Method and apparatus for information aggregation, error detection, and analyte concentration determination during continuous analyte sensing.

Abstract

To provide methods for information gathering and / or error correction for CGM glucose concentration determination.SOLUTION: A continuous glucose monitoring (CGM) device may include a wearable portion having a sensor configured to produce glucose signals from interstitial fluid, a processor, a memory and a transmitter circuitry. The memory may include a computer program code stored therein that, when executed by the processor, causes the CGM device to (a) measure and store a plurality of glucose signals using the sensor and memory; (b) for a presently-measured glucose signal, employ the plurality of presently-measured glucose signals stored in the memory and the pre-determined gain function to compute a compensated glucose value; and (c) communicate the compensated glucose value to a user of the CGM device.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] Related applications This application claims priority and interest to U.S. Provisional Patent Application No. 62 / 898,507, filed on September 10, 2019, entitled “METHODS AND APPARATUS FOR INFORMATION GATHERING, ERROR DETECTION AND ANALYTE CONCENTRATION DETERMINATION DURING CONTINUOUS ANALYTE SENSING,” which is incorporated herein by reference in its entirety for all purposes.

[0002] This disclosure generally relates to determining the concentration of an analyte in an analyte-containing fluid using continuous analyte sensing. [Background technology]

[0003] Continuous analyte sensing in vivo and / or in vitro samples, such as continuous glucose monitoring (CGM), has become a routine detection operation, particularly in diabetes care. Providing real-time glucose concentrations allows for timely application of therapeutic / clinical actions and better control of blood glucose levels.

[0004] During continuous glucose monitoring (CGM) operation, the biosensor is typically inserted subcutaneously and operates continuously in an environment surrounded by tissue and interstitial fluid (ISF). The biosensor can operate continuously at a constant potential relative to a reference electrode, such as an Ag / AgCl electrode or a composite reference counter electrode. The biosensor may also operate with two working electrodes, one dedicated to measuring the analyte of interest, such as glucose, by a glucose-specific enzyme such as glucose oxidase. The other electrode is dedicated to measuring background signals resulting from interfering species such as uric acid, acetaminophen, or similar substances. In this dual-electrode operation scheme, the interfering signals can be continuously subtracted from the main signal of the analyte of interest by either a simple subtraction method or another algorithmic method.

[0005] Optical sensors can also be used for continuous glucose monitoring using fluorescence, absorbance, reflectance, and / or similar methods. For example, optical oxygen sensors that rely on fluorescence or fluorescence quenching have been used to indirectly measure glucose by measuring the oxygen concentration in an ISF, which has an inverse relationship with glucose concentration. (See, for example, Stein et al., "Microscale Enzymatic Optical Biosensors using Mass-Transport Limiting Nanofilms.1. Fabrication and Characterization using Glucose as a Model Analyte," Anal Chem, Author Manuscript, 2008, www.ncbi.nlm.nih.gov / pmc / articles / PMC2518633 / )

[0006] To determine the accuracy of an analytical method in order to measure the concentration of an analyte in a sample, a reference concentration may be used. For a biosensor deployed subcutaneously and exposed to interstitial fluid, the defined glucose signal responds to ISF glucose. However, directly determining the ISF glucose concentration is difficult because ISF samples are not readily available for reference ISF glucose measurement. Furthermore, the relevant therapeutic effects based on blood glucose status depend on capillary glucose delivered to cells through the capillary system.

[0007] As is known, ISF glucose lags behind capillary glucose by approximately 5–15 minutes, depending on whether the biological system is fasting or in the glucose conversion phase. Therefore, considering the time delay, ISF glucose can only function as an indicator of capillary glucose, and it is difficult to provide accurate predictions of capillary glucose using CGM biosensors. Furthermore, signal noise due to system calibration (in-situ calibration or factory calibration), tissue effects on the biosensor membrane, and changes in sensitivity over time due to other known factors makes the ISF glucose timing profile relative to capillary glucose even more difficult to define.

[0008] One conventional method to reduce the glucose delay in ISF and thus increase the accuracy of CGM is by filtering. Another method is so-called delay correction, which is used to compensate for the delay by comparing the estimated glucose value with the measured glucose value. However, due to the errors related to ISF glucose measurement caused by the factors mentioned above, filtering or delay correction methods may prove to be of little significance.

[0009] Improved CGM methods and devices are desired. [Overview of the Initiative]

[0010] In some embodiments, a method for fabricating a continuous glucose monitoring (CGM) device includes: (1) creating a gain function based on a plurality of sensor progress parameters of a glucose signal measured by a CGM sensor, wherein each sensor progress parameter is based on the glucose signal at a point of interest and glucose signals measured prior to the glucose signal at the point of interest; (2) providing a CGM device including a sensor, memory, and a processor; (3) storing the gain function in the memory of the CGM device; and (4) storing computer program code in the memory of the CGM device, the computer program code, when executed by the processor, causes the CGM device to (a) measure and store a plurality of glucose signals using the sensor and memory of the CGM device; (b) calculate a plurality of sensor progress parameters for the currently measured glucose signal based on the currently measured glucose signal and a plurality of previously measured glucose signals stored in memory; (c) calculate a corrected glucose value using the plurality of sensor progress parameters and the stored gain function; and (d) communicate the corrected glucose value to the user of the CGM device.

[0011] In some embodiments, a continuous glucose monitoring (CGM) device includes a wearable portion having a sensor configured to produce a glucose signal from interstitial fluid, a processor, a memory coupled to the processor, and a transmitter circuit coupled to the processor. The memory includes a gain function based on a plurality of sensor progression parameters of the glucose signal, each sensor progression parameter based on the glucose signal at a point of interest and glucose signals measured prior to the glucose signal at the point of interest. The memory also includes computer program code stored in the memory, which, when executed by the processor, causes the CGM device to (a) measure and store a plurality of glucose signals using the sensor and memory of the wearable portion, (b) calculate a plurality of sensor progression parameters for the currently measured glucose signal based on the currently measured glucose signal and a plurality of previously measured glucose signals stored in the memory, (c) calculate a corrected glucose value using the plurality of sensor progression parameters and the stored gain function, and (d) communicate the corrected glucose value to the user of the CGM device.

[0012] In some embodiments, a continuous glucose monitoring (CGM) device includes a wearable portion having a sensor configured to produce a glucose signal from interstitial fluid, a current sensing circuit coupled to the sensor and configured to measure the glucose signal produced by the sensor, and a transmitter circuit configured to transmit the measured glucose signal. The CGM device also includes a portable user device having memory, a processor, and a receiver circuit configured to receive glucose signals from the wearable portion. The memory includes a gain function based on a plurality of sensor progression parameters of the glucose signal, each sensor progression parameter based on the glucose signal at point of interest and glucose signals measured prior to the glucose signal at point of interest. The memory contains computer program code stored in the memory, which, when executed by the processor, causes the CGM device to (a) acquire and store multiple glucose signals using the sensors of the wearable portion and the memory of the portable user device; (b) calculate multiple sensor progress parameters for the currently measured glucose signal based on the currently measured glucose signal and multiple previously measured glucose signals stored in memory; (c) calculate a corrected glucose value using the multiple sensor progress parameters and the stored gain function; and (d) communicate the corrected glucose value to the user of the CGM device.

[0013] In some embodiments, a method for correcting errors during continuous glucose monitoring (CGM) measurement includes: (a) providing a CGM device comprising a sensor, memory, and a processor, wherein the CGM device has a gain function stored in memory, the gain function being based on a plurality of sensor progress parameters of a glucose signal, each sensor progress parameter being based on a glucose signal at a point of interest and glucose signals measured prior to the glucose signal at the point of interest; (b) measuring and storing a plurality of glucose signals using the sensor and memory; (c) calculating a plurality of sensor progress parameters for a currently measured glucose signal based on the currently measured glucose signal and a plurality of previously measured glucose signals stored in memory; (d) calculating a corrected glucose value using the plurality of sensor progress parameters and the stored gain function; and (e) communicating the corrected glucose value to the user of the CGM device.

[0014] In some embodiments, a method for determining the analyte concentration during continuous monitoring measurement includes: (a) subcutaneous insertion of a biosensor into a target, wherein the biosensor includes a counter electrode, a reference electrode, and a working electrode having a chemical composition configured to oxidize the analyte at point of interest; (b) applying a constant voltage to the working electrode having the chemical composition to generate a continuous current from the working electrode; (c) sensing and storing in memory a working electrode current signal from the working electrode; (d) aggregating the working electrode current signal at point of interest and a portion of the working electrode current signal stored in memory that was measured prior to the working electrode current signal at point of interest; (e) generating a gain function value from a predetermined gain function using the working electrode current signal at point of interest and the portion of the working electrode current signal aggregated from memory; (f) correcting the system gain using the gain function value generated from the predetermined gain function; and (g) determining the analyte concentration of the working electrode current signal at point of interest based on the corrected system gain and the working electrode current signal at point of interest.

[0015] In some embodiments, a continuous analytic monitoring (CAM) device is provided, comprising a biosensor configured to be subcutaneously inserted into a subject, wherein the biosensor includes a counter electrode, a reference electrode, and a working electrode having a chemical composition configured to oxidize an analytic at point of interest and generate an analytic signal from interstitial fluid; a processor; a memory coupled to the processor; and a transmitter circuit coupled to the processor. The memory includes a predetermined gain function based on the analytic signal at point of interest and analytic signals measured prior to the analytic signal at point of interest. The memory contains computer program code stored in the memory, which, when executed by the processor, causes the CAM device to (a) apply a constant voltage to an operating electrode having a chemical composition, thereby generating a continuous current from the operating electrode; (b) sense the operating electrode current signal from the operating electrode and store it in the memory; (c) aggregate the operating electrode current signal at the point of interest and a portion of the operating electrode current signal stored in the memory that was measured before the operating electrode current signal at the point of interest; (d) generate a gain function value from a gain function using the operating electrode current signal at the point of interest and the portion of the operating electrode current signal aggregated from the memory; (e) modify the system gain using the gain function value generated from a predetermined gain function; and (f) determine the analyte concentration of the operating electrode current signal at the point of interest based on the modified system gain and the operating electrode current signal at the point of interest.

[0016] In some embodiments, a method for fabricating a continuous analyte monitoring device includes: (a) operably coupling an analyte sensor with a host for use during a continuous analyte monitoring process; (b) continuously recording an analyte signal during the continuous analyte monitoring process; (c) recording a reference analyte concentration during the continuous analyte monitoring process; (d) establishing data pairings between the analyte signal and the reference analyte concentration; (e) calculating a relative analyte error referenced with respect to the reference analyte concentration; (f) calculating sensor progress parameters by aggregating sensor progress information and referencing analyte data points of interest to previously measured analyte data points; (g) performing statistical analysis by setting at least one of the relative analyte error referenced with respect to the reference analyte concentration and the relative gain error referenced with respect to a reference gain as targets for statistical analysis, and by setting the sensor progress parameters as input variables to obtain a gain function; and (h) recording a gain function, including selected sensor progress parameters and their weighting coefficients, as a factory calibration component to be stored in the continuous analyte monitoring device.

[0017] Further features, aspects, and advantages of the embodiments of this disclosure will become more fully apparent from the embodiments for carrying out the invention described below, the concluding claims, and the accompanying drawings, by illustrating numerous exemplary embodiments and implementations. The various embodiments of this disclosure may also take on other different uses, and some of their details may be modified in various ways without departing from the spirit and scope of the claims. Accordingly, the drawings and description should be considered illustrative in nature and not limiting. The drawings are not necessarily drawn to scale. [Brief explanation of the drawing]

[0018] [Figure 1A]This illustrates the reference of current data points to past data points in the collection of glucose signals acquired over a 3-hour period, and the use of the collected glucose signals to calculate sensor progress parameters such as ratios, according to embodiments described herein. [Figure 1B] This illustrates the reference of current data points to past data points in the collection of glucose signals acquired over a 12-hour period, and the use of the collected glucose signals to calculate sensor progress parameters such as ratios, according to embodiments described herein. [Figure 1C] The table below shows exemplary ratios of the operating electrode current Iw, background electrode current Ib, and Iw-Ib current difference of the CGM sensor, using previous data points measured up to one hour prior to the last data point (acquired at time = 1 hour) according to the embodiments provided herein. [Figure 1D] The table below shows exemplary ratios of the operating electrode current Iw, background electrode current Ib, and Iw-Ib current difference of the CGM sensor, using previous data points measured up to one hour prior to the last data point (acquired at time = 1 hour) according to the embodiments provided herein. [Figure 1E] The table below shows exemplary ratios of the operating electrode current Iw, background electrode current Ib, and Iw-Ib current difference of the CGM sensor, using previous data points measured up to one hour prior to the last data point (acquired at time = 1 hour) according to the embodiments provided herein. [Figure 1F] This specification shows a graph illustrating the ratio of elapsed time to an example of operating electrode current signals captured over a period of 50 hours according to embodiments provided herein. [Figure 2] The normalized gain-versus-time graphs illustrating an example of a series of in-situ calibrations for two CGM sensors (sensor 1 and sensor 2) according to embodiments provided herein are shown. [Figure 3A]This specification shows graphs of glucose versus time as measured using BGM (capillary glucose) and CGM according to embodiments provided herein. [Figure 3B] The graphs of ΔG / G (or ΔGain / Gain) versus gain functions derived from multivariate regression from clinical trial datasets according to embodiments provided herein are shown. [Figure 4A] The following are illustrative gain functions (referred to as gain function 1, gain function 2, and gain function 3 in Figures 4A-C) for the segment shown in Figure 2, according to embodiments described herein. [Figure 4B] The following are illustrative gain functions (referred to as gain function 1, gain function 2, and gain function 3 in Figures 4A-C) for the segment shown in Figure 2, according to embodiments described herein. [Figure 4C] The following are illustrative gain functions (referred to as gain function 1, gain function 2, and gain function 3 in Figures 4A-C) for the segment shown in Figure 2, according to embodiments described herein. [Figure 4D] The following are the respective lists of the gain function ratios and cross-term definitions for Figures 4A, 4B, and 4C, respectively, according to the embodiments described herein. [Figure 4E] The following are the respective lists of the gain function ratios and cross-term definitions for Figures 4A, 4B, and 4C, respectively, according to the embodiments described herein. [Figure 4F] The following are the respective lists of the gain function ratios and cross-term definitions for Figures 4A, 4B, and 4C, respectively, according to the embodiments described herein. [Figure 5A] The following are illustrative consensus error grid plots for raw and corrected glucose values ​​of a CGM sensor, respectively, according to embodiments provided herein. [Figure 5B] The following are illustrative consensus error grid plots for raw and corrected glucose values ​​of a CGM sensor, respectively, according to embodiments provided herein. [Figure 6A] The embodiments provided herein show the BGM glucose value, corrected CGM glucose value (GComp), and uncorrected CGM glucose value (GRaw) for the time of the first CGM sensor (sensor 1 in Figure 6A) and the second CGM sensor (sensor 2 in Figure 6B). [Figure 6B] The embodiments provided herein show the BGM glucose value, corrected CGM glucose value (GComp), and uncorrected CGM glucose value (GRaw) for the time of the first CGM sensor (sensor 1 in Figure 6A) and the second CGM sensor (sensor 2 in Figure 6B). [Figure 7A] This specification shows a high-level block diagram of an exemplary CGM device according to embodiments provided herein. [Figure 7B] This specification shows a high-level block diagram of another exemplary CGM device according to embodiments provided herein. [Figure 8] This is a schematic side view of an exemplary glucose sensor according to embodiments provided herein. [Figure 9] This is a flowchart illustrating an exemplary method for fabricating a CGM device according to embodiments provided herein. [Figure 10] This is a flowchart illustrating an exemplary method for determining glucose concentration during continuous glucose monitoring measurement, according to embodiments provided herein. [Figure 11] This specification provides an example of a method for determining the concentration of an analyte during continuous monitoring measurements using a biosensor subcutaneously inserted into a subject, according to embodiments provided herein. [Figure 12] This specification provides another exemplary method for fabricating a continuous analyte monitoring device according to the embodiments provided herein. [Figure 13] This is a graph of exemplary CGM response currents paired with reference values ​​from a blood glucose meter, according to embodiments provided herein. [Figure 14]This is a graph illustrating exemplary reference glucose values ​​against glucose current during a CGM process, according to embodiments provided herein. [Modes for carrying out the invention]

[0019] overview To more closely monitor a person's glucose levels and detect shifts in glucose levels, continuous glucose monitoring (CGM) methods and devices have been developed. While CGM systems generate a glucose signal "continuously" during operation, such as through continuous electrochemical and / or optical signals, measurements of the generated glucose signal are typically not truly continuous, but performed every few minutes. CGM systems, having implantable and non-implantable parts, can be worn for several days before removal and replacement. A CGM system may include a sensor portion inserted so as to be located beneath the skin, and a non-implantable processing portion adhered to the outer surface of the skin, e.g., the abdomen or the back of the upper arm. Unlike blood glucose monitoring (BGM) systems, which measure glucose concentration in the blood, CGM systems measure glucose concentration in interstitial fluid or indirect capillary blood samples.

[0020] A CGM system can provide frequent measurements of a person's glucose levels, each of which may not require the collection of a blood sample, such as using a fingertip stick. CGM systems may still occasionally employ the use of a BGM system, such as the Contour NEXT One® from Ascensia Diabetes Care AG in Basel, Switzerland, for calibration of the CGM system.

[0021] As mentioned above, during continuous glucose monitoring (CGM), the biosensor may operate continuously at a constant potential relative to a reference electrode or a combined reference counter electrode. Because potential pulses for each data point can destabilize the resulting glucose signal and lead to a decrease in signal quality, methods equivalent to gated amperometry used in the field of background glucose monitoring (BGM) are not employed during CGM. Therefore, there is a lack of readily available meaningful information to support glucose measurement through algorithmic methods during CGM.

[0022] Within the BGM testing field, the gated amperometry method described in U.S. Patent Publication 2013 / 0256156, titled "Gated Amperometry Methods," applies a bias voltage to a test strip, and a group of signal / data points is measured in response to the applied bias for the final analyte determination. The segmented signal processing method for optical and electrochemical sensors described in U.S. Patent Publication 2013 / 0071869, titled "Analysis Compensation Including Segmented Signals," uses data points within a single process together to provide information for endpoint analyte determination. These patent publications describe individual tests in transient processes where one analyte determination is independent of all other analyte determinations. Therefore, the group of signal / data points from a single sensor test is used solely for the purpose of providing a single glucose measurement / analyte determination for the test strip / cartridge. Subsequent glucose / analyzer measurements each depend on a new group of data points (and a new test strip / cartridge).

[0023] In contrast, during a continuous glucose monitoring process, according to the embodiments described herein, each measured signal / data point is its own endpoint for analyte determination in the data continuum, but is also related to its adjacent data points in short-term and / or long-term relationships. According to the embodiments provided herein, due to the continuous nature of CGM, previous signal / data points may contain information related to subsequently measured signal / data points. That is, each data point may be related to its adjacent (e.g., previous) data points, or even data points acquired much earlier. The relationship of the current (point of interest) data point to many of the previously measured data points in the continuum has been found to contain sensor error and / or status information (referred to herein as "sensor progress information"). In some embodiments, previous data points may be sources of information suggesting the source of sensor error or sensor status. Parameters related to the current data point or data point of interest and the previously measured data points are referred herein as sensor progress parameters (SPPs).

[0024] As described herein, sensor progression parameters in CGM or other continuous analyte monitoring methods may be determined by referencing the current analyte signal to previously measured analyte signals in the data continuum in the form of ratios, differences, relative differences, and / or similar. According to one or more embodiments described herein, methods for information aggregation and / or error correction for CGM glucose concentration determination are provided. In some embodiments, the method may include generating a gain function value using previously measured glucose signals from a CGM sensor along with the CGM glucose signal at the current point of interest. The gain function value may be used to adjust for errors in the gain generated from the gain function (using SPP) and used to determine the glucose concentration from the glucose signal at the point of interest, as well as errors due to ISF glucose delay. In some embodiments, if each and all glucose readings in the CGM data continuum are determined point by point by correcting and / or reducing errors from signal deviation and ISF delay, there will be little or no ISF delay relative to a reference glucose profile.

[0025] For example, the gain function value may be determined from a gain function using sensor progression parameters (SPP), which is calculated from the current point of interest glucose signal and previously measured glucose signals (e.g., using ratios, differences, etc., as described below). That is, the gain function may be a function of SPP (i.e., gain function = f(SPP)). For example, the operating electrode current signal and / or background current signals from a biosensor (e.g., a CGM sensor) may be periodically sampled and stored in memory. For subsequent point of interest glucose signals (e.g., operating electrode current signals), the stored current signals can be used together with the point of interest glucose signals to calculate the gain function value from the gain function. The gain function value can then be used during the calculation of the glucose concentration of the point of interest glucose signal to reduce errors in the calculated glucose concentration of the point of interest glucose signal (e.g., by adjusting the system gain using the gain function value).

[0026] As will be further explained below, the gain function may be determined using statistical techniques such as multivariate regression. In some embodiments, the gain function is predetermined by the CGM device manufacturer and stored in the CGM device's memory for use during glucose monitoring with the CGM device.

[0027] In one or more embodiments, the method may include generating a set of sensor progression parameters by taking the ratio of the glucose signal from the current data point to the glucose signal from a previous data point. These ratios, and / or combinations of these ratios and / or other related terms, may be employed within a predetermined gain function that enables error correction for error sources in the CGM glucose signal, such as gain change over time and ISF delay. This can increase the accuracy of the CGM and / or support therapeutic measures taken in response to the CGM glucose measurement. Biosensor systems according to these and other embodiments are provided.

[0028] Although the description primarily concerns glucose concentration measurement during continuous glucose monitoring, it will be understood that the embodiments described herein may be used in conjunction with other continuous analyte monitoring systems (e.g., cholesterol, lactic acid, uric acid, alcohol, or other analyte monitoring systems).

[0029] For example, one or more gain functions can be developed that include a selected group of sensor progression parameters, such as ratios based on glucose signals taken at different times, combinations of such ratios, and other cross-terms in linear combinations. The cross-terms are, for example, the initial glucose (G RAW This may include the term SPP (e.g., ratio, difference, etc.) in relation to normalized gain, background / interference signal, motion parameter, temperature value, different ratios, and / or other types of parameters. Nonlinear combinations may also be used. The sample gain function can take the following forms: (1)Gain Function=c1*R_t1+c2*R_t2+c3*R_t3+c4*R_t4+...c n *R_tn In the formula, c1, c2, c3, c4...c n, the weighting factors and R_t1, R_t2, R_t3, R_t4...R_tn are ratios of glucose data points taken at different time points (e.g., the "point of interest" glucose data point divided by the glucose data point measured and / or sensed before the glucose data point at the point of interest), combinations of ratios of glucose data points, or other cross terms. Accordingly, the gain function is representative of the relative error in gain and / or relative error in glucose, and is derived from information aggregated in the form of sensor progression parameters from the current (point of interest) glucose signal and previously measured glucose signals (in some embodiments, for example, glucose signals measured from a few minutes before the current glucose signal up to 12 hours or more before the current glucose signal). More specifically, in some embodiments, the gain function can be derived from sensor progression parameters in the form of the ratio, difference and / or relative difference of the current glucose signal to past glucose signals, and their cross terms with each other and / or with other parameters, via multivariate regression or another statistical analysis technique. In some embodiments, the gain function may be based on and / or include tens or hundreds (or more) of sensor progression parameters such as ratios, differences, relative differences, and / or cross terms. Exemplary gain functions and methods for determining such gain functions are described below.

[0030] According to the embodiments provided herein, the raw or uncorrected glucose signal Signal Raw error can be corrected and / or otherwise corrected by using a sensor progression parameter that references the current data point to past data points in the gain function. For example, in some embodiments, the corrected glucose signal G Comp can be calculated as follows: (2)G Comp =Signal Raw *Gain*(1 / (1+Gain Function)) wherein Gain is, for example, a calibration glucose value from a blood glucose meter (G BGM ) versus the CGM sensor current (Signal CGMDividing by (Gain=G BGM / Signal CGM This represents the (system) gain determined from in-situ calibration, such as by [specific method / method].

[0031] In some embodiments, one or more gain functions may be determined and stored in the memory of the CGM device, such as a wearable or other part of the CGM device, and used to calculate a corrected glucose value based on sensor progress parameters such as the glucose signal ratio (and / or other relationship) and the uncorrected glucose signal measured by the interstitial CGM sensor.

[0032] Traditionally, BGM accuracy has been expressed as a percentage within the ±x% precision limit, such as ±20%, ±15%, or ±10%, and as the percentage difference between the BGM glucose value and the reference glucose value (100% * [G BGM -G Ref ] / G Ref This represents the percentage of data points in the sample population that fall within a certain precision limit. The smaller the precision limit, the higher the precision.

[0033] For CGM glucose determination, the measurement accuracy can be defined by the following mean absolute relative difference (MARD): (3) MARD = Σ[Abs([G CGM -G REF ] / G REF )] / n) In the formula, G CGM This is the glucose value measured by CGM, G REF∫ is, for example, a reference glucose value measured by BGM, and n is the number of data points. The expression of MARD combines the mean and standard deviation of the sample population relative to the reference glucose value to produce a composite MARD value, where a smaller MARD value indicates better accuracy. Although the BGM rule for accuracy is not used to evaluate error within a specific accuracy limit, it can be estimated that the intrinsic accuracy, expressed as a percentage within ±x% accuracy limit, is about 2.5 times the MARD value, depending on the mean and standard deviation of errors in the data population. Thus, a MARD value of 10% may have an approximate accuracy of ±25% of the data, or an approximate accuracy of 25%. Conversely, a BGM system with an accuracy of ±10% is predicted to have a MARD value of 4%. Embodiments described herein may enable a reduction in the MARD value of a CGM device (e.g., to about 7-10% or less in some embodiments).

[0034] According to embodiments provided herein, a sensor progress parameter referencing a previously measured data point from a current (point of interest) data point may be expressed as a ratio of the signal from the current data point to the signal from the previously measured data point. This may form a network of information embedded in the sensor progress parameter, which is then used to calculate the CGM glucose value of the current data point for improved accuracy. The ratio formed from the current or present data point and the previous data point may, for convenience, be referred to herein as the “current-past ratio”. The current-past ratio may be calculated for the operating electrode current Iw, background current Ib, and Iw-Ib, or for optical signals such as fluorescence, absorbance and / or reflectance signals, and / or similar.

[0035] Figure 1A illustrates the reference of the current data point to past data points in the collection of glucose signals acquired over a 3-hour period, and the use of the collected glucose signals to calculate sensor progression parameters such as ratios, according to embodiments described herein. Referring to Figure 1A, past glucose signal data points measured 3 hours, 2 hours, 1 hour, 9 minutes, 6 minutes, and 3 minutes prior to the currently measured glucose signal data point are shown (indicated by circles above R_3hr, R_2hr, R_1hr, R_9min, R_6min, and R_3min, respectively). Using these past data points, a ratio to the current data point can be calculated (referred to as R_3hr, R_2hr, R_1hr, R_9min, R_6min, and R_3min in Figure 1A). Ratios of other numbers may be calculated, and / or time increments may be used. Ratios to future data points can similarly be calculated as CGM sensing progresses during CGM operation. As shown in Figure 1B as R_4hr, R_8hr, and R_12hr respectively, ratios can be used that utilize even "older" data points, such as data points acquired 4, 8, or even 12 hours earlier. A longer or shorter range of past data points may also be used.

[0036] An example of present-past comparison is shown below, where Iw t This represents the current data point at time t (time at the point of interest) of the operating electrode current, and Iw t-xmin This represents a past data point at time t-xmin where the operating electrode current was measured x minutes prior to the current data point. For example, the current-to-past ratio R_3min of the operating electrode current, based on the current operating electrode current and the operating electrode current 3 minutes ago, is: (4) R_3min=Iw t / Iw t-3min That is the case. In this particular case, data points are acquired regularly at 3-minute intervals. Ratios over longer periods may be based on time that is a multiple of 3 minutes. For example, the current-to-past ratios for the operating electrode current are given by equations (5) to (9) below, for 6 minutes, 9 minutes, 1 hour, 3 hours, and 12 hours earlier than the operating electrode current at the current point of interest. (5) R_6min=Iw t / Iw t-6min (6) R_9min=Iw t / Iw t-9min (7) R_1hr = Iw t / Iw t-1hr (8) R_3hr = Iw t / Iw t-3hr (9) R_12hr=Iw t / Iw t-12hr Other measurement intervals may be used. For example, if the data acquisition rate is based on measurements taken every 5, 10, or 15 minutes, the current-to-past ratio may be a multiple of 5, 10, or 15 minutes. Similar ratios may be determined for the background current Ib, the current difference between the working electrode and the background current, or similar, as shown by equations (10) to (17) below. (10) R_6min=Ib t / Ib t-6min (11) R_9min=Ib t / Ib t-9min (12) R_1h = Ib t / Ib t-1hr (13) R_3h = Ib t / Ib t-3hr (14) R_6min=(Iw t -Ib t ) / (Iw t-6min -Ib t-6min ) (15) R_9min=(Iw t -Ib t ) / (Iw t-9min -Ib t-9min) (16) R_1h = (Iw t -Ib t ) / (Iw t-1hr -Ib t-1hr ) (17) R_3h=(Iw t -Ib t ) / (Iw t-3hr -Ib t-3hr )

[0037] Crossterms, including ratios and other parameter combinations, and / or combinations of multiple ratios, may also be determined as further described below. Thus, for each measured data point, there is a set of parameters associated with the measured data point, which may be obtained using previous data points. As described above, for the sensor progress parameter, the current or “point of interest” glucose signal may be referenced to past glucose signals measured 6, 8, 10, or even 12 hours or more before the glucose signal at the point of interest is measured. In some embodiments, the sensor progress parameter may be calculated in terms of a ratio, difference, or other relationship between the current (point of interest) glucose signal and a previously measured glucose signal, where the signal may be an electrochemical current or an optical signal such as fluorescence, absorbance, or reflectance.

[0038] In some cases, a warm-up period (e.g., a 3-hour warm-up period or a shorter or longer warm-up period) may be adopted after the CGM sensor is inserted into the patient. In such cases, there may be a period of several hours (e.g., 3 hours or a longer warm-up period) during which only data points collected during the warm-up period may be acquired. After the warm-up period, as more data points are collected, ratios or other sensor progress parameters may be calculated based on increasingly older data points (e.g., 4 hours, 5 hours, 6 hours, etc.). In some embodiments, sensor progress parameters may be calculated using the current glucose signal and past glucose signals measured up to 12 hours prior. Other cutoff points (e.g., longer or shorter than 12 hours) may be used.

[0039] Figures 1C, 1D, and 1E show Tables 100a, 100b, and 110c, respectively, of exemplary ratios of the operating electrode current Iw, background electrode current Ib, and Iw-Ib current difference of a CGM sensor, using previous data points measured up to one hour prior to the last data point (acquired at time = 1 hour), according to embodiments provided herein. The labeling of the ratios in Table 100a (e.g., w_3min, w_6min, etc.) indicates that the ratio is derived from the operating electrode (e.g., enzyme electrode) current Iw. The labeling of the ratios in Table 100b indicates that the ratio is derived from the background current Ib, and the labeling of the ratios in Table 100c indicates that the ratio is derived from the Iw-Ib current values. For each row, the ratio is calculated by dividing the leftmost data point by the previously measured data point. For example, the most recent Iw, Ib, or Iw-Ib signal (acquired at time = 1 hour) is divided by the current signal measured previously (at 3-minute intervals, returning to time = 0). As seen in Tables 100a, 100b, and 100c, numerous ratios can be developed for each measured glucose signal based on previously measured glucose signals.

[0040] Different sensor progression parameters contain different information for the current signal, based on the specific previously measured signals used. For example, Figure 1F shows an example of ratio vs. elapsed time graph of operating electrode current signals captured over 50 hours, calculated by dividing each operating electrode current signal by operating electrode current signals measured 3 minutes, 30 minutes, and 2 hours earlier (w_3min, w_30min, and w_2hr, respectively). In this plot, different ratios at any given time point t have different magnitudes and result in different temporal profiles of the sensor progression parameters. As shown in Figure 1F, at each time point there is a different ratio that represents different information from previous data points, which can be used to improve the accuracy of glucose measurements (as described below).

[0041] As mentioned above, there are two major sources of error during CGM measurement: signal error and ISF glucose delay. The first source of error, signal error, can result from changes in sensitivity over time, or even within the calibration period. This is illustrated in Figure 2, which shows a normalized gain versus time graph 200 of an example of a series of in-situ CGM calibrations for two CGM sensors (sensor 1 and sensor 2) according to embodiments provided herein. Specifically, each plateau or horizontal region, such as plateau 202 in Figure 2, represents a normalized gain calculated by dividing the BGM glucose value determined using a blood glucose meter by the glucose signal (e.g., Iw or Iw-Ib) of the CGM sensor (sensor 1 or sensor 2), and then dividing this gain by the initial gain (referred to as Gain_1). For example, according to equation (18) below, (18) Gain = G Ref-cal / Signal cal And, In the formula, G Ref-cal This is the reference glucose value from a blood glucose meter, and Signal cal This is the raw glucose signal measured from the CGM sensor (e.g., operating electrode current, operating electrode current minus background electrode current, or similar). Gain_1 is the initial gain of the CGM sensor, (19) Gain_1=G Ref-cal_1 / Signal cal_1 And, As a result, the normalized gain, Gain / Gain_1, (20)Gain / Gain_1=(G Ref-cal / Signal cal ) / (G Ref-cal_1 / Signal cal_1 )

[0042] Gain (also called system gain) is defined similarly to electronic gain and has the physical dimension of [concentration / signal]. Therefore, if the BGM concentration is [mg / dL] and the sensor current signal is [nanoAmps or nA], the units of gain are [mg / dL] [nA]. -1 That is the case.

[0043] Each stepwise gain in the gain curve 200 in Figure 2 represents the gain used to convert the CGM sensor signal into glucose concentration, which can be done by the following equation. (21)G Raw =Gain*Signal In the formula, G Raw represents the initial (uncorrected) glucose value, and Gain is the gain (G) determined by calibration. Ref-cal / Signal cal ) and Signal is a glucose signal from a CGM sensor (e.g., Iw or Iw-Ib).

[0044] By providing a series of in-situ calibrations, the set of gains forms the gain curve shown in Figure 2, which reflects changes in sensor sensitivity and provides cross-sectional calibration to the CGM sensor during the CGM sensor deployment process (e.g., typically about 1-2 weeks or 7-14 days). However, further changes in sensitivity between in-situ calibrations become a source of error in the long-term monitoring process. The gain curve in Figure 2 is specific to the sensor used and depends on in-situ calibrations (e.g., taken periodically during the CGM process) using a reference glucose value such as the BGM glucose value. That is, the gain curve in Figure 2 is calculated and / or adjusted based on data points measured during the CGM process.

[0045] Another source of error is the apparent ISF (glucose) delay, as shown in the schematic diagram in Figure 3A. Specifically, Figure 3A shows a graph of glucose versus time as measured using BGM (reference glucose profile, curve 302) and CGM (CGM glucose profile, curve 304). When considering glucose profiles from reference (BGM) glucose measurements and glucose profiles from CGM sensors, the two glucose profiles are separated or shifted, resulting in a time delay where the ISF (CGM) glucose profile (curve 304) is delayed by a time delay Δt relative to the reference (BGM) glucose profile (curve 302). The time delay Δt varies depending on whether the glucose was measured during fasting or a glucose transition phase. As mentioned earlier, conventional methods to reduce this time delay are by filtering, i.e., delay correction. However, while these methods may work to some extent, the time delay may still exist because the nature of the time delay changes.

[0046] As shown in Figure 3A and according to embodiments described herein, when each individual error in glucose concentration ΔG on the CGM glucose profile is reduced / eliminated, there is no apparent shift in the CGM glucose profile (curve 304) from the reference (BGM) glucose profile (curve 302). This point-by-point error correction allows for improved accuracy of CGM glucose measurements, as will be further explained below.

[0047] Relationship G Raw From =Gain*Signal, we can show that the relative change in glucose ΔG / G is equal to the relative change in the sensor conversion gain, ΔGain / Gain, which maintains the signal constant. That is, (22)ΔG / G=ΔGain / Gain=(Gain act -Gain cal ) / Gain cal =Gain act / Gain cal -1, wherein, Gain act is the actual gain that fully accounts for errors in the CGM system, while Gain cal is the gain obtained from in-situ calibration (e.g., fingertip stick reading from BGM). Gain act can be obtained from paired data points (e.g., glucose signal and reference glucose value) within one calibration period of the study, where Gain act =G BGM / Signal act In contrast, Gain cal can be determined after each in-situ calibration (Gain cal is obtained as Gain cal =BGM cal / Signal cal ). Gain act and Gain cal may be identical when there is no error, or they may be different when there is some error. At the same time, as shown below, the relative change of glucose ΔG / G is also equal to the relative change of the signal, and the gain constant is maintained, (23) ΔSignal / Signal=(Signal act -Signal ideal ) / Signal ideal =Signal act / Signal ideal -1, wherein wherein, Signal act is a real-world signal that includes a portion of errors that lead to errors in actual glucose G act , while Signall ideal is the calibration gain Gain calUsing this, we obtain an ideal (error-free) signal for error-free glucose measurement. In each of the above, the relative changes in glucose, gain, and signal refer to the ideal value, calibrated value, or true value (through the denominator term). As can be seen from equation (21), the relative change in gain is opposite to the relative change in signal, which holds the glucose constant. This can also be seen from equations (22) and (23), where the complete gain in equation (22) act (Considering all system errors) exists within the molecule, while the ideal signal ideal This exists in the denominator of equation (23). This means that the relative signal error ΔSignal / Signal is equal to the relative gain change ΔGain / Gain in the opposite direction. It is assumed that any change in the signal ΔSignal / Signal is due to a change in the sensor gain, ΔGain / Gain, but may be due to a change in the opposite direction. Therefore, taking the signal error into account, Gain cal / (1+ΔGain / Gain) cal This may be adjusted. Therefore, the final glucose value G final teeth, (24)G final =Signal*Gain / (1+ΔGain / Gain), In the formula, the modification coefficient 1 / (1+ΔGain / Gain) represents the relative change in gain that defines the instantaneous calibration state, but it is in the opposite direction to the relative signal change. final Furthermore, in this specification, corrected glucose value G comp The signal may be called a raw or uncorrected glucose signal. Raw It can be called. Next, equation (24) is G Comp =Signal Raw It may also be written as *Gain*(1 / (1+Gain Function)), which is equation (2) above. For transformation functions with nonlinear relationships or stepwise calculations of analyte concentrations, the correction relationship can be expressed as follows: (25)G comp =G raw / (1+Gain Function) In the formula, G raw This is the initial glucose from these other conversion functions.

[0048] G Final =Signal Raw Given the above relationship of *Gain / (1+ΔGain / Gain), the goal is to find sensor progression parameters, such as ratios or other parameters, that satisfy and / or define the gain function ΔGain / Gain. From the previous consideration of current-past ratios as aggregated information from previous data points, in some embodiments, the gain function can be derived from these ratios and their cross-terms. There may be no explicit or obvious correlation between some single ratio and the gain function ΔGain / Gain, or the relative glucose change ΔG / G. However, a collective combination of multiple ratio terms and their selective cross-terms may provide the necessary correlation between the relative gain change and the gain function ΔGain / Gain. For example, in some embodiments, multivariate regression may be employed with ΔGain / Gain or ΔG / G as the regression target, and a number of current-past ratio terms and cross-terms as input parameters providing aggregated information from previous data points. In some embodiments, up to 2000 or more combined terms of current-to-past ratios and / or cross-terms may be used as input parameters. Fewer or more ratio terms and / or cross-terms may be used, and other relationships between current (point of interest) data points and previous data points (e.g., differences or other relationships) may also be used. Figure 3B shows the correlation between the relative glucose error ΔG / G and the gain function defined by the group of ratios and their cross-terms. The gain function is a function of the sensor progress parameter (SPP), i.e., gain function = f(SPP). 2 =64% indicates a strong correlation. 2A larger value indicates a stronger correlation, and more precisely, the gain function approaches the relative gain change (and the correction result is better based on the gain function).

[0049] As an example, 167 hours of CGM data were collected for several dozen users using multiple CGM sensors. The entire 167 hours of CGM data was divided into three segments: (1) 3–21 hours, (2) 12–45 hours, and (3) 40–167 hours. These segments are identified in the gain curves in Figure 2 by reference numbers 204a, 204b, and 204c, respectively. Fewer or more segments may be used. Referring to Figure 2, the largest change in gain occurs within the second segment 204b. For the first segment 204a, a ratio may be adopted using previous data points taken up to 3 hours prior to the signal at the point of interest (due to a 3-hour warm-up time). For example, in some embodiments, data collected during the warm-up period may be used to calculate the present-to-past ratio starting from 3 hours. For the second segment 204b, a ratio may be adopted using previous data points taken up to 12 hours prior to the signal at the point of interest, focusing on relatively large changes in gain. For the third segment 204c, previous data points taken up to 12 hours prior to the signal of the point of interest may be used. As mentioned above, even older previous data points may be used.

[0050] Multivariate regression can be performed using any suitable data analysis and / or statistical software package to obtain gain functions for each segment 204a, 204b, and 204c. For example, Minitab software, available from Minitab, LLC of State College, Pennsylvania, or another similar software package may be employed.

[0051] Multivariate regression on individual data points and their ratio parameters can be used to determine examples of the gain functions shown in the following examples. Other ratios, data point relationships, cross-terms, and / or gain functions may be employed.

[0052] Figures 4A, 4B, and 4C show the exemplary gain functions (referred to as Gain Function 1, Gain Function 2, and Gain Function 3 in Figures 4A-C) for segments 204a, 204b, and 204c of Figure 2, according to embodiments described herein. Figures 4D, 4E, and 4F are lists of definitions for the sensor progress parameters (e.g., ratios) and cross-terms for Gain Function 1, Gain Function 2, and Gain Function 3, respectively, according to embodiments described herein. This information is also provided in the appendix section below. Other and / or a number of other gain functions, sensor progress parameters, cross-terms, coefficient values, and / or constants may be employed. These gain functions and gain function terms are merely representative. Other types and / or numbers of gain functions may be used.

[0053] During operation, the gain function may be stored in the memory of the CGM device and may be used to generate a gain function value used to calculate a corrected glucose value based on the currently measured glucose signal from the CGM sensor (e.g., operating electrode current or optical signal) and past glucose signals taken more than 12 hours prior to the currently measured glucose signal. Using such a gain function can significantly reduce errors in the CGM glucose value caused by gain changes and ISF delay. For example, some uncorrected glucose values ​​from the CGM sensor were observed to have a MARD value of 18% to 25%, while corrected glucose values ​​determined using the gain function were observed to have a MARD value of 7% to 10% according to the embodiments described herein.

[0054] Figures 5A and 5B show illustrative consensus error grid plots 500a and 500b for raw and corrected glucose values ​​of a CGM sensor, respectively, according to embodiments provided herein. The clinical significance of regions A, B, C, D, and E is described below in Table 1, based on Joan L. Parkes et al., “A New Consensus Error Grid to Evaluate the Clinical Significance of Inaccuracies in the Measurement of Blood Glucose,” Diabetes Care, Volume 23(8), pp. 1143-1148 (2000). [Table 1]

[0055] As shown in Figures 5A and 5B, the combined data for regions A and B against glucose values ​​with error correction is greater than 99% (Figure 5B) compared to less than 98% (Figure 5A) for uncorrected glucose values. Furthermore, the glucose value in region A increases significantly. This performance improvement can also be seen in the effective reduction of ISF delay for corrected CGM glucose values. For example, Figures 6A and 6B show the BGM glucose value and corrected CGM glucose value (G) according to embodiments provided herein. Comp ), and uncorrected CGM glucose values ​​(G Raw This shows the time for the first CGM sensor (Sensor 1 in Figure 6A) and the second CGM sensor (Sensor 2 in Figure 6B). By using the gain function, the corrected glucose value (G) for both CGM sensors is shown. Comp ) is the raw glucose value (G Raw Compared to ), it appears to essentially not include ISF delay.

[0056] Figure 7A shows a high-level block diagram of an exemplary CGM device 700 according to embodiments provided herein. Although not shown in Figure 7A, various electronic components and / or circuits are, of course, configured to be coupled to a power source such as a battery, but are not limited to these. The CGM device 700 includes a bias circuit 702 which may be configured to be coupled to a CGM sensor 704. The bias circuit 702 may be configured to apply a bias voltage, such as a continuous DC bias, to the analyte-containing fluid through the CGM sensor 704. In this exemplary embodiment, the analyte-containing fluid may be human interstitial fluid, and the bias voltage may be applied to one or more electrodes 705 of the CGM sensor 704 (e.g., an operating electrode, a background electrode, etc.).

[0057] In some embodiments, the CGM sensor 704 may include two electrodes, and a bias voltage may be applied across the pair of electrodes. In such cases, the current may be measured through the CGM sensor 704. In other embodiments, the CGM sensor 704 may include three electrodes, such as an operating electrode, a counter electrode, and a reference electrode. In such cases, the bias voltage may be applied between the operating electrode and the reference electrode, and the current may be measured, for example, through the operating electrode. The CGM sensor 704 contains a chemical that reacts with the glucose-containing solution in a reduction-oxidation reaction, affecting the concentration of the charge carrier and the time-dependent impedance of the CGM sensor 704. Exemplary chemicals include glucose oxidase, glucose dehydrogenase, or similar. In some embodiments, mediators such as ferricyanide or ferrocene may be used.

[0058] The bias voltage generated and / or applied by the bias circuit 702 may be in the range of approximately 0.1 to 1 volt relative to the reference electrode. Other bias voltages may be used.

[0059] The current passing through the CGM sensor 704 in the analyte-containing fluid, which responds to the bias voltage, is measured from the CGM sensor 704 (I measThe current can be transmitted to circuit 706 (also called the current sensing circuit). The current sensing circuit 706 may be configured to sense and / or record a current sensing signal having a magnitude indicating the magnitude of the current transmitted from the CGM sensor 704 (for example, using a suitable current-to-voltage converter (CVC)). In some embodiments, the current sensing circuit 706 may include a resistor having a known nominal value and a known nominal precision (for example, in some embodiments, 0.1% to 5%, or even less than 0.1%) through which the current transmitted from the CGM sensor 704 passes. The voltage generated across the resistor in the current sensing circuit 706 represents the magnitude of the current and is the current sensing signal (or raw glucose signal). Raw ) You can also call it that.

[0060] In some embodiments, the sample circuit 708 may be coupled to the current measurement circuit 706 and configured to sample the current measurement signal, producing digitized time-domain sample data representing the current measurement signal (e.g., a digitized glucose signal). For example, the sample circuit 708 may be any suitable A / D converter circuit configured to receive the current measurement signal, which is an analog signal, and convert it into a digital signal having a desired number of bits as its output. The number of bits output by the sample circuit 708 may be 16 bits in some embodiments, but more or fewer bits may be used in other embodiments. In some embodiments, the sample circuit 708 may sample the current measurement signal at a sampling rate in the range of about 10 samples per second to 1000 samples per second. Faster or slower sampling rates may be used. For example, downsampling may be performed using sampling rates such as about 10 kHz to 100 kHz to further reduce the signal-to-noise ratio. Any suitable sampling circuit may be employed.

[0061] Referring further to Figure 7A, the processor 710 may be coupled to the sample circuit 708 and further coupled to the memory 712. In some embodiments, the processor 710 and the sample circuit 708 are configured to communicate directly with each other via a wired path (e.g., via a serial or parallel connection). In other embodiments, the coupling between the processor 710 and the sample circuit 708 may be via the memory 712. In this arrangement, the sample circuit 708 writes digital data to the memory 712, and the processor 710 reads digital data from the memory 712.

[0062] Memory 712 may store within it one or more gain functions 714 for use in determining corrected glucose values ​​based on raw glucose signals (from current measurement circuit 706 and / or sampling circuit 708). For example, in some embodiments, as described above, three or more gain functions may be stored in memory 712 for use with data collected by CGM for different segments (periods). Memory 712 may also store multiple instructions within it. In various embodiments, the processor 710 may be, but is not limited to, a computing resource such as a microprocessor, microcontroller, embedded microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA) configured to operate as a microcontroller, or similar.

[0063] In some embodiments, a plurality of instructions stored in memory 712 may include instructions that, when executed by the processor 710, cause the processor 710 to (a) measure a glucose signal (e.g., a current signal) from interstitial fluid (via a bias circuit 702, a CGM sensor 704, a current measurement circuit 706, and / or a sample circuit 708); (b) store the glucose signal in memory 712; (c) calculate sensor progress parameters such as the ratio (and / or other relationship) of the glucose signal at point of interest to a previously measured glucose signal; (d) calculate a corrected glucose value (e.g., concentration) using the calculated sensor progress parameters and the stored gain function; and (e) communicate the corrected glucose value to the user.

[0064] Memory 712 may be any suitable type of memory, including, but is not limited to, one or more of volatile memory and / or non-volatile memory. Volatile memory may include, but is not limited to, static random access memory (SRAM) or dynamic random access memory (DRAM). Non-volatile memory may include, but is not limited to, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., EEPROM of the type in either a NOR or NAND configuration, and / or in either a stacked or planar arrangement, and / or in any arrangement of single-level cell (SLC), multi-level cell (MLC), or a combination of SLC / MLC), resistive memory, filamentary memory, metal oxide memory, phase-change memory (e.g., chalcogenide memory), or magnetic memory. Memory 112 may be packaged, for example, as a single chip or as multiple chips. In some embodiments, memory 112 may be embedded in an integrated circuit, such as an application-specific integrated circuit (ASIC), together with one or more other circuits.

[0065] As described above, memory 712 may have a plurality of instructions stored in it that, when executed by processor 710, cause processor 710 to perform various operations specified by one or more of the stored instructions. Memory 712 may further have portions reserved for one or more “scratchpad” storage areas that can be used for read or write operations by processor 710 in response to the execution of one or more of the plurality of instructions.

[0066] In the embodiment shown in Figure 7A, the memory 712, which includes a bias circuit 702, a CGM sensor 704, a current measurement circuit 706, a sample circuit 708, a processor 710, and a gain function 714, may be located within the wearable sensor portion 716 of the CGM device 700. In some embodiments, the wearable sensor portion 716 may include a display 717 for displaying information such as glucose concentration information (e.g., without using an external device). The display 717 may be any suitable type of human-aware display, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, or an organic light-emitting diode (OLED) display.

[0067] Referring further to Figure 7A, the CGM device 700 may further include a portable user device portion 718. A processor 720 and a display 722 may be located within the portable user device portion 718. The display 722 may be coupled to the processor 720. The processor 720 may control the text or images displayed by the display 722. The wearable sensor portion 716 and the portable user device portion 718 may be communicatively coupled. In some embodiments, the communicative coupling of the wearable sensor portion 716 and the portable user device portion 718 may be by wireless communication via transmitter and / or receiver circuits, such as a transmit / receive circuit TxRx724a of the wearable sensor portion 716 and a transmit / receive circuit TxRx724b of the portable user device portion 718. Such wireless communication may be by any suitable means, including but not limited to standards-based communication protocols such as the Bluetooth® communication protocol. In various embodiments, wireless communication between the wearable sensor portion 716 and the portable user device portion 718 may alternatively be by near-field communication (NFC), radio frequency (RF) communication, infrared (IR) communication, or optical communication. In some embodiments, the wearable sensor portion 716 and the portable user device portion 718 may be connected by one or more wires.

[0068] The display 722 may be any suitable type of human-aware display, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, or an organic light-emitting diode (OLED) display.

[0069] Referring now to Figure 7B, an exemplary CGM device 750 is shown, similar to the embodiment shown in Figure 7A, but with a different division of components. In the CGM device 750, the wearable sensor portion 716 includes a bias circuit 702 coupled to the CGM sensor 704, and a current measuring circuit 706 coupled to the CGM sensor 704. The portable user device portion 718 of the CGM device 750 includes a sample circuit 708 coupled to the processor 720, and a display 722 coupled to the processor 720. The processor 720 is further coupled to memory 712, which has a gain function 714 stored in memory 712. In some embodiments, the processor 720 in the CGM device 750 may also perform the aforementioned functions performed by, for example, the processor 710 in the CGM device 700 of Figure 7A. The wearable sensor portion 716 of the CGM device 750 may be smaller, lighter, and therefore less invasive than the CGM device 700 in Figure 7A, because it does not include the sample circuit 708, processor 710, memory 712, etc. Other component configurations may be adopted. For example, as a variation of the CGM device 750 in Figure 7B, the sample circuit 708 may remain on the wearable sensor portion 716 (so that the portable user device 718 receives the digitized glucose signal from the wearable sensor portion 716).

[0070] Figure 8 is a schematic side view of an exemplary glucose sensor 704 according to embodiments provided herein. In some embodiments, the glucose sensor 704 may include a working electrode 802, a reference electrode 804, a counter electrode 806, and a background electrode 808. The working electrode 802 may include a conductive layer coated with a chemical that reacts with a glucose-containing solution in a reduction-oxidation reaction (affecting the concentration of the charge carrier and the time-dependent impedance of the CGM sensor 704). In some embodiments, the working electrode 802 may be formed from platinum or surface-roughened platinum. Other working electrode materials may be used. Exemplary chemical catalysts (e.g., enzymes) for the working electrode 802 include glucose oxidase, glucose dehydrogenase, or similar. The enzyme component may be immobilized on the electrode surface by a crosslinking agent, such as glutaraldehyde. An outer film layer may be added on the enzyme layer to protect the overall internal components, including the electrode and the enzyme layer. In some embodiments, mediators such as ferricyanide or ferrocene may be used. Other chemical catalysts and / or mediators may be used.

[0071] In some embodiments, the reference electrode 804 may be formed from Ag / AgCl. The counter electrode 806 and / or background electrode 808 may form a suitable conductor such as platinum, gold, palladium, or similar. Other materials may be used for the reference electrode, counter electrode, and / or background electrode. In some embodiments, the background electrode 808 may be identical to the working electrode 802, but without the chemical catalyst and mediator. The counter electrode 806 may be separated from the other electrodes by a separation layer 810 (e.g., polyimide or another suitable material).

[0072] Figure 9 is a flowchart of an exemplary method 900 for fabricating a CGM device according to embodiments provided herein. Referring to Figure 9, in block 902, a gain function is constructed based on several sensor progress parameters, such as multiple ratios of glucose signals (measured by the CGM sensor). The glucose signals may be electrochemical currents, optical signals, or similar. Each sensor progress parameter (SPP) is based on the glucose signal at point of interest and glucose signals measured prior to the glucose signal at point of interest. For example, the SPP ratio includes the ratio of the glucose signal at point of interest to glucose signals measured prior to the glucose signal at point of interest. In some embodiments, glucose signals measured up to 12 hours prior to the glucose signal at point of interest may be used. Shorter or longer periods may be used. In at least some embodiments, multivariate regression or similar statistical techniques may be employed with hundreds or even thousands of sensor progress parameters, such as ratios, differences, etc., and / or other cross-terms, to calculate the relevant SPPs (e.g., ratios, differences, etc.), cross-terms, and coefficients to employ in the gain function. Multiple gain functions (e.g., 2, 3, 4, 5, etc.) may be determined for use during different periods of CGM monitoring. For example, one or more gain functions may be stored in the memory 712 of the wearable sensor unit 716 (Figure 7A) or the portable user device 718 (Figure 7B).

[0073] In block 904, the gain function is stored in the memory of the CGM device (for example, in the form of parameter names and their coefficients). For example, one or more gain functions may be stored in the memory 712 of the wearable sensor portion 716 (Figure 7A) or the portable user device 718 (Figure 7B).

[0074] In block 906, computer program code is stored in the memory of the CGM device, and when executed by the processor, the computer program code causes the CGM device to (a) measure multiple glucose signals using the sensors of the CGM device, (b) store the glucose signals in the memory of the CGM device, (c) calculate multiple sensor progress parameters, such as ratios and differences, for the currently measured glucose signal, based on the currently measured glucose signal and multiple previously measured glucose signals stored in memory, (d) calculate a corrected glucose value (e.g., concentration) using the multiple sensor progress parameters and the stored gain function, and (e) communicate the corrected glucose value to the user of the CGM device. For example, the computer program code may be stored in the memory 712 of the wearable sensor portion 716 (Figure 7A) or the portable user device 718 (Figure 7B). The CGM sensor 704 may be used to measure glucose signals that may be stored in the memory 712. These stored glucose signals may be used to calculate multiple ratios to the currently measured glucose signal. Next, the calculated ratio can be used along with the stored gain function to calculate the corrected glucose value (for example, using processor 710, processor 720, and / or equation (2) above). Once calculated, the corrected glucose value can be communicated to the user (for example, via the display 717 of the wearable sensor 716, and / or the display 722 of the portable user device 718).

[0075] Figure 10 is a flowchart of an exemplary method 1000 for determining glucose concentration during continuous glucose monitoring (CGM) measurement, according to embodiments provided herein. Referring to Figure 10, block 1002 provides a CGM device including a sensor, memory, and a processor. The CGM device includes one or more gain functions stored in memory. Each gain function is based on a plurality of sensor progression parameters of the glucose signal, such as a ratio, each including the ratio of the glucose signal at point of interest to the glucose signal measured prior to the glucose signal at point of interest. In some embodiments, glucose signals measured up to 12 hours prior to the glucose signal at point of interest may be used. Shorter or longer periods may be used. For example, one or more gain functions may be stored in the memory 712 of a wearable sensor portion 716 (Figure 7A) or a portable user device 718 (Figure 7B). The glucose signal may be an electrochemical current, an optical signal, or something similar. In any of the embodiments described herein, additional calibration information, such as in-situ and / or factory calibration data, may be stored in a CGM or other analyte monitoring device (e.g., in the device's memory, such as memory 712) for use during glucose and / or other analyte determination.

[0076] In block 1004, multiple glucose signals are measured and stored in the memory of the CGM device. For example, the wearable sensor portion 716 can be applied to a user of the CGM device 700 using a suitable inserter. During the insertion process, the CGM sensor 704 is inserted through the user's skin and comes into contact with the interstitial fluid. The bias circuit 702 may apply a bias voltage (e.g., continuous DC bias) to the CGM sensor 704, and the current measurement circuit 706 may then sense the current signal generated by the applied bias voltage (e.g., operating electrode current, and in some embodiments, background electrode current). The sample circuit 708 may digitize the sensed current signal, and the processor 710 (or processor 720 in the embodiment of Figure 7B) may store the current signal in the memory 712.

[0077] In block 1006, multiple sensor progress parameters, such as multiple ratios, are calculated based on the currently measured glucose signal and multiple previously measured glucose signals stored in the CGM's memory. For example, each sensor progress parameter employed by a gain function stored in the CGM device's memory can be calculated for the currently measured glucose signal (for example, using processor 710 or 720 and memory 712 in Figure 7A or Figure 7B).

[0078] In block 1008, the calculated sensor progress parameters and stored gain function are used to calculate a corrected glucose value based on the currently measured glucose signal. As will be discussed, different gain functions may be used for different CGM usage periods (e.g., 3–21 hours, 12–45 hours, 40–167 hours, or similar), so the gain function used may depend on the period over which the currently measured glucose signal is measured. For example, processor 710 (or processor 720) calculates the ratio and other cross-terms used in the stored gain function and uses equation G Comp =Signal Raw*Gain*(1 / (1+Gain Function)) can be used to calculate the corrected glucose value. Alternatively, this can be considered as adjusting the system gain (Gain) based on the gain function, and then using the adjusted gain function to calculate the corrected glucose value (e.g., concentration).

[0079] In block 1010, the corrected glucose value is communicated to the user of the CGM device. For example, display 117 (Figure 7A) or display 722 (Figure 7B) may display the glucose value. Alternatively, the glucose value may be used as part of a trend line, graph, or image. In some embodiments, the corrected glucose value may not be displayed until much later in the future and / or until the user requests to display the glucose value.

[0080] In some embodiments, other analytes may be measured using continuous monitoring as provided herein. For example, concentrations of cholesterol, lactic acid, uric acid, alcohol, or similar substances may be detected using the analyte or other biosensor, the analyte signal at the point of interest, previously measured analyte signals, and one or more suitable gain functions.

[0081] Figure 11 shows an exemplary method 1100 for determining analyte concentration during continuous monitoring measurement using a biosensor subcutaneously implanted in a subject, according to embodiments provided herein. Referring to Figure 11, the method 1100 for determining analyte concentration during continuous monitoring measurement includes subcutaneously implanting a biosensor in a subject (block 1102). In some embodiments, the biosensor may include a counter electrode, a reference electrode, and a working electrode having a chemical composition configured to oxidize the analyte of interest. For example, a CGM sensor 704 or another analyte sensor may be inserted into the user. A bias voltage, such as a constant voltage, may be applied to the working electrode to generate a continuous current from the working electrode (block 1104). In block 1106, a working electrode current signal from the working electrode may be sensed and stored in memory (e.g., memory 712 of a CGM device 700 or 750, or memory of another continuous analyte monitoring device). For example, the working electrode current signal may be sensed (e.g., sampled) periodically and stored.

[0082] In block 1108, the operating electrode current signal at the point of interest and a portion of the operating electrode current signal stored in memory, measured prior to the operating electrode current signal at the point of interest, can be aggregated (e.g., read from memory). The gain function value can then be generated from a predetermined gain function using the operating electrode current signal at the point of interest and the portion of the operating electrode current signal aggregated from memory (block 1110). For example, ratios and / or differences based on the operating electrode current signal at the point of interest and the portion of the operating electrode current signal aggregated from memory can be used within the predetermined gain function.

[0083] The system gain can be modified using a gain function value generated from a given gain function (block 1112). For glucose, the system gain may be based on in-situ calibration using BGM glucose values. The system gain for other analytes can similarly be determined based on the reference analyte concentration and the operating electrode current signal (e.g., Gain=A Calb / (Iw) or A Calb / (Iw-Ib), where A Calb (This is the reference analyte value). In block 1114, based on the corrected system gain and the operating electrode current signal at the point of interest, the corrected analyte concentration (A) of the operating electrode current signal at the point of interest is calculated. Comp ) can be determined as follows: (25)A Comp =Signal Raw *Gain*(1 / (1+Gain Function)), In the formula, Gain*(1 / (1+Gain Function)) represents the modified system gain.

[0084] Exemplary analytes include glucose, cholesterol, lactic acid, uric acid, alcohol, or similar substances. In some embodiments, the background current signal may be stored in the operating electrode signal in memory and used to filter the signal out of interfering substances such as vitamin C and acetaminophen.

[0085] In yet another embodiment, a continuous analyte monitoring (CAM) device may include a wearable portion having a biosensor configured to be subcutaneously inserted into a target. The biosensor may include a counter electrode, a reference electrode, and a working electrode having a chemical composition configured to oxidize the analyte at point of interest and generate an analyte (e.g., glucose) signal from the interstitial fluid. The wearable portion may also include a processor, a memory coupled to the processor, and a transmitter circuit coupled to the processor (e.g., as shown in Figure 7A for a CGM device 700). The memory may include a predetermined gain function based on the analyte signal at point of interest and analyte signals measured prior to the analyte signal at point of interest. Furthermore, the memory may include computer program code stored in the memory, which, when executed by the processor, causes the CAM device to (a) apply a constant voltage to an operating electrode having a chemical composition, thereby generating a continuous current from the operating electrode; (b) sense an operating electrode current signal from the operating electrode and store it in memory; (c) aggregate the operating electrode current signal at a point of interest and a portion of the operating electrode current signal stored in memory that was measured before the operating electrode current signal at the point of interest; (d) generate a gain function value from a gain function using the operating electrode current signal at the point of interest and the portion of the operating electrode current signal aggregated from memory; (e) modify the system gain using the gain function value generated from a predetermined gain function; and (f) determine the analyte concentration of the operating electrode current signal at the point of interest based on the modified system gain and the operating electrode current signal at the point of interest. For example, in some embodiments, the analyte concentration using the modified system gain may be calculated using the above formula (25).

[0086] Figure 12 shows another exemplary method 1200 for fabricating a continuous analyte monitoring device according to embodiments provided herein. Referring to Figure 12, in block 1202, the analyte sensor is operably coupled to a host. For example, the analyte sensor may be inserted through the host's skin so that the analyte sensor is in contact with the interstitial fluid region. Exemplary analytes that can be detected include glucose, cholesterol, lactic acid, uric acid, alcohol, or similar substances.

[0087] In block 1204, the analyte sensor is used to continuously record the analyte signal, such as at a normal sampling rate. For example, the analyte signal may be recorded every minute, two minutes, three minutes, five minutes, ten minutes, or any other increment (e.g., it may be sensed, measured, sampled, and / or stored). In some embodiments, the analyte signal may be collected as part of a clinical trial using multiple analyte sensors and multiple hosts. A reference analyte concentration may also be recorded (e.g., periodically during a continuous analyte monitoring process). For example, the reference analyte concentration may be recorded using a reference analyte device such as a blood glucose meter. The reference analyte concentration may be recorded at any appropriate increment, such as hourly, daily, every other day, or similar.

[0088] In block 1206, the analyte sensor signal is paired with a reference analyte concentration. For example, an analyte signal measured after a reference analyte concentration has been recorded may be paired with the recorded reference analyte concentration until another reference analyte concentration is recorded. Other pairings may be used.

[0089] Figure 13 is a graph of exemplary CGM response currents, such as Iw-Ib, paired with a reference glucose value from a blood glucose meter (e.g., a Contour Next One® blood glucose meter) according to embodiments provided herein. As shown in Figure 13, Iw-Ib tracks the reference glucose value. In some embodiments, a glucose signal measured 3 to 5 minutes before a new reference glucose value is measured is paired with the new reference glucose value rather than the previously recorded reference glucose value.

[0090] Figure 14 is a graph of exemplary reference glucose values ​​against glucose currents such as Iw-Ib during a CGM process according to embodiments provided herein. As shown in Figure 14, the glucose current signal accurately tracks the reference glucose values ​​and R 2 The value is 0.83.

[0091] In block 1208, the relative analyte error ΔA / A is calculated. For example, the analyte error is equivalent to the signal error ΔSignal / Signal and can be calculated using equation (23) above.

[0092] In block 1210, sensor progression information is aggregated and used to calculate sensor progression parameters by referencing analyte data points at points of interest with previously measured analyte data points across the entire dataset. For example, sensor progression parameters may be calculated for each analyte signal at a point of interest by calculating a ratio, difference, or other relationship between the analyte signal at the point of interest and analyte signals measured before the analyte signal at the point of interest.

[0093] In block 1212, statistical analysis is performed by setting at least one of the relative analyte error referenced with respect to a reference analyte concentration and the relative gain error referenced with respect to a reference gain as targets for statistical analysis, and by setting sensor progress parameters as input variables to obtain a gain function. In some embodiments, the statistical analysis may include multivariate regression. Other statistical analysis methods may be used. As described above, the gain function may be obtained using tens, hundreds, or thousands of sensor progress parameters.

[0094] In block 1214, the acquired gain function is recorded as a factory calibration component for storage in a continuous analyte monitoring device. The gain function includes selected sensor progression parameters and their weighting coefficients. As previously mentioned with reference to Figure 2, more than one gain function may be determined for an analyte dataset (e.g., two, three, four, or more). In some embodiments, the gain function may be stored in the memory 712 of the CGM device 700, or in a CGM device such as another continuous analyte monitoring device. The gain function may be based on an electrochemical signal, an optical signal, or something similar.

[0095] The foregoing description discloses only illustrative embodiments. Modifications of the apparatus and methods of the above disclosure within the scope of this disclosure will be readily apparent to those skilled in the art.

[0096] appendix The gain functions, coefficients, sensor progress parameters, and / or cross-terms listed below are for illustrative purposes only. Other gain functions, coefficients, sensor progress parameters, and / or cross-terms may be used.

[0097] Gain function 1 (3-20 hours) Gain Function1=3.091118+1.471262*w27min-0.984106*w2h-2.665682*b3min-2.254352*b18min+2.314656*b 1h-0.0149674*w6mG-0.0227495*w24mG+1.117723*w27mSS1+0.951549*w3hSS1+0.0020214*w3hGSS1+0.53 6988*w3m2h-0.97541*w9m2h+0.326197*w15m1h+0.699317*w24m2h-0.397502*w27m2h+0.0287526*b3mG-0 .01240012*b6mG-0.43543*b3hSS1-0.0025204*b30mGSS1-0.841776*w27mb6mSS1-1.268666*w3hb9mSS1...

[0098] Gain function 2 (12-45 hours) Gain Function2=6.333784+2.7006532*w9min-0.4503101*b1h-0.0004858*w9mGSS1+9.35e-5*w12hGSS1+0.0930 561*w3m3h-0.0721993*w3m12h+0.292186*w9m1h-0.2503538*w12m2h-0.231265*w21m2h+0.508004*w24m2h -0.2291636*w30m1h-0.0584004*w30m3h+0.0409812*w30m4h-0.0041765*b6mG+0.0039682*b12hG-0.13460 56*b6hSS1+0.0010892*b1hGSS1+0.0009736*b10hGSS1+0.1471952*w8hb4hSS1-0.0364746*w12hb4hSS1...

[0099] Gain function 3 (40-167 hours) Gain Function3=0.979878-0.637773*w9min+0.0001337*w2hGSS1+0.0001573*w12hGSS1+0.167157*w3m3h-0.32 5834*w3m12h+0.311399*w6m12h+0.378304*w12m1h-0.308914*w12m3h-0.022367*w18m10h-0.377033*w27m1 h+0.167712*w27m3h-0.390158*b15mSS1+0.356742*b8hSS1-0.0008352*b9mGSS1+0.0008576*b4hGSS1-0.20 4995*w3mb2h-0.59261*w6mb1h+0.0020452*Gw12hb12h-0.296086*w12hb12mSS1+0.289866*w12hb12hSS1...

[0100] Sensor progress parameters and cross-terms for gain function 1 At the in-situ calibration point, Gain i =BGM cal-i (Iw-Ib) cal-i In the equation, i = 1, 2, ..., 10, etc.

[0101] S / S1 = Gain i / Gain1, individual Gain i The ratio of Gain1 = BGM cal-1 (Iw-Ib) cal-1

[0102] G raw = 0.85 * (Iw - Ib)t * Gain i if Gain i >12; else Gain i >8,0.9*(Iw-Ib)t*Gain i ;else(Iw-Ib)t*Gain i

[0103] w27min=(Iw-Ib) t (Iw-Ib) t-27min The ratio of Iw-Ib at time t to Iw-Ib at time t-27 minutes.

[0104] w2h=(Iw-Ib) t / (Iw-Ib) t-2hour , the ratio of Iw-Ib at time t to Iw-Ib at time t-2 hours

[0105] b3min=Ib t / Ib t-3min , the ratio of Ib at time t to Ib at time t-3 minutes

[0106] b18min=Ib t / Ib t-18min

[0107] b1h=Ib t / Ib t-1hour

[0108] w6mG=G raw *(Iw-Ib) t / (Iw-Ib) t-6min

[0109] w24mG=G raw *(Iw-Ib) t / (Iw-Ib) t-24min

[0110] w27mSS1=(S / S1)*(Iw-Ib) t / (Iw-Ib) t-27min

[0111] w3hSS1=(S / S1)*(Iw-Ib) t / (Iw-Ib) t-3hour

[0112] w3hGSS1=G raw *(S / S1)*(Iw-Ib) t / (Iw-Ib) t-3hour

[0113] w3m2h=w_3min / w_2h=[(Iw-Ib) t / (Iw-Ib) t-3min / [(Iw-Ib) t / (Iw-Ib) t-2hour〕=(Iw-Ib) t-2hour / (Iw-Ib) t-3min

[0114] w9m2h=w_9min / w_2h=〔(Iw-Ib) t / (Iw-Ib) t-9min (Iw-Ib) t / (Iw-Ib) t-2hour 〕=(Iw-Ib) t-2hour / (Iw-Ib) t-9min

[0115] w15m1h=w_15min / w_1h=〔(Iw-Ib) t / (Iw-Ib) t-15min (Iw-Ib) t / (Iw-Ib) t-1hour 〕=(Iw-Ib) t-1hour / (Iw-Ib) t-15min

[0116] w24m2h=w_24min / w_2h=〔(Iw-Ib) t / (Iw-Ib) t-24min (Iw-Ib) t / (Iw-Ib) t-2hour 〕=(Iw-Ib) t-2hour / (Iw-Ib) t-24min

[0117] w27m2h=w_27min / w_2h=〔(Iw-Ib) t / (Iw-Ib) t-27min (Iw-Ib) t / (Iw-Ib) t-2hour 〕=(Iw-Ib) t-2hour / (Iw-Ib) t-27min

[0118] b3mG=G raw *Ib t / Ib t-3min

[0119] b6mG=G raw *Ib t / Ib t-6min

[0120] b3hSS1=(S / S1)*Ib t / Ib t-3hour

[0121] b30mGSS1=G raw *(S / S1)*Ib t / Ib t-30min

[0122] w27mb6mSS1=(S / S1)*w_27min / b_6min=(S / S1)*[(Iw-Ib) t / (Iw-Ib) t-27min / [Ib t / Ib t-6min

[0123] w3hb9mSS1=(S / S1)*w_3h / b_9min=(S / S1)*[(Iw-Ib) t / (Iw-Ib) t-3hour / [Ib t / Ib t-9min

[0124] Sensor progression parameters and cross terms of gain function 2 At the in-situ calibration point, Gain i =BGM cal-i / (Iw-Ib) cal-i wherein i=1, 2, ..... 10, etc.

[0125] S / S1=Gain i / Gain1, the ratio of each individual Gain i to Gain1=BGM cal-1 / (Iw-Ib) cal-1

[0126] G raw =0.85*(Iw-Ib)t*Gain i , if Gaini>12; elseif Gain i >8, 0.9*(Iw-Ib)t*Gain i ; else (Iw-Ib)t*Gain i

[0127] ​​ w9min=(Iw-Ib) t / (Iw-Ib) t-9min , which is the ratio of Iw-Ib at time t to Iw-Ib at time t-9 minutes

[0128] b1h=Ib t / Ib t-1hour , which is the ratio of Ib at time t to Ib at time t-1 hour

[0129] w9mGSS1=G raw *(S / S1)*(Iw-Ib) t / (Iw-Ib) t-9min

[0130] w12hGSS1=G raw *(S / S1)*(Iw-Ib) t / (Iw-Ib) t-12hour

[0131] w3m3h=w_3min / w_3h=[(Iw-Ib) t / (Iw-Ib) t-3min / [(Iw-Ib) t / (Iw-Ib) t-3hour =(Iw-Ib) t-3hour / (Iw-Ib) t-3min

[0132] w3m12h=w_3min / w_12h=[(Iw-Ib) t / (Iw-Ib) t-3min / [(Iw-Ib) t / (Iw-Ib) t-12hour =(Iw-Ib) t-12hour / (Iw-Ib) t-3min

[0133] w9m1h=w_9min / w_1h=[(Iw-Ib) t / (Iw-Ib) t-9min / [(Iw-Ib) t / (Iw-Ib) t-1hour =(Iw-Ib) t-1hour / (Iw-Ib) t-9min

[0134] w12m2h=w_12min / w_2h=〔(Iw-Ib) t / (Iw-Ib) t-12min (Iw-Ib) t / (Iw-Ib) t-2hour 〕=(Iw-Ib) t-2hour / (Iw-Ib) t-12min

[0135] w21m2h=w_21min / w_2h=〔(Iw-Ib) t / (Iw-Ib) t-21min (Iw-Ib) t / (Iw-Ib) t-2hour 〕=(Iw-Ib) t-2hour / (Iw-Ib) t-21min

[0136] w24m2h=w_24min / w_2h=〔(Iw-Ib) t / (Iw-Ib) t-24min (Iw-Ib) t / (Iw-Ib) t-2hour 〕=(Iw-Ib) t-2hour / (Iw-Ib) t-24min

[0137] w30m1h=w_30min / w_1h=〔(Iw-Ib) t / (Iw-Ib) t-30min (Iw-Ib) t / (Iw-Ib) t-1hour 〕=(Iw-Ib) t-1hour / (Iw-Ib) t-30min

[0138] w30m3h=w_30min / w_3h=〔(Iw-Ib) t / (Iw-Ib) t-30min (Iw-Ib) t / (Iw-Ib) t-3hour 〕=(Iw-Ib) t-3hour / (Iw-Ib) t-30min

[0139] w30m4h=w_30min / w_4h=〔(Iw-Ib) t / (Iw-Ib) t-30min / [(Iw-Ib) t / (Iw-Ib) t-4hour =(Iw-Ib) t-4hour / (Iw-Ib) t-30min

[0140] b6mG=G raw *Ib t / Ib t-6min

[0141] b12hG=G raw *Ib t / Ib t-12hour

[0142] b6hSS1=(S / S1)*Ib t / Ib t-6hour

[0143] b1hGSS1=G raw *(S / S1)*Ib t / Ib t-1hour

[0144] b10hGSS1=G raw *(S / S1)*Ib t / Ib t-10hour

[0145] w8hb4hSS1=(S / S1)*w_8h / b_4h=(S / S1)*[(Iw-Ib) t / (Iw-Ib) t-8hour / [Ib t / Ib t-4hour

[0146] w12hb4hSS1=(S / S1)*w_12h / b_4h=(S / S1)*[(Iw-Ib) t / (Iw-Ib) t-12hour / [Ib t / Ib t-4hour

[0147] Sensor progression parameters and cross terms of gain function 3 At the in-situ calibration point, Gain i =BGM cal-i / (Iw-Ib)​​cal-i and in the formula, i = 1, 2, ...... 10, etc.

[0148] S / S1=Gain i / Gain1, each individual Gain i the ratio of to Gain1 = BGM cal-1 / (Iw-Ib) cal-1

[0149] G raw =0.85*(Iw-Ib)t*Gain i ,if Gain i >12;elseif Gain i >8,0.9*(Iw-Ib)t*Gain i ;else(Iw-Ib)t*Gain i

[0150] w9min=(Iw-Ib) t / (Iw-Ib) t-9min , the ratio of Iw-Ib at time t to Iw-Ib at t-9 minutes

[0151] w2hGSS1=G raw *(S / S1)*(Iw-Ib) t / (Iw-Ib) t-2hour

[0152] w12hGSS1=G raw *(S / S1)*(Iw-Ib) t / (Iw-Ib) t-12hour

[0153] w3m3h=w_3min / w_3h= [(Iw-Ib) t / (Iw-Ib) t-3min / [(Iw-Ib) t / (Iw-Ib) t-3hour =(Iw-Ib) t-3hour / (Iw-Ib) t-3min

[0154] w3m12h=w_3min / w_12h= [(Iw-Ib) t / (Iw-Ib) t-3min / [(Iw-Ib)t / (Iw-Ib) t-12hour 〕=(Iw-Ib) t-12hour / (Iw-Ib) t-3min

[0155] w6m12h=w_6min / w_12h=〔(Iw-Ib) t / (Iw-Ib) t-6min (Iw-Ib) t / (Iw-Ib) t-12hour 〕=(Iw-Ib) t-12hour / (Iw-Ib) t-6min

[0156] w12m1h=w_12min / w_1h=〔(Iw-Ib) t / (Iw-Ib) t-12min (Iw-Ib) t / (Iw-Ib) t-1hour 〕=(Iw-Ib) t-1hour / (Iw-Ib) t-12min

[0157] w12m3h=w_12min / w_3h=〔(Iw-Ib) t / (Iw-Ib) t-12min (Iw-Ib) t / (Iw-Ib) t-3hour 〕=(Iw-Ib) t-3hour / (Iw-Ib) t-12min

[0158] w18m10h=w_18min / w_10h=〔(Iw-Ib) t / (Iw-Ib) t-18min (Iw-Ib) t / (Iw-Ib) t-10hour 〕=(Iw-Ib) t-10hour / (Iw-Ib) t-18min

[0159] w27m1h=w_27min / w_1h=〔(Iw-Ib) t / (Iw-Ib) t-27min (Iw-Ib) t / (Iw-Ib) t-1hour 〕=(Iw-Ib) t-1hour / (Iw-Ib) t-27min

[0160] w27m3h=w_27min / w_3h=〔(Iw-Ib) t / (Iw-Ib) t-27min 〕 / 〔(Iw-Ib) t / (Iw-Ib) t-3hour 〕=(Iw-Ib) t-3hour / (Iw-Ib) t-27min

[0161] b15mSS1=(S / S1)*Ib t / Ib t-15min

[0162] b8hSS1=(S / S1)*Ib t / Ib t-8hour

[0163] b9mGSS1=G raw *(S / S1)*Ib t / Ib t-9min

[0164] b4hGSS1=G raw *(S / S1)*Ib t / Ib t-4hour

[0165] w3mb2h=w_3min / b_2h=〔(Iw-Ib) t / (Iw-Ib) t-3min 〕 / 〔Ib t / Ib t-2hour 〕

[0166] w6mb1h=w_6min / b_1h=〔(Iw-Ib) t / (Iw-Ib) t-6min 〕 / 〔Ib t / Ib t-1hour 〕

[0167] Wed12hb 12h=G raw *w_12h / b_12h=G raw *〔(Iw-Ib) t / (Iw-Ib) t-12hour 〕 / 〔Ib t / Ib t-12hour 〕

[0168] w12hb12mSS1=(S / S1)*w_12h / b_12min=(S / S1)*〔(Iw-Ib) t / (Iw-Ib) t-12hour 〕 / 〔Ib t / Ib t-12min 〕

[0169] w12hb12hSS1=(S / S1)*w_12h / b_12h=(S / S1)*〔(Iw-Ib) t / (Iw-Ib) t-12hour 〕 / 〔Ib t / Ib t-12hour 〕

Claims

1. A continuous analyte monitoring (CAM) device including a wearable component equipped with a biosensor, processor, and memory, The biosensor comprises a counter electrode, a reference electrode, a background electrode, and a working electrode having a chemical composition, wherein the chemical composition is configured to oxidize the analyte at the point of interest to generate an analyte signal from the interstitial fluid. The memory is connected to the processor, and the memory includes an analyte signal of a point of interest, which includes a predetermined gain function based on an analyte signal measured prior to the analyte signal of the point of interest, The memory stores computer program code, and when the computer program code is executed by the processor, it causes the CAM device to perform the following processes: Process a: Applying a constant voltage to the operating electrode having the chemical composition to generate a continuous current from the operating electrode; Process b: Sensing and storing the operating electrode current signal from the operating electrode; Process c: Sensing and storing a background electrode current signal from the background electrode; A process d to calculate one or more sensor progress parameters (SPPs) from the operating electrode current signal of the point of interest, the background electrode current signal of the point of interest, one or more stored signals of the operating electrode current signal, and one or more stored signals of the background electrode current signal; Process e: A process to generate a gain function value by applying one or more SPPs to the predetermined gain function; Process f to correct the system gain using the gain function value generated from the predetermined gain function; A process g in which the analyte concentration of the operating electrode current signal at the point of interest is determined by applying the system gain to the operating electrode current signal at the point of interest; and Process h to communicate the concentration of the analyte to the user. A continuous analyte monitoring (CAM) device configured as such.

2. The CAM apparatus according to claim 1, wherein the predetermined gain function is determined in-house using multivariate regression.

3. The CAM apparatus according to claim 1, wherein the gain function value is based on an operating electrode current signal measured at least one hour before the operating electrode current signal of the point of interest.

4. The CAM apparatus according to claim 1, wherein the gain function value is based on an operating electrode current signal measured 6 to 12 hours before the operating electrode current signal at the point of interest.

5. The CAM apparatus according to claim 1, wherein the predetermined gain function uses the ratio of the operating electrode current signal at the point of interest to a previously measured operating electrode current signal.

6. The CAM device further comprises a transmitter circuit connected to the processor, The CAM device according to claim 1, wherein the process h for communicating the analyte concentration to the user in the CAM device includes a process of transmitting the analyte concentration to a portable user device using the transmitter circuit.

7. The CAM device further includes a display for displaying information to the user, The CAM device according to claim 1, wherein the process h for communicating the analyte concentration to the user in the CAM device includes a process of displaying the analyte concentration to the user using the display.

8. The aforementioned operating electrode current signal is measured before the operating electrode current signal at the point of interest. The background electrode current signal is measured before the background electrode current signal at the point of interest. The CAM device according to claim 1.

9. The CAM apparatus according to claim 1, wherein the process e for generating the gain function value includes a process for determining the value based on the operating electrode current signal of the point of interest and a ratio based on the operating electrode current signal.

10. The CAM apparatus according to claim 1, wherein the process e for generating the gain function value includes a process for determining the difference between the operating electrode current signal of the point of interest and the background electrode current signal of the point of interest.

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