least one partitioning scheme may include a predicted in vivo sensitivity, an in vivo intercept, or another predicted in vivo characteristic of the glucose sensor. A corresponding set of regional SG models can be determined for the region of the input parameter space partitioned according to each partitioning scheme. For example, based on the region into which the current input parameter falls, a regional SG model can be selected from each set of regional SG models for the corresponding input parameter space partitioning scheme. Therefore, multiple regional SG models can be selected from multiple sets of regional SG models for different partitioning schemes, and the multiple regional SG models can be used to calculate SG values ​​in parallel, wherein the outputs of the selected multiple regional SG models can be combined (e.g., by averaging or weighted averaging) to determine the final predicted SG value.

[0147] exist Fig. 11C In one example shown, in the first partitioning scheme 1104, the input parameter space may be partitioned into four regions based on different ranges of sensor age and predicted in vivo sensitivity, and four different regional SG models (e.g., models 1 to 4) may be determined (e.g., trained) for the four regions, respectively. In the second partitioning scheme 1106, the input parameter space may be partitioned into four regions based on different ranges of Vcntr and predicted in vivo intercepts, and four different regional SG models (e.g., models 5 to 8) may be determined (e.g., trained) for the four regions, respectively. When receiving an input parameter set (e.g., sensor measurement data and derived features), a first regional SG model may be selected from the four regional SG models of the four regions in the first partitioning scheme 1104 based on the region into which both the current sensor age and the predicted in vivo sensitivity fall, and a second regional SG model may be selected from the four regional SG models of the four regions in the second partitioning scheme 1106 based on the region into which both the current sensor age and the predicted in vivo intercept fall. The SG value predicted using the first region SG model may be combined (eg, averaged or weighted averaged) with the SG value predicted using the second region SG model to determine the final SG value for the current input parameter value. Fig. 11C This is for illustrative purposes only and is not intended to limit the partitioning scheme to Fig. 11C Specific examples shown.

[0148] Fig. 12AA flowchart 1200 is included that illustrates an example of a process for predicting in vivo characteristics of a glucose sensor according to certain embodiments. The operations in the flowchart 1200 may be performed by, for example, a computing device (e.g., a computer or a server). In one example, the operations in the flowchart 1200 may be performed by the modeling system 1030 described above. Although the flowchart 1200 may describe the operations as a sequential process, some operations may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. The process may have additional steps not included in the figure. Some operations may be optional or may be omitted.

[0149] The operations in block 1210 of flowchart 1200 may include obtaining manufacturing process measurement data for a glucose sensor, as described above with respect to, for example, Fig.10 Described. The manufacturing process measurement data of the glucose sensor can be measured during or after the manufacture of the glucose sensor, and can be measured at the location of the glucose sensor or at one or more PCMs adjacent to the glucose sensor on the substrate. In some embodiments, the manufacturing process measurement data can be calculated (e.g., interpolated or extrapolated) based on the manufacturing process measurement data of the adjacent PCM or sacrificial sensor. The manufacturing process measurement data may include, for example, glucose oxidase (GOx) thickness, GOx solution activity, glucose limiting membrane (GLM) thickness, working electrode (WE) platinum imaginary impedance, counter electrode (CE) platinum imaginary impedance, human serum albumin (HSA) concentration, dielectric properties, permeability, diffusivity, etc.

[0150] The optional operation in block 1220 may include estimating in vitro characteristics of the glucose sensor based on manufacturing process measurement data. Fig.10 As described, the modeling system 1030 may utilize machine learning or other techniques to determine a combination of manufacturing process measurement parameters that can best correlate with or predict each of the in vitro characteristics, and also determine a corresponding equation, function, or another model and corresponding weights or coefficients for predicting the in vitro characteristics based on the combination of manufacturing process measurement parameters. The determined model may be able to map a corresponding combination of one or more manufacturing process measurement parameters to each in vitro characteristic of the glucose sensor, such as an in vitro sensitivity or intercept associated with Isig. The determined model may be used to predict the in vitro characteristics of a new glucose sensor based on manufacturing process measurement data associated with the new glucose sensor.

[0151] The operations in block 1230 may include determining the in vivo characteristics of the glucose sensor based on the manufacturing process measurement data or the in vitro characteristics estimated in block 1220. For example, in some embodiments, the in vivo characteristics of the glucose sensor may be directly predicted based on the manufacturing process measurement data using a determined model that maps a specific combination of one or more manufacturing process measurement parameters to an in vivo characteristic of the glucose sensor, such as an in vivo sensitivity or intercept associated with Isig, as described above with respect to Fig.10 In some embodiments, the in vitro to in vivo transformation model described above and the in vitro properties estimated in block 1220 may be used to predict the in vivo properties of the glucose sensor.

[0152] The operations in block 1240 may include saving the in vivo characteristics of the glucose sensor, for example, to a memory device of the sensor electronics connected to the glucose sensor before the glucose sensor is delivered to the user. For example, the in vivo characteristics of the glucose sensor may be predicted using the operations of blocks 1210 to 1230 and saved to the memory device after the glucose sensor and the sensor electronics are assembled in a factory. After the glucose sensor is inserted into the user and initialized and the sensor electronics are activated, the processing unit may read and use the in vivo characteristics of the glucose sensor saved in the memory device.

[0153] Fig. 12B 1205 is included that illustrates an example of a process for estimating SG values ​​using the in vivo characteristics of a glucose sensor and one or more SG models according to certain embodiments. The operations in flowchart 1205 may be performed by, for example, monitoring device 104, glucose sensor subsystem 210, sensor electronics 304, FIG. 14A to FIG. 14B The glucose sensor device 1400 described in the flowchart 1205 is performed. Although the flowchart 1205 can describe the operations as a sequential process, some operations can be performed in parallel or concurrently. In addition, the order of the operations can be rearranged. The process may have additional steps not included in the figure. Some operations may be optional or may be omitted.

[0154] The operations in block 1250 of flowchart 1205 may include obtaining in vivo characteristics of a glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, as described above with respect to, for example, Fig.10 and Fig. 12A The in vivo characteristic may include, for example, in vivo sensitivity, in vivo intercept, another in vivo characteristic of the glucose sensor, or a combination thereof. The in vivo sensitivity and in vivo intercept may characterize the relationship between the sensor current (Isig) of the glucose sensor and the corresponding blood glucose (BG) level according to the following formula:

[0155] BG = Isig / in vivo sensitivity + in vivo intercept.

[0156] Both the in vivo sensitivity and the in vivo intercept of the glucose sensor can be time-varying. In some embodiments, the above equation may include optional functions of one or more other (predicted) in vivo characteristics. As described above, before the glucose sensor is inserted into the subcutaneous layer of the user, the in vivo characteristics of the glucose sensor can be stored in a memory device of the sensor electronics of the glucose sensor, and the sensor electronics can obtain the in vivo characteristics of the glucose sensor by reading the in vivo characteristics from the memory device of the glucose sensor.

[0157] The operations in block 1260 may include receiving sensor measurement data measured by the glucose sensor. The sensor measurement data may include, for example, sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof. In some embodiments, derived features or other features may be derived or extracted from the sensor measurement data (as described above with respect to, for example, Figures 6 to 8 described), and can be used in combination with sensor measurement data as an input parameter for estimating the SG value.

[0158] The operation in box 1270 may include using an SG model to estimate the SG value, wherein the input parameters of the SG model may include the in vivo characteristics of the glucose sensor and the sensor measurement data (and features derived or extracted from the sensor measurement data), and the output of the SG model may be the SG value. In some embodiments, the input parameters of the SG model may include one or both of the predicted in vivo sensitivity and the predicted in vivo intercept of the glucose sensor. In some embodiments, the SG model may include a partition model, which includes multiple regional models for corresponding regions in multiple regions of the input parameter space of the SG model, wherein estimating the SG value using the SG model may include selecting a regional model from multiple regional models based on the in vivo characteristics of the glucose sensor, the sensor measurement data, or both. Different regions in the multiple regions of the input parameter space of the SG model may correspond to different ranges of the input parameters of the SG model. In some embodiments, the input parameter space of the SG model may be partitioned into multiple regions based at least in part on the in vivo characteristics of the glucose sensor. The SG model may include one or more machine learning models, equations, functions, or combinations thereof.

[0159] In some embodiments, the SG model may include a first partition model, the first partition model including a plurality of first regional models for corresponding regions of a plurality of first regions of the input parameter space of the SG model, wherein the input parameter space of the SG model may be partitioned into the plurality of first regions based on a first partition scheme. The SG model may also include a second partition model, the second partition model including a plurality of second regional models for corresponding regions of the plurality of second regions of the input parameter space of the SG model, wherein the input parameter space of the SG model may be partitioned into the plurality of second regions based on a second partition scheme different from the first partition scheme. For example, the first partition scheme and the second partition scheme may partition the input parameter space using different combinations of input parameters, and / or may partition the input parameter space according to different combinations of input parameter ranges. The SG model may be used to estimate the SG value in the following manner: generating a first SG value using one regional model selected from the plurality of first regional models based on an in vivo characteristic of the glucose sensor, sensor measurement data, or both; generating a second SG value using one regional model selected from the plurality of second regional models based on an in vivo characteristic of the glucose sensor, sensor measurement data, or both; determining a final SG value based on a combination (e.g., an average or weighted average) of the first SG value and the second SG value.

[0160] Fig.13A 1300 is included that illustrates an example of improving sensor performance by using predicted in vivo characteristics in SG value estimation according to certain embodiments. Fig.13A , the horizontal axis represents the number of unique features used in the SG model, and the vertical axis represents the mean absolute relative difference (MARD, which indicates the accuracy of the sensor) of the estimated SG value relative to the reference measurement result. In the illustrated example, data points 1310, 1312, and 1314 indicate the MARD values ​​of the SG values ​​estimated using SG models that use 1, 7, and 16 unique parameters of the sensor measurement data, respectively, without using the predicted in vivo characteristics. Data points 1320, 1322, and 1324 indicate the MARD values ​​of the SG values ​​estimated using SG models that use the same 1, 7, and 16 unique parameters of the sensor measurement data as data points 1310, 1312, and 1314, and also use the predicted in vivo characteristics (e.g., sensitivity and intercept associated with Isig). As illustrated, by using the predicted in vivo characteristics as additional input parameters of the SG model, the MARD values ​​and therefore the accuracy of the sensor can be significantly improved.

[0161] Fig. 13B 1305 is included that illustrates an example of improving sensor performance by using predicted in vivo characteristics in SG value estimation according to certain embodiments. Fig. 13B, the horizontal axis represents the number of unique features used in the SG model, and the vertical axis represents the proportion of estimated SG values ​​that are within 20 mg / dL of a reference glucose level or within 20% of a reference glucose level (referred to as consistency 20 / 20%, which may indicate the accuracy of the glucose sensor). In the illustrated example, data points 1330, 1332, and 1334 indicate consistency 20 / 20% values ​​of SG values ​​estimated using SG models that use 1, 7, and 16 unique parameters of the sensor measurement data, respectively, without using predicted in vivo characteristics. Data points 1340, 1342, and 1344 indicate consistency 20 / 20% values ​​of SG values ​​estimated using SG models that use the same 1, 7, and 16 unique parameters of the same sensor measurement data as data points 1330, 1332, and 1334, and also use predicted in vivo characteristics (including sensitivity and intercept). As illustrated, by using the predicted in vivo properties as additional input parameters to the SG model, the agreement 20 / 20% value and thus the accuracy of the sensor can be significantly improved.

[0162] Fig.14A and Fig. 14BAn example of an integrated glucose sensor device 1400 according to certain embodiments is depicted, which can implement some of the examples disclosed herein. The glucose sensor device 1400 can be an example of a monitoring device 104, a glucose sensor subsystem 210, or a CGM system 300. In the illustrated example, the glucose sensor device 1400 may include a housing formed by a base 1410 and a top cover 1420, a sensor electronics enclosed in the housing, an electrochemical sensor 1440 partially located within the housing and connected to the sensor electronics, and an adhesive patch 1430 attached to the base 1410. The electrochemical sensor 1440 can be an example of the electrochemical sensor described above (e.g., glucose sensor 400 or 402), and may include a plurality of electrodes located at a distal end that can be inserted into the subcutaneous layer of a user. The proximal end of the electrochemical sensor 1440 can be bent and held against the printed circuit board (PCB) 1412 by an elastomer or rubber block 1414, so that the contacts on the proximal end of the electrochemical sensor 1440 can be physically and electrically connected to the contact pads on the PCB 1412. The housing can be hermetically sealed to protect the sensor electronics from moisture. The electrochemical sensor 1440 can be inserted into the subcutaneous layer of the user using an insert. The insert can include a needle and can encapsulate the entire glucose sensor device 1400 before insertion. When activated (e.g., pushed), the insert can move the entire glucose sensor device 1400 toward the user's skin to attach the adhesive patch 1430 to the user's skin and insert the distal end of the electrochemical sensor 1440 into the subcutaneous layer of the user using a needle, wherein the needle can be automatically retracted, for example, after the distal end of the electrochemical sensor 1440 is inserted into the user's body.

[0163] The sensor electronics in the housing may include a battery 1416, an antenna 1418, and circuits on the PCB 1412. The circuits on the PCB 1412 may include, for example, a processor, a controller, a potentiostat, a filter, an amplifier, a wireless (e.g., Bluetooth) transceiver, one or more memory devices, a power conversion and management device, or a combination thereof. The predicted in vivo characteristics of the electrochemical sensor 1440 and the SG model described above may be stored in one or more memory devices. Before or after the distal end of the electrochemical sensor 1440 is inserted into the subcutaneous layer of the user, the sensor electronics may be activated to initialize the sensor for measuring sensor measurement data (e.g., Isig, Vcntr, EIS, etc.). The processor or controller may execute instruction codes stored on one or more memory devices to obtain sensor measurement data, process (e.g., filter, amplify, transform, etc.) the sensor measurement data, estimate SG values ​​using the SG model and the sensor measurement data, optionally clear certain estimated SG values, and send the estimated SG values ​​to a receiver (e.g., a smart phone or insulin pump) via the wireless transceiver and antenna 1418, as described above.

[0164] Fig.15 An example of a delivery device 1500 is depicted that can implement the above-described delivery devices (e.g., delivery device 102 or insulin delivery subsystem 230). In an insulin delivery device implemented using delivery device 1500, insulin delivery can be performed based on internal communications between a central computing module (e.g., a microprocessor of delivery device 1500) and an insulin delivery module (e.g., including a microcontroller, a motor, and a pump). For example, insulin delivery can be caused by the central computing module conveying a delivery command in the form of an electrical signal that travels to the insulin delivery module via a communication structure. The central computing module can also be configured to communicate with a remote or cloud computing system (e.g., a microprocessor of delivery device 1500) to communicate with the insulin delivery module. Figure 1 A computing device (e.g., a remote / cloud computing system 108) Figure 1 According to the techniques described herein, the delivery device 1500 can communicate various event data toward a remote or cloud computing system, which can communicate insulin delivery determinations toward the delivery device 1500.

[0165] The delivery device 1500 can provide insulin through a small tube 1510, which is configured to be connected to the cannula fluid inserted subcutaneously. The delivery device 1500 can be configured to deliver two types of doses: a basic dose, which can be delivered in micro-amounts periodically (e.g., every five minutes) throughout the day and night; and a bolus dose to cover the blood sugar increase caused by a meal and / or correct high blood sugar levels. In the depicted example, the delivery device 1500 includes a user interface with a button element 1520, which can be manipulated to administer insulin boluses, change therapy settings, change user preferences, and select display features, etc. The delivery device 1500 may also include a display device 1530, which can be used to present various types of information or data to the user. According to various aspects of the present disclosure, the user of the delivery device 1500 can use the button element 1520 to enter certain event data (e.g., event type, event start time, event details, etc.), and the display device 1530 can be used to confirm the user input. Fig.15 The delivery device 1500 is provided by way of example only, and other types of insulin delivery devices and other technologies than those described above are considered to be within the scope of the present disclosure.

[0166] Fig.16 16 is a block diagram of an example of an electronic device 1600 that can implement some of the examples disclosed herein. For example, the electronic device 1600 can be used to implement the delivery device 102, the monitoring device 104, the computing device 106 and the remote / cloud computing system 108, the glucose sensor subsystem 210, the controller 220, the insulin delivery subsystem 230, the glucose delivery subsystem 240 and the glucagon delivery subsystem 250, the CGM system 300, the glucose sensor device 1400, the delivery device 1500, or another device that can be used to calculate insulin dosage and / or predict future blood glucose levels. In the illustrated example, the computing device 1600 may include one or more processors 1610 and a memory 1620. The processor 1610 may be configured to execute instructions stored in the memory 1620 to perform one or more of the methods and other applications described herein. The processor 1610 may include, for example, one or more central processing units, microprocessors, microcontrollers, special-purpose processors (e.g., digital signal processors), ASICs, DSPs, FPGAs, or other processors suitable for implementation in portable electronic devices. The processor 1610 may be communicatively coupled to a number of components within the electronic device 1600. To achieve this communicatively coupling, the processor 1610 may communicate with the other illustrated components across a bus 1640. The bus 1640 may be any subsystem suitable for transmitting data within the electronic device 1600. The bus 1640 may include multiple computer buses and additional circuits for transmitting data.

[0167] The memory 1620 may include one or more transient and / or non-transient storage devices, such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), read access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash-EPROM, secure digital (SD) card, and any other memory chip or cartridge. Such storage devices may be configured to implement any suitable data storage, including but not limited to various file systems, data structures, computer-readable instructions, program modules, and the like. In some embodiments, the memory 1620 may be distributed into different hardware modules.

[0168] The memory 1620 may include an operating system 1625 loaded therein. The operating system 1625 may be operable to initiate the execution of instructions provided by the application modules 1622 to 1824 and / or manage other hardware modules 1670, as well as interface with the communication subsystem 1630 which may include one or more wired and / or wireless transceivers. The operating system 1625 may be adapted to perform other operations across the components of the electronic device 1600, including threading, virtualization, resource management, data storage control, and other similar functionality. In some embodiments, the memory 1620 may store a plurality of application modules 1622 to 1624, which may include any number of applications. Examples of applications may include insulin calculators, blood sugar level monitors or predictors, glucose level management applications, etc. The application modules 1622 to 1424 may include specific instructions to be executed by the processor 1610. In some embodiments, certain applications or portions of the application modules 1622 to 1424 may be executed by other hardware modules 1670.

[0169] The communication subsystem 1630 may include, for example, an infrared communication device, a wireless communication device, and / or a chipset (such as Device, IEEE 802.11 device, Wi-Fi device, WiMax device, cellular communication device, etc.) and / or similar communication interfaces. One or more antennas (not shown) may be used for wireless communication as part of the communication subsystem 1630 or as a separate component (e.g., a wireless charging receiver or a near field communication receiver) coupled to any portion of the electronic device 1600. In some embodiments, the communication subsystem 1630 may include circuits for wired communication technologies, such as Ethernet, coaxial communications, a universal serial bus (USB), etc. The communication subsystem 1630 may permit data to be exchanged with a network, other computer systems, and / or any other device. For example, the communication subsystem 1630 may be used to receive a therapeutic determination of the delivery of a therapeutic fluid (e.g., insulin) from a cloud computing system, such as via an intermediate computing device (e.g., a controller) that is communicatively coupled to the electronic device 1600, wherein the processor 1610 may transmit a command to the actuator controller based on the therapeutic determination to cause the delivery of an appropriate amount of therapeutic fluid (e.g., insulin) to the user. In another example, the communication subsystem 1630 may be used to communicate a measurement result (e.g., a sensor glucose level) to a computing device (e.g., a smart phone or a personal health monitoring device) and / or to a remote server via the computing device, or to receive data (e.g., calibration data, configuration data, etc.) from the computing device or the remote server via the computing device.

[0170] Sensor 1650 may include, for example, a camera, an infrared sensor, an accelerometer, a pressure sensor, a temperature sensor, a proximity sensor, a magnetometer, a gyroscope, an inertial sensor (e.g., an inertial measurement unit (IMU)), an ambient light sensor, a position sensor, a depth sensor, a gesture detector, or any other similar module operable to provide sensory output and / or receive sensory input. In one example, sensor 1650 may be used to implement a meal detector.

[0171] The input / output user interface 1660 may allow a user to transmit an action request to the electronic device 1600 to perform a specific action, and may provide information to the user (e.g., the status of the electronic device 1600, measurement results, alarms, etc.). The input / output user interface 1660 may include one or more input devices, such as a touch screen, a touch pad, a microphone, a button, a dial, a switch, a keyboard, a mouse, a game controller, or any other suitable device for receiving an action request and communicating the received action request to the processor 1610. In some embodiments, the input / output user interface 1660 may include one or more output devices, such as a display, a speaker, a light emitting device, a tactile device, etc., to provide feedback or alarms to the user.

[0172] In some embodiments, the electronic device 1600 may include multiple other hardware modules 1670. Each of the other hardware modules 1670 may be a physical module within the electronic device 1600. Although each of the other hardware modules 1670 may be permanently configured as a structure, some of the other hardware modules 1670 may be temporarily configured to perform a specific function or be temporarily activated. Examples of other hardware modules 1670 may include, for example, an audio output and / or input module (e.g., a microphone or speaker), a near field communication (NFC) module, a rechargeable battery, a battery management system, a wired / wireless battery charging system, an actuator controller, etc. In some embodiments, one or more functions of the other hardware modules 1670 may be implemented in software.

[0173] In one example, electronic device 1600 can be a part of an insulin delivery device (e.g., a pump), which can deliver rapid-acting insulin by being configured to be connected to a cannula fluid inserted subcutaneously. Electronic device 1600 can cause the delivery of two types of doses-basal doses, which can be delivered in micro-amounts periodically (e.g., every five minutes) throughout the day and night; and bolus doses, which cover the blood sugar increase caused by eating and / or correct high blood sugar levels. The insulin delivery device may include a user interface with button elements, which can be manipulated to administer insulin boluses, change therapy settings, change user preferences, and select display features, etc. The insulin delivery device may also include a display device that can be used to present various types of information or data to the user. According to various aspects of the present disclosure, the user of the insulin delivery device can use button elements to input certain event data (e.g., event type, event start time, event details, etc.), and can use a display device to confirm user input.

[0174] In various specific implementations, the above hardware and modules may be implemented on a single device or on multiple devices that can communicate with each other using wired or wireless connections. In alternative configurations, different and / or additional components may be included in the electronic device 1600. Similarly, the functionality of one or more components may be distributed among the components in a manner different from that described above.

[0175] The methods, systems and devices discussed above are examples. Various programs or components may be omitted, substituted or added to various embodiments as appropriate. For example, in an alternative configuration, the described method may be performed in a different order than described, and / or various stages may be added, omitted and / or combined. In addition, the features described with respect to certain embodiments may be combined in various other embodiments. Different aspects and elements of the embodiments may be combined in a similar manner. In addition, technology is constantly evolving, so many elements are examples, and the scope of the present disclosure is not limited to those specific examples.

[0176] Specific details are given in the description to provide a thorough understanding of the embodiments. However, embodiments may be practiced without these specific details. For example, well-known circuits, processes, systems, structures, and techniques have been shown without the necessary details to avoid blurring the embodiments. This description only provides example embodiments and is not intended to limit the scope, applicability, or configuration of the present invention. On the contrary, the foregoing description of the embodiments will provide enabling descriptions for realizing various embodiments for those skilled in the art. Various changes may be made to the functions and arrangements of the elements without departing from the spirit and scope of the present disclosure.

[0177] In addition, some embodiments are described as processes depicted as flow charts or block diagrams. Although each process can describe the operation as a sequential process, many operations can be performed in parallel or concurrently. In addition, the order of the operations can be rearranged. The process may have additional steps not included in the figure. In addition, the embodiments of these methods can be implemented by hardware, software, firmware, middleware, microcode, hardware description language or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segment used to perform the associated task can be stored in a computer-readable medium such as a storage medium. The processor can perform the associated task.

[0178] It will be apparent to those skilled in the art that substantial variations may be made depending on specific requirements. For example, customized or dedicated hardware may also be used, and / or specific elements may be implemented in hardware, software (including portable software, such as applets, etc.), or both. In addition, connections to other computing devices such as network input / output devices may be employed.

[0179] Each embodiment disclosed herein is an example of the present disclosure and can be embodied in various forms. For example, although some embodiments herein are described as separate embodiments, each of these embodiments herein can be combined with one or more of the other embodiments herein. The specific structural and functional details disclosed herein should not be construed as restrictive, but as the basis of the claims, and as a representative basis for teaching those skilled in the art to use the present disclosure differently with any appropriate detailed structure. In the entire description of the drawings, similar reference numerals may refer to similar elements.

[0180] Any of the techniques, operations, methods, programs, algorithms or codes described herein may be converted to or expressed in a programming language or computer program embodied on a computer, processor or machine-readable medium. As used herein, the terms "programming language" and "computer program" each include any language for specifying instructions for a computer or processor, and include (but are not limited to) the following languages ​​and their derivatives: assembler, Basic, Batch file, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command language, Pascal, Perl, PL1, Python, scripting language, visual Basic, meta-language self-specified programming and all first, second, third, fourth, fifth or higher generation computer languages. Also included are databases and other data models and any other meta-languages. No distinction is made between interpretation, compiled languages ​​or languages ​​that use both compilation and interpretation methods. No distinction is made between compiled and source versions of a program. Therefore, references to programs that can exist in more than one state (such as source, compiled, target or linked) of a programming language are references to any and all such states. References to procedures may cover the actual instructions and / or the intent of those instructions.

[0181] It should be understood that the foregoing description only illustrates the present disclosure. To the extent consistent, any or all aspects described in detail herein may be used in combination with any or all other aspects described in detail herein. Without departing from the present disclosure, a person skilled in the art may devise various alternatives and modifications. Therefore, the present disclosure is intended to cover all such alternatives, modifications and variations. The embodiments described with reference to the accompanying drawings are presented only to show certain examples of the present disclosure. Other elements, steps, methods and techniques that are not significantly different from those described in the above and / or attached claims are also intended to be included within the scope of the present disclosure. Although several embodiments of the present disclosure have been shown in the drawings, it is not intended to limit the present disclosure to this, because it is desired to make the present disclosure as broad as the scope allowed by the art and the present specification should be read in the same manner. Therefore, the above description should not be understood as limiting, but only as an illustration of a specific embodiment. A person skilled in the art can envision other modifications within the scope and essence of the claims attached hereto. The aspects and features of the present disclosure can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but rather as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in any appropriately detailed structure.

[0182] With reference to the accompanying drawings, the components that may include memory may include non-transient machine-readable media. The term "machine-readable medium", "processor-readable medium" or "computer-readable medium" may refer to any storage medium that participates in providing data that enables a machine to operate in a particular manner. In the embodiments provided above, various machine-readable media may involve providing instructions / codes to a processing unit and / or other devices for execution. Additionally or alternatively, machine-readable media may be used to store and / or carry such instructions / codes. In many specific implementations, computer-readable media are physical and / or tangible storage media. Such media may take a variety of forms, including but not limited to non-volatile media, volatile media and transmission media. Common forms of computer-readable media include, for example, magnetic and / or optical media, such as compact disks (CDs) or digital versatile disks (DVDs), punch cards, paper tapes, any other physical media with hole patterns, RAM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash-EPROM, any other memory chip or box, carrier waves as described below, or any other medium from which a computer can read instructions and / or codes. A computer program product may include code and / or machine executable instructions, which may represent procedures, functions, subprograms, programs, routines, applications (Apps), subroutines, modules, software packages, classes, or any combination of instructions, data structures, or program statements.

[0183] Those skilled in the art will appreciate that any of a variety of different technologies and techniques may be used to represent information and signals used to convey the messages described herein. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0184] As used herein, the terms "and" and "or" may include multiple meanings, which are also expected to depend at least in part on the context in which the terms are used. Generally, if used in an associative list (such as A, B, or C), "or" is intended to mean A, B, and C (used herein in an inclusive sense) as well as A, B, or C (used herein in an exclusive sense). In addition, the term "one or more" used herein may be used to describe any characteristic portion, structure, or characteristic of the singular, or may be used to describe some combination of features, structures, or characteristics. However, it should be noted that this is merely an illustrative example, and the subject matter claimed is not limited to this example. In addition, if used in an associative list (such as A, B, or C), the term "at least one" may be interpreted to mean A, B, C, or any combination of A, B, and / or C, such as AB, AC, BC, AA, ABC, AAB, AABBCCC, etc.

[0185] In addition, although certain embodiments have been described using a specific combination of hardware and software, it should be appreciated that other combinations of hardware and software are also possible. Certain embodiments may be implemented with hardware only, or with software only, or using a combination thereof. In one example, the software may be implemented with a computer program product that includes computer program codes or instructions that may be executed by one or more processors for performing any or all of the steps, operations, or processes described in this disclosure, wherein the computer program may be stored on a non-transient computer-readable medium. The various processes described herein may be implemented in any combination on the same processor or on different processors.

[0186] Where a device, system, component or module is described as being configured to perform certain operations or functions, such configuration may be achieved, for example, by designing electronic circuits to perform the operations, by programming programmable electronic circuits (such as microprocessors) to perform the operations (such as by executing computer instructions or code), or by a processor or core programmed to execute code or instructions stored on a non-transitory memory medium, or any combination thereof. Processes may communicate using a variety of techniques, including but not limited to conventional techniques for inter-process communication, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times.

[0187] In view of this description, implementations may include different combinations of features. Specific implementation examples are described in the following numbered clauses:

[0188] Clause 1. A processor-implemented method, the processor-implemented method comprising:

[0189] obtaining an in vivo characteristic of the glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristic comprising an in vivo sensitivity, an in vivo intercept, or a combination thereof;

[0190] receiving sensor measurement data measured by the glucose sensor, the sensor measurement data comprising sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof; and

[0191] A sensor glucose (SG) value is estimated using a SG model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

[0192] Clause 2. The processor-implemented method of clause 1, wherein the in vivo sensitivity and the in vivo intercept characterize the sensor current of the glucose sensor according to the following formula:

[0193] The relationship between (Isig) and the corresponding blood sugar level (BG):

[0194] BG = Isig / in vivo sensitivity + in vivo intercept + optional function of one or more other in vivo characteristics.

[0195] Clause 3. The processor-implemented method of clause 1 or 2, wherein the input parameters of the SG model include both the in vivo sensitivity and the in vivo intercept of the glucose sensor.

[0196] Clause 4. The processor-implemented method of any one of clauses 1 to 3, wherein both the in vivo sensitivity and the in vivo intercept of the glucose sensor are time-varying. Clause 5. The processor-implemented method of any one of clauses 1 to 4, wherein:

[0197] storing the in vivo characteristics of the glucose sensor in a memory device of the glucose sensor before inserting the glucose sensor into a subcutaneous layer of a user; and

[0198] Obtaining the in vivo characteristic of the glucose sensor comprises reading the in vivo characteristic of the glucose sensor from the memory device of the glucose sensor. Clause 6. A processor-implemented method according to any one of clauses 1 to 5, further comprising generating a derived feature based on the sensor measurement data, wherein the input parameter of the SG model comprises the derived feature.

[0199] Clause 7. A processor-implemented method according to any one of clauses 1 to 6, wherein:

[0200] The SG model includes a partition model including a plurality of regional models for corresponding regions of a plurality of regions of an input parameter space of the SG model; and

[0201] Estimating the SG value using the SG model includes selecting a regional model from the plurality of regional models based on the in vivo characteristics of the glucose sensor, the sensor measurement data, or both.

[0202] Clause 8. The processor-implemented method of Clause 7, wherein different regions of the plurality of regions of the input parameter space of the SG model correspond to different ranges of the input parameter of the SG model.

[0203] Clause 9. The processor-implemented method of Clause 8, wherein the input parameter space of the SG model is partitioned into the plurality of regions based at least in part on the in vivo characteristics of the glucose sensor.

[0204] Clause 10. A processor-implemented method according to any one of clauses 1 to 6, wherein:

[0205] The SG model includes a first partition model, the first partition model including a plurality of first regional models for corresponding regions of a plurality of first regions of an input parameter space of the SG model, wherein the input parameter space of the SG model is partitioned into the plurality of first regions based on a first partition scheme;

[0206] The SG model further includes a second partition model including a plurality of second regional models for corresponding regions of the plurality of second regions of the input parameter space of the SG model, wherein the input parameter space of the SG model is partitioned into the plurality of second regions based on a second partition scheme different from the first partition scheme; and

[0207] Estimating the SG value using the SG model includes:

[0208] generating a first SG value using a regional model selected from the plurality of first regional models based on the in vivo characteristic of the glucose sensor, the sensor measurement data, or both;

[0209] generating a second SG value using a regional model selected from the plurality of second regional models based on the in vivo characteristics of the glucose sensor, the sensor measurement data, or both; and determining a final SG value based on a combination of the first SG value and the second SG value.

[0210] Clause 11. The processor-implemented method of Clause 10, wherein at least one of the first partitioning scheme or the second partitioning scheme partitions the input parameter space based at least in part on the in vivo characteristic of the glucose sensor.

[0211] Clause 12. A processor-implemented method according to any one of clauses 1 to 11, wherein:

[0212] determining the in vivo characteristic of the glucose sensor based on the in vitro characteristic of the glucose sensor and an in vitro to in vivo transformation model; and

[0213] The in vitro characteristic of the glucose sensor is predicted based on the manufacturing process measurement data associated with the glucose sensor.

[0214] Clause 13. A processor-implemented method according to any one of clauses 1 to 12, wherein the SG model comprises one or more machine learning models, equations, functions, or a combination thereof.

[0215] Clause 14. A system comprising:

[0216] one or more processors; and

[0217] One or more processor-readable media storing instructions that, when executed by the one or more processors, cause operations including:

[0218] obtaining an in vivo characteristic of the glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristic comprising an in vivo sensitivity, an in vivo intercept, or a combination thereof;

[0219] Receive sensor measurement data measured by the glucose sensor, the sensor measurement data including sensor current (Isig), reverse voltage

[0220] (Vcntr), electrochemical impedance spectroscopy (EIS) data, the age of the glucose sensor, or a combination thereof; and

[0221] The SG value is estimated using a sensor glucose (SG) model, wherein the input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is the output of the SG model. Clause 15. The system of clause 14, wherein the in vivo sensitivity and the in vivo intercept characterize the relationship between the sensor current (Isig) of the glucose sensor and the corresponding blood glucose level (BG) according to the following formula:

[0222] BG = Isig / in vivo sensitivity + in vivo intercept + optional function of one or more other in vivo characteristics.

[0223] Clause 16. The system of Clause 14 or 15, wherein both the in vivo sensitivity and the in vivo intercept of the glucose sensor are time-varying.

[0224] Clause 17. A system according to any one of clauses 14 to 16, wherein:

[0225] storing the in vivo characteristics of the glucose sensor in a memory device of the glucose sensor before inserting the glucose sensor into a subcutaneous layer of a user; and

[0226] Obtaining the in vivo characteristic of the glucose sensor comprises reading the in vivo characteristic of the glucose sensor from the memory device of the glucose sensor. Clause 18. The system of any one of clauses 14 to 17, wherein the operations further comprise generating a derived feature based on the sensor measurement data, wherein the input parameters of the SG model comprise the derived feature.

[0227] Clause 19. A system according to any one of clauses 14 to 18, wherein:

[0228] The SG model includes a partition model including a plurality of regional models for corresponding regions of a plurality of regions of an input parameter space of the SG model; and

[0229] Estimating the SG value using the SG model includes selecting a regional model from the plurality of regional models based on the in vivo characteristics of the glucose sensor, the sensor measurement data, or both.

[0230] Clause 20. One or more non-transitory processor-readable storage media storing instructions that, when executed by one or more processors, cause performance of operations comprising:

[0231] obtaining an in vivo characteristic of the glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristic comprising an in vivo sensitivity, an in vivo intercept, or a combination thereof;

[0232] receiving sensor measurement data measured by the glucose sensor, the sensor measurement data comprising sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof; and

[0233] A sensor glucose (SG) value is estimated using a SG model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

Claims

1. A processor-implemented method, the processor-implemented method comprising: obtaining an in vivo characteristic of the glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristic comprising an in vivo sensitivity, an in vivo intercept, or a combination thereof; receiving sensor measurement data measured by the glucose sensor, the sensor measurement data including sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof; and A sensor glucose (SG) value is estimated using a SG model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

2. The processor-implemented method of claim 1 , wherein the in vivo sensitivity and the in vivo intercept characterize the relationship between a sensor current (Isig) of the glucose sensor and a corresponding blood glucose level (BG) according to the following equation: BG = Isig / in vivo sensitivity + in vivo intercept + optional function of one or more other in vivo characteristics.

3. The processor-implemented method of claim 1 or 2, wherein the input parameters of the SG model include both the in vivo sensitivity and the in vivo intercept of the glucose sensor.

4. The processor-implemented method of any one of claims 1 to 3, wherein both the in vivo sensitivity and the in vivo intercept of the glucose sensor are time-varying.

5. A processor-implemented method according to any one of claims 1 to 4, wherein: storing the in vivo characteristics of the glucose sensor in a memory device of the glucose sensor prior to inserting the glucose sensor into a subcutaneous layer of a user; as well as Obtaining the in vivo characteristic of the glucose sensor includes reading the in vivo characteristic of the glucose sensor from the memory device of the glucose sensor.

6. The processor-implemented method of any one of claims 1 to 5, further comprising generating derived features based on the sensor measurement data, wherein the input parameters of the SG model include the derived features.

7. A processor-implemented method according to any one of claims 1 to 6, wherein: The SG model includes a partition model, the partition model including a plurality of regional models for corresponding regions of a plurality of regions of an input parameter space of the SG model; as well as Estimating the SG value using the SG model includes selecting a regional model from the plurality of regional models based on the in vivo characteristics of the glucose sensor, the sensor measurement data, or both.

8. The processor-implemented method of claim 7, wherein different ones of the plurality of regions of the input parameter space of the SG model correspond to different ranges of the input parameters of the SG model.

9. The processor-implemented method of claim 8, wherein the input parameter space of the SG model is partitioned into the plurality of regions based at least in part on the in vivo characteristics of the glucose sensor.

10. The processor-implemented method of any one of claims 1 to 6, wherein: The SG model includes a first partition model, the first partition model including a plurality of first regional models for corresponding regions of a plurality of first regions of an input parameter space of the SG model, wherein the input parameter space of the SG model is partitioned into the plurality of first regions based on a first partition scheme; The SG model further includes a second partition model, the second partition model including a plurality of second regional models for respective regions of a plurality of second regions of the input parameter space of the SG model, wherein the input parameter space of the SG model is partitioned into the plurality of second regions based on a second partition scheme different from the first partition scheme; as well as Estimating the SG value using the SG model includes: generating a first SG value using a regional model selected from the plurality of first regional models based on the in vivo characteristic of the glucose sensor, the sensor measurement data, or both; generating a second SG value using a regional model selected from the plurality of second regional models based on the in vivo characteristics of the glucose sensor, the sensor measurement data, or both; and A final SG value is determined based on a combination of the first SG value and the second SG value.

11. The processor-implemented method of claim 10, wherein at least one of the first partitioning scheme or the second partitioning scheme partitions the input parameter space based at least in part on the in vivo characteristic of the glucose sensor.

12. A processor-implemented method according to any one of claims 1 to 11, wherein: determining the in vivo characteristics of the glucose sensor based on the in vitro characteristics of the glucose sensor and an in vitro to in vivo transformation model; and The in vitro property of the glucose sensor is predicted based on the manufacturing process measurement data associated with the glucose sensor.

13. A processor-implemented method according to any one of claims 1 to 12, wherein the SG model comprises one or more machine learning models, equations, functions, or a combination thereof.

14. One or more non-transitory processor-readable storage media storing instructions that, when executed by one or more processors, cause operations including: obtaining an in vivo characteristic of the glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristic comprising an in vivo sensitivity, an in vivo intercept, or a combination thereof; receiving sensor measurement data measured by the glucose sensor, the sensor measurement data comprising sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof; and A sensor glucose (SG) value is estimated using a SG model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

15. A system, comprising: one or more processors; and One or more processor-readable media storing instructions that, when executed by the one or more processors, cause operations including: obtaining an in vivo characteristic of the glucose sensor predicted using manufacturing process measurement data associated with the glucose sensor, the in vivo characteristic comprising an in vivo sensitivity, an in vivo intercept, or a combination thereof; receiving sensor measurement data measured by the glucose sensor, the sensor measurement data comprising sensor current (Isig), reverse voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, or a combination thereof; and A sensor glucose (SG) value is estimated using a SG model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

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