Parameter extraction for analyte concentration determination

Abstract

A method for determining a glucose level during a continuous glucose monitoring (CGM) measurement includes providing a CGM device including a sensor, a memory, and a processor, applying a constant voltage potential to the sensor, measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in memory, applying a probing potential modulation sequence to the sensor, measuring a probing potential modulation current signal resulting from the probing potential modulation sequence and storing the measured probing potential modulation current signal in memory, determining an initial glucose concentration based on a transfer function and a ratio of the measured probing potential modulation current signals, determining a connection function value based on the primary current signal and the plurality of probing potential modulation current signals, and determining a final glucose concentration based on the initial glucose concentration and the connection function value. Other aspects are disclosed.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. Provisional Patent Application No. 63 / 061,135, filed August 4, 2020, entitled "CONTINUOUS ANALYTE MONITORING SENSOR CALIBRATION AND MEASUREMENTS BY A CONNECTION FUNCTION," U.S. Provisional Patent Application No. 63 / 061,152, filed August 4, 2020, entitled "NON-STEADY-STATE DETERMINATION OF ANALYTE CONCENTRATION FOR CONTINUOUS GLUCOSE MONITORING BY POTENTIAL MODULATION," U.S. Provisional Patent Application No. 63 / 061,157, filed August 4, 2020, entitled "EXTRACTING PARAMETERS FOR ANALYTE CONCENTRATION DETERMINATION," and U.S. Provisional Patent Application No. 63 / 061,158, filed August 4, 2020, entitled "BIOSENSOR WITH MEMBRANE STRUCTURE FOR STEADY-STATE AND This application claims the benefit of U.S. Provisional Patent Application No. 63 / 061,167, entitled "NON-STEADY-STATE CONDITIONS FOR DETERMINING ANALYTE CONCENTRATIONS," the disclosures of each of which are hereby incorporated by reference in their entirety for all purposes.

[0002] This application relates generally to continuous sensor monitoring of analytes in bodily fluids, and more particularly to continuous glucose monitoring (CGM). [Background technology]

[0003] Continuous analyte sensing in in vivo or in vitro samples, such as in CGMs, has become a routine sensing operation in the medical device field, and more particularly in diabetes care. For example, for biosensors that measure analytes in whole blood samples using discrete sensing, such as a finger prick to obtain a blood sample, the temperature of the sample and the hematocrit of the blood sample can be major sources of error. However, for sensors deployed in non-whole blood environments with a relatively constant temperature, such as sensors used in continuous in vivo sensing operations, other sources of sensor error may exist.

[0004] Therefore, improved devices and methods for determining glucose levels using CGM sensors are desirable. Summary of the Invention

[0005] In some embodiments, a method for determining a glucose value during a continuous glucose monitoring (CGM) measurement includes providing a CGM device including a sensor, a memory, and a processor; applying a constant voltage potential to the sensor, measuring a primary current signal resulting from the constant voltage potential, and storing the measured primary current signal in memory; applying a probing potential modulation sequence to the sensor, measuring a probing potential modulation current signal resulting from the probing potential modulation sequence, and storing the measured probing potential modulation current signal in memory; determining an initial glucose concentration based on a transfer function and a ratio of the measured probing potential modulation current signals; determining a connection function value based on the primary current signal and the plurality of probing potential modulation current signals; and determining a final glucose concentration based on the initial glucose concentration and the connection function value.

[0006] In some embodiments, a continuous glucose monitoring (CGM) device includes a wearable portion having a sensor configured to generate a current signal from interstitial fluid, a processor, a memory coupled to the processor, and a transmitter circuit coupled to the processor. The memory includes a connection function based on a primary current signal generated by application of a constant voltage potential applied to a reference sensor and a plurality of probing voltage-modulated current signals generated by application of a probing voltage-modulation sequence applied between measurements of the primary current signal. The memory also includes computer program code stored in the memory that, when executed by the processor, causes the CGM device to measure and store the primary current signal using the sensor and memory of the wearable portion, measure and store a plurality of probing voltage-modulated current signals associated with the primary current signal, determine an initial glucose concentration based on the conversion function and a ratio of the measured probing voltage-modulated current signals, determine a connection function value based on the primary current signal and the plurality of probing voltage-modulated current signals, and determine a final glucose concentration based on the initial glucose concentration and the connection function value.

[0007] Further aspects, features, and advantages of the present disclosure will become readily apparent from the following detailed description and illustrations of several exemplary embodiments and implementations, including the best mode contemplated for carrying out the invention. The present disclosure may enable other different embodiments, and its several details may be modified in various respects, without departing from the scope of the present invention. For example, although the following description relates to continuous glucose monitoring, the devices, systems, and methods described below may be readily adapted to monitoring other analytes, such as cholesterol, lactate, uric acid, alcohol, and the like, in other continuous analyte monitoring systems. [Brief explanation of the drawings]

[0008] The drawings described below are for illustrative purposes and are not necessarily drawn to scale. Accordingly, the drawings and descriptions should be regarded as illustrative in nature, and not as restrictive. The drawings are not intended to limit the scope of the invention in any way. [Figure 1A] 1 shows a graph of applied voltage E0 versus time for a continuous glucose monitoring (CGM) sensor in accordance with one or more embodiments of the present disclosure. [Figure 1B] 1B shows a graph of a current profile of a probing potential modulation (PPM) sequence for the CGM sensor of FIG. 1A in accordance with one or more embodiments of the present disclosure. [Figure 2] 10 illustrates a graph of a comparison of the primary current i10 of the sequence and the glucose response signal by the ratio parameter R4 of the data, in accordance with one or more embodiments of the present disclosure. [Figure 3A] 1 illustrates a graph of the steady-state conditions associated with an electrode and its nearby boundary environment, in accordance with one or more embodiments of the present disclosure. [Figure 3B] 10 illustrates a graph of an example PPM sequence applied to a sensor from which data was collected, in accordance with one or more embodiments of the present disclosure. [Figure 3C] 10 illustrates a graph of the non-steady-state conditions associated with an electrode and its near boundary environment during the E2 and E3 potential steps, in accordance with one or more embodiments of the present disclosure. [Figure 3D] 1 shows an IV curve of a PPM sequence implemented in accordance with one or more embodiments of the present disclosure, and a graph of the individual potential steps. [Figure 3E] 3C illustrates a graph of a typical output current from the PPM sequence shown in FIG. 3B and the labeling of the current at each potential step, in accordance with one or more embodiments of the present disclosure. [Figure 4A] 10 illustrates a graph comparing i10 response to glucose from sensors Type 1 and Type 2, in accordance with one or more embodiments of the present disclosure. [Figure 4B] 10 illustrates a graph comparing R1 responses to glucose from sensors Type 1 and Type 2, in accordance with one or more embodiments of the present disclosure. [Figure 4C] 10 illustrates a graph comparing R4 response to glucose from sensors Type 1 and Type 2, in accordance with one or more embodiments of the present disclosure. [Figure 4D] 10 illustrates a graph comparing y45 responses to glucose from sensors Type 1 and Type 2, according to one or more embodiments of the present disclosure. [Figure 5A] 10 illustrates a graph comparing the initial response of i10 and R4 ratio from a single sensor in accordance with one or more embodiments of the present disclosure. [Figure 5B] 10 illustrates a graph comparing averaged initial normalized responses of i10 and R4 from seven sensors in accordance with one or more embodiments of the present disclosure. [Figure 6A] 1 illustrates a graph of the temporal current profile of primary data points in a linearity test with four levels of acetaminophen using PPM and non-PPM (NPPM) methods, in accordance with one or more embodiments of the present disclosure. [Figure 6B] 10 illustrates a graph of the current i10 response to glucose in a linearity study with four levels of acetaminophen using the NPPM method, in accordance with one or more embodiments of the present disclosure. [Figure 6C] 10 illustrates a graph of the PPM current response to glucose in the same test, in accordance with one or more embodiments of the present disclosure. [Figure 6D] 10 illustrates a graph of R4 response to glucose in the same study, according to one or more embodiments of the present disclosure. [Figure 6E] 10 illustrates a graph of the R1 response to glucose in the same study, according to one or more embodiments of the present disclosure. [Figures 7A-7D] 7A and 7B illustrate graphs of CGM sensor responses and their baseline correlations from a group of seven sensors in a linearity test in accordance with one or more embodiments of the present disclosure; in particular, FIG. 7A illustrates a graph of G baseline versus R1 ratio, FIG. 7B illustrates a graph of G baseline versus R4 ratio, FIG. 7C illustrates a graph of G baseline versus y45 ratio, and FIG. 7D illustrates a graph of i10 versus G baseline. [Figure 8] 1 illustrates a table summarizing G production and G synthesis from i10, R4, y45, and R1 using an in vitro dataset, in accordance with one or more embodiments of the present disclosure. [Figure 9A] 1 shows a high-level block diagram of an exemplary CGM device in accordance with one or more embodiments of the present disclosure. [Figure 9B] 1 shows a high-level block diagram of another exemplary CGM device in accordance with one or more embodiments of the present disclosure. [Figure 10] FIG. 1 is a schematic side view of an exemplary glucose sensor in accordance with one or more embodiments of the present disclosure. [Figure 11] 1 illustrates an exemplary method for determining glucose values ​​during a continuous glucose monitoring measurement according to embodiments provided herein. DETAILED DESCRIPTION OF THE INVENTION

[0009] Embodiments described herein include systems and methods for applying probing potential modulation (PPM) on top of an otherwise constant voltage applied to an analyte sensor. The terms "voltage," "potential," and "voltage potential" are used interchangeably herein. "Current," "signal," and "current signal" are also used interchangeably herein, as are "continuous analyte monitoring" and "continuous analyte sensing." As used herein, PPM refers to intentional periodic variations in the otherwise constant voltage potential applied to a sensor during continuous analyte sensing, such as the application of a probing potential step, pulse, or other potential modulation to the sensor. The use of PPM during continuous analyte sensing may be referred to as a PP or PPM method, while performing continuous analyte sensing without PPM may be referred to as a NP or NPPM method.

[0010] A primary data point or primary current refers to a measurement of a current signal generated in response to an analyte at a constant voltage potential applied to a sensor during continuous analyte sensing. For example, FIG. 1A illustrates a graph of applied voltage E versus time for a continuous glucose monitoring (CGM) sensor according to one or more embodiments of the present disclosure. Exemplary times at which a primary data point measurement can be made and subsequent PPM can be applied are shown. As shown in FIG. 1A, in this example, the constant voltage potential E applied to the working electrode of the analyte sensor can be approximately 0.55 volts. Other voltage potentials can be used. FIG. 1A illustrates an example of a typical cycle of primary data points acquired at a constant applied voltage. Primary data points are data points measured or sampled at a constant applied voltage and at regular intervals, such as 3-15 minutes, during continuous glucose monitoring and are used to calculate a user's glucose level. A primary data point may be, for example, the working electrode current measured for the analyte sensor during continuous analyte monitoring. Rather than illustrating the primary data points, FIG. 1A illustrates the time and voltage at which each primary data point is measured. For example, circle 102 in Figure 1A represents the time / voltage (3 minutes / 0.55 volts) at which a first primary data point (e.g., a first working electrode current) is measured for a sensor biased at a voltage of E. Similarly, circle 104 in Figure 1A represents the time / voltage (6 minutes / 0.55 volts) at which a second primary data point (e.g., a second working electrode current) is measured for a sensor biased at a voltage of E.

[0011] PPM current refers to a measurement of a current signal generated in response to PPM applied to a sensor during continuous analyte detection. PPM is described in more detail below in connection with FIG. 3B, which shows an exemplary PPM cycle including six steps of voltage potential, and FIG. 3E, which shows an exemplary current response to the PPM cycle of FIG. 3B. The currents generated during a PPM cycle, referred to as PPM currents, may be sampled and labeled as described in FIG. 3E (e.g., i11 is the first current sampled during the first voltage step, i12 is the second current sampled during the first voltage step, i13 is the third current sampled during the first voltage step, i21 is the first current sampled during the second voltage step, etc.). Other numbers and / or types of voltage potential steps may be used.

[0012] A reference sensor refers to a sensor used to generate primary data points and PPM currents in response to a reference glucose concentration, for example, as represented by a blood glucose meter (BGM) reading (e.g., primary currents and PPM currents measured to determine predictive equations including connection functions that are subsequently stored in a continuous analyte monitoring (CAM) device and used during continuous analyte sensing to determine analyte concentration).

[0013] For sensors used in non-whole-blood environments with relatively constant temperatures, such as sensors deployed in continuous in vivo sensing operations, sensor errors can be related to the short-term and long-term sensitivity of the sensor and the subsequent calibration method. There are several issues / challenges associated with such continuous sensing operations: (1) long commissioning (warm-up) times, (2) factory or field calibration, and (3) sensitivity changes during continuous sensing operations. These issues / problems appear to be related to sensor sensitivity as manifested by initial decay (commissioning / warm-up time), sensitivity changes due to the sensor's sensitivity to the environment during sensor production, and the environment / conditions in which the sensor is subsequently deployed.

[0014] In accordance with one or more embodiments of the present disclosure, the device and method are operable to probe an initial starting condition for continuous sensor operation of a sample analyte, and then probe the sensor condition at any point during the continuous sensing operation of the sensor.

[0015] Methods are provided for developing parameters for predictive equations (e.g., transfer functions and / or connection functions) that can be used to accurately determine analyte concentrations continuously from analyte sensors. Additionally, methods and apparatus for determining analyte concentrations are provided using PPM self-contained signals (e.g., working electrode currents resulting from application of PPM). Such methods and apparatus may enable determination of analyte concentrations while (1) overcoming the effects of different background interference signals, (2) equalizing or eliminating the effects of different sensor sensitivities, (3) reducing warm-up time at the beginning of a (long-term) continuous monitoring process, and / or (4) correcting for changes in sensor sensitivity over the course of a continuous monitoring process. These and other embodiments are described below with reference to FIGS. 1A-10.

[0016] In continuous glucose monitoring (CGM) biosensors, which typically operate at a constant applied voltage, the current from the mediator is continuously measured as a result of the enzymatic oxidation of the target analyte glucose. In practice, the current is typically measured or sensed every 3 to 15 minutes, or at another regular time interval, even though it is referred to as continuous. When a CGM sensor is first inserted / implanted into a user, there is an initial commissioning period, which can last from 30 minutes to several hours. Once a CGM sensor is commissioned, its sensitivity may still change for a variety of reasons. Therefore, during that initial period and after the commissioning period, the operating status of the sensor must be sensed to identify any changes in its sensitivity.

[0017] CGM sensor operation begins with an applied voltage E0 after the CGM sensor is inserted / implanted subcutaneously into the user. This applied voltage E0 is typically at a point on the redox plateau of the mediator. For the natural mediator of oxygen with the enzyme glucose oxidase, the oxidation plateau of hydrogen peroxide HO (the oxidation product of the enzymatic reaction) ranges from approximately 0.5 to 0.8 volts versus an Ag / AgCl reference electrode in a medium with a chloride concentration of approximately 100 to 150 mM. The operating potential of the glucose sensor can be set to 0.55 to 0.7 volts, which is within the plateau region.

[0018] The embodiments described herein use PPM as a periodic perturbation to an otherwise constant voltage potential applied to a working electrode of a subcutaneous biosensor in a continuous sensing operation (e.g., for monitoring a biological sample analyte such as glucose). During a continuous sensing operation, such as continuous glucose monitoring, the sensor working electrode current is typically sampled every 3-15 minutes (or some other frequency) for glucose level determination. These current measurements represent the primary current and / or primary data points used for analyte determination during the continuous sensing operation. In some embodiments, a periodic cycle of probing potential modulation (PPM) can be used after each primary current measurement, resulting in a self-sufficient group of currents that accompany each primary data point with information about the status and / or condition of the sensor / electrode.

[0019] A PPM can include one or more steps at a different potential than the constant voltage normally used during continuous analyte monitoring. For example, a PPM can include a first potential step above or below a constant voltage potential, a first potential step above or below a constant voltage potential followed by a return to a constant voltage potential, a series of potential steps above and / or below a constant voltage potential, a voltage step, a voltage pulse, a pulse of the same or different duration, a square wave, a sine wave, a triangular wave, or any other potential modulation. An example of a PPM sequence is shown in FIG. 3B.

[0020] As described, conventional biosensors used in continuous analyte sensing operate by applying a constant potential to the sensor's working electrode (WE). Under these conditions, the current from the WE is recorded periodically (e.g., every 3–15 minutes or at some other time interval). In this way, the biosensor generates a current that can be attributed solely to changes in analyte concentration, not to changes in the applied potential. That is, there is no non-steady-state current associated with the application of different potentials. While this approach simplifies continuous sensing operation, the current signal in the data stream resulting from the application of a constant potential to the sensor provides minimal information regarding the sensor status / condition. That is, the sensor current signal resulting from the application of a constant potential to the sensor provides little information related to challenges associated with long-term continuous sensor monitoring, such as lot-to-lot sensitivity variations, long warm-up times due to initial signal decay, changes in sensor sensitivity over the long-term monitoring process, influences from various background interference signals, and the like.

[0021] Subcutaneously implanted continuous glucose monitoring (CGM) sensors require periodic calibration against a reference glucose value. Traditionally, this calibration process involves obtaining a blood glucose meter (BGM) reading from a fingerstick glucose measurement or capillary glucose value and inputting the BGM value into the CGM device to set the calibration point for the CGM sensor during the next operating period. Typically, this calibration process involves daily or at least one fingerstick glucose measurement per day, as the sensitivity of the CGM sensor can vary from day to day. Although inconvenient, this is a necessary step to ensure the accuracy of the CGM sensor system.

[0022] Embodiments described herein include systems and methods for applying PPM on top of an otherwise constant voltage applied to an analyte sensor. Methods are provided for developing parameters for predictive equations, such as transfer functions and / or connection functions, that can be used to accurately determine analyte concentrations continuously from an analyte sensor.

[0023] Extracted Parameters: According to one or more embodiments of the present disclosure, devices and methods are operable to determine analyte concentration from currents under non-steady-state (NSS) conditions and steady-state (SS) degradation using extracted parameters, such as ratios R1, R4, and y45, described below. Using extracted parameters to determine analyte concentration represents a different and unique method for determining analyte concentration. Analyte-indicating parameters are extracted from non-steady-state and steady-state degenerated currents during continuous sensor operation that repeatedly alternates between steady-state and non-steady-state conditions. Using ratio parameters as analyte-indicating parameters has the advantages of being independent of sensor electrode size, requiring short warm-up times, and being relatively free of background signal. Additionally, ratio parameters can also provide a wide range of connectivity from in vitro to in vivo analytes, providing a narrow range of output analyte concentration variation from a wide range of sensor responses through the use of a connection function.

[0024] The PPM method described above provides potential modulation for a constant applied voltage. Primary data points obtained from steady-state conditions are used as indicators of analyte concentration, while associated PPM currents and PPM parameters are used to provide information about sensor and electrode conditions. An example PPM sequence and output current profile has a potential step from high to low before returning to high, thus alternating between steady-state and non-steady-state conditions.

[0025] In some embodiments, extracted parameters, or more specifically, ratio parameters, are used as input parameters in regression for error compensation. Several extracted parameters from the non-steady-state and steady-state degenerated currents, such as R1 (= i13 / i11), R4 (= i43 / i41), and y45 (= i43 / i51), as described below, are strongly correlated with analyte concentration. Because parameters such as the ratio of PPM currents are extracted, these parameters are unitless. Figure 2 shows a plot of one ratio parameter, R4, compared to the primary current, i10, in a series of linearity tests. It can be seen that there is a different response corresponding to the glucose concentration level. To better understand the characteristics of these ratio parameters, the sensor membrane and electrode boundary conditions are described below.

[0026] Steady-state condition: Conventional biosensors used for continuous analyte monitoring operate under steady-state conditions, which are established when the continuous monitoring sensor stabilizes after a settling time with a constant applied potential to the working electrode (WE). Under this condition, current is drawn from a constant flow of incident analyte molecules in a steady-state diffusion condition created by the outer membrane. This condition is illustrated in Figure 3A. Under this condition, the enzyme layer, and theoretically the boundary structure as defined by the outer membrane, create a boundary environment that allows for a linear C 媒介物質 The analyte concentration C is determined by the constant flux of the measurable species or reduced mediator, which is roughly defined by 外部 If there is no change in , the current will be proportional to the concentration gradient C of the measurable species at the electrode surface. 媒介物質 , which further depends on the analyte concentration gradient as defined by the boundary conditions.

[0027] Boundary environment: The boundary conditions in Figure 3A can be theoretically interpreted as follows: analyte concentration C 外部 is the membrane concentration C at the outer surface of the membrane 膜 The concentration in the membrane, C, is at some value that is in equilibrium with 膜The low C indicates that the membrane is designed to reduce the influx of analyte molecules so that the biosensor operates under steady-state conditions. 外部 and C 膜 The relationship between the equilibrium constant K 外部 =C 膜 / C 外部 <1. Furthermore, D 外部 A diffusion coefficient D lower than 膜 Together, the permeability of the membrane to the analyte, P 膜 =D 膜 *C 膜 defines the analyte throughput. As analyte molecules migrate toward the enzyme-coated electrode, they are rapidly decayed to zero by the enzyme. Meanwhile, the enzyme converts the analyte molecules to a measurable species that can be oxidized at the electrode, e.g., H2O2 with oxygen as a mediator for glucose oxidase. As this measurable species is produced, it will diffuse toward the electrode as well as toward the membrane. Under a constant applied voltage that completely oxidizes the measurable species, a constant flux of the measurable species will be drawn toward the electrode. Soon, a current will flow according to the concentration gradient (dC) of the measurable species at the electrode surface. 媒介物質 Under diffusion-limited conditions (meaning that the oxidation / consumption rate of the measurable species is at a maximum and limited only by the diffusion of the measurable species), a steady state is established where C 媒介物質 The concentration gradient is expected to be a straight line defined at the electrode surface as being zero, and a point at the membrane interface defined by an equilibrium condition reached by multiple processes (e.g., analyte flux entering the enzyme, analyte consumption and conversion by the enzyme, and diffusion of the measurable species). 媒介物質 is loosely determined by diffusion. This steady-state condition changes dynamically as the external analyte concentration changes.

[0028] Under operating conditions governed by probing potential modulation (PPM) cycles, the primary data points are actually sampled and recorded under steady-state conditions, as the boundary environment resumes steady-state conditions after each non-steady-state potential modulation cycle, as shown in FIG. 3B (which illustrates a PPM sequence or cycle having six steps, although fewer, more, or different potential modulation steps may be used).

[0029] Potential modulation and non-steady-state conditions: If the applied potential is modulated away from a constant voltage, such as a potential step of 0.55 V to 0.6 V (step 1 in Figure 3B and E0 to E1 in Figure 3D), but still within the oxidation plateau of the mediator (diffusion-limited region on the V axis), there will still be some finite current generated with a small decay. This is still exp(E 印加 -E 0’ ) is an induced current process resulting from an asymmetric plateau governed by E 印加 is the applied voltage, and E 0’ is the formal potential of the redox species, representing its electrochemical properties. This finite current with slight decay is sometimes referred to as a plateau degeneration current, representing slightly different oxidation states across the plateau. The current-voltage relationship for a mediator is generally illustrated in Figure 3D. An example of such an output current is shown in Figure 3E, where the PPM currents are labeled i11, i12, and i13, while i10 is the primary current under steady-state conditions. For example, i11 is the first current sampled during the first potential step.

[0030] When the applied potential is reversed to a lower voltage, or specifically, to E1-E2 in FIG. 3D and then to E3 (steps 2 and 3 in FIG. 3B), two things can happen: (1) the measurable species is no longer completely oxidized at the electrode surface due to the lower potential, and (2) there is partial reduction of the measurable species or the oxidized form of the mediator, accompanied by the generation of a negative current. The combined effect of these two events causes excess measurable species to accumulate at and near the electrode surface. Thus, the concentration profile is perturbed from a linear state that reaches zero at the electrode surface. This state is referred to as a non-steady state and is illustrated in FIG. 3C, where C 媒介物質 is non-zero at the electrode surface. The output currents of such effects are shown as negative and are labeled i21, i22, i23, and i31, i32, i33 in Figure 3E for steps 2 and 3 in Figure 3B. The negative currents suggest a partial decrease in the potential step from high to low. The disturbance of the steady-state condition is due to the increase in the membrane (C 膜 and C 外部 ) while the boundary environments inside and outside remain unchanged, the potential modulation process occurs only near the electrode surface for a short time.

[0031] Alternating NSS and SS Conditions: As shown in Figures 3B and 3D, when the potential is reversed again from E3 to E2 in step 4, some of the accumulated measurable species is consumed, and the rate of oxidation there is higher than that set by the higher potential E2. Even though E2 is not in the plateau region of the redox species, this step results in a sudden consumption of the measurable species, causing a jump in the current output from the non-steady-state concentration, thus providing a strong indication of concentration. Step 5 from E2 to E1 in Figure 3B further completes the non-steady-state oxidation of the excess species, again positioning the sensor at an operating potential above the plateau region. Step 6 in Figure 3B takes a negative plateau regression step, returning to the original potential leading to the resumption of steady-state conditions before the next potential modulation cycle. Such conditions are, in theory, the same as those in Figure 3A. Thus, as the PPM cycle is repeated, steady-state and non-steady-state conditions alternate, providing a signal for analyte concentration determination.

[0032] Further consideration of the extracted ratio parameters: R1 (= i13 / i11), R4 (= i43 / i41), and y45 (= i43 / i51), as examples, provides the following insights. Parameter R1 is extracted from the PPM current at potential step 1 (Figure 3B), which is referred to as plateau regression because the current is from the quasi-plateau region. Parameter R4 is extracted from the PPM current at potential step 4 (Figure 3B), which is under non-steady-state conditions. According to the process described in Figures 3A-3C, this potential step provides a clean and sharp oxidation of the excess measurable species that accumulated during the brief time the electrode was at potential E3 (Figure 3D), a condition for partial oxidation of the measurable species. Therefore, this parameter is both an NSS and a ratio parameter. Parameters R1 (= i13 / i11) and R4 (= i43 / i41) are each extracted from the current at the same potential step (step 1 for R1 and step 4 for R4). Finally, the parameter y45 (= i43 / i51) is defined according to the Yij format (last step current / first step current, as explained below) and is also extracted from the non-steady-state current. This parameter represents the current ratio from the currents across the two potential steps.

[0033] Sensor Electrode Size Independence: When R1, R4, and y45 are used to indicate analyte concentration, they offer the advantage of being independent of electrode size. Figure 4A shows a comparison of the steady-state i10 current from two sensor types, with Sensor 1 having twice the electrode area of ​​Sensor 2. The response current of Sensor 1 is twice as sensitive as Sensor 2 in linearity tests of 50 mg / dL, 100 mg / dL, 200 mg / dL, 300 mg / dL, and 450 mg / dL glucose solutions. This is expected. In contrast, when current ratios such as R1, R4, and y45 are used as indicator parameters, the response with different ratio parameters is virtually independent of electrode size. Small differences can be attributed solely to differences in sensor manufacturer / lot. These comparison plots are shown in Figures 4B, 4C, and 4D for R1, R4, and y45, respectively. The correlation between these ratio parameters and analyte concentration is better described by a non-linear relationship, such as a second order polynomial.

[0034] Short Initial Warm-Up Time: Another advantage of using a ratio parameter to indicate analyte concentration is the sensor's nearly constant initial decay during continuous monitoring. Figure 5A directly compares the current i10 of a primary data point with the R4 ratio from the same sensor in the very first linearity test of a long-term study. The clear R4 value corresponds to glucose levels, but the initial small decay of R4 is even more striking. This advantage is better understood by comparing the normalized initial response from a steady-state current, such as the primary data point i10, with the ratio parameter R4 extracted from the non-steady-state current. Figure 5B compares the average normalized i10 and R4 values ​​for the initial responses from seven sensors. It can be seen that the i10 current takes approximately 60 minutes to settle, dropping 35% from the initial reading after immersion in the initial solutions (50 mg / dL and 100 mg / dL), yet the average R4 ratio drops only 5% from its initial reading. This means that the warm-up time of the sensor can be very fast using the R4 in the order of 10-15 minutes without having to rely on compensation methods / algorithms (as explained below).

[0035] Independence of background signal: Another advantage of using ratio parameters for analyte concentration determination is that they are relatively free from background effects from different oxidizing species. One drawback of the steady-state operating conditions of continuous monitoring is that other chemical species that can cross the membrane and be oxidized at the electrode surface also affect the overall current at each current sampling time. These oxidizing species are not the target analyte and therefore are interfering species that affect the overall signal. Therefore, one major concern with continuous analyte detection is the background effect in the sensor's output current. This can be seen in Figure 6A, where one CGM sensor is operated in the PPM mode and another is operated at a constant applied voltage (NPPM or np). The CGM sensors were tested with glucose solutions containing four different levels of acetaminophen, representing background signals: 0.2 mg / dL, 0.6 mg / dL, 1.2 mg / dL, and 1.8 mg / dL. An acetaminophen concentration of 0.2 mg / dL was considered equivalent to a normal level of interfering background signal, and 0.6 mg / dL was considered a high level. Acetaminophen concentrations of 1.2 and 1.8 mg / dL were considered extremely high. One linearity test at five levels of glucose concentration, 50, 100, 200, 300, and 450 mg / dL, was performed for each level of background acetaminophen.

[0036] The responses of the primary data points from the NPPM method (without PPM cycling) and the PPM method (with PPM cycling) are shown in Figures 6B and 6C, respectively. The difference in response slope is due to the two different sensors operating in NPPM and PPM modes. The effect of different background levels of acetaminophen is virtually identical as indicated by the intercepts of the NPPM and PPM methods, which increase by approximately 75%, 150%, and 250% as the acetaminophen interference level increases from 0.2 mg / dL to 0.6, 1.2, and 1.8 mg / dL. The primary data points from the NPPM sensor operating under steady-state conditions show a dependence of the intercept on the level of added acetaminophen, indicating that the primary data points from the PPM method are also from steady-state conditions and are identical to those from the NPPM method.

[0037] In contrast, when a ratio parameter such as R4 is used to indicate glucose concentration, the response is relatively independent of different levels of background acetaminophen, as shown in Figures 6D and 6E. Because their responses are relatively independent of the background signal, ratio parameters as signals indicative of analyte concentration allow more regression resources (parameter terms) to be directed toward further improving the accuracy of the analyte concentration determination.

[0038] Response curves and wide-area connections: Figures 7A-7C show the G versus future ratio parameters, along with the raw i10 signals from the primary data points under steady-state conditions, for the same data set consisting of a group of seven sensors. 基準 The three plots of G 基準is the gravimetric glucose concentration, which was determined to within ±2% of nominal value using a YSI glucose analyzer (commercially available from YSI Incorporated, Yellow Springs, Ohio). The second-order polynomials in each ratio plot serve as reference correlations for each of these three ratio parameters from the regression of the mean values ​​of the ratios at each glucose concentration. The independent and dependent variables in these three plots are inverted so that the ratio parameters can be input directly into the polynomials to obtain the glucose concentration, instead of trying to solve a quadratic equation to obtain the glucose concentration. The ratio response is approximately three-fold from low to high and is the same as that shown in Figure 7D for the linear response plot of the i10 current.

[0039] The ratio parameter as an analyte indicating parameter can also provide a wide range of connectivity from in vitro to in vivo glucose across a wide range of responses in the same way as the i10 current. That is, a single conversion function can be used to convert the ratio R4 value to G 生 value, and subsequently the error ΔG / G via a connection function, as further explained below. 生 Other methods utilizing the R4 ratio (or other PPM ratio parameters) to determine analyte concentration can also be used. The results of compensation by the connection function for each parameter are summarized in table 800 shown in FIG. 8. The results show that the ratio parameters can converge a widely scattered sensor response to a narrow range of glucose values ​​by the connection function.

[0040] Use of conversion and connection functions Assuming the uncertainty of a one-to-one correlation between in vitro and in vivo sensitivity, a method is disclosed herein for connecting in vitro glucose to in vivo glucose by applying a unified "transformation function" to a wide range of data for sensor response, followed by a "connection function" to reduce the glucose error to a narrow band. This unified conversion function converts the raw or "initial" glucose value G 生= f(signal), where "signal" is the measured current signal (or a parameter derived from one or more measured signals), and "f" can be a linear or nonlinear function. If the transfer function f is nonlinear, then no sensitivity or response slope (as explained below) is applied.

[0041] In its simplest form, the unified conversion function can be a linear relationship between the measured current signal and the reference glucose level obtained from in vitro test data. For example, the unified conversion function can be a linear relationship between the glucose signal (e.g., Iw-Ib, Rl, R4, y45, or another PPM current signal), the slope, and the reference glucose G 基準 There can be a linear relationship between Signal = Gradient * G 基準 As a result, the following is true: G 基準 = signal / gradient Here, the gradient is the compound gradient (gradient 複合 ), also referred to as the unified composite gradient. The above relationship is then used to calculate the initial or raw glucose G 生 can be calculated as follows: G 生 = signal / gradient 複合

[0042] As explained above, PPM current signal parameters such as Rl, R4, and y45 may be less sensitive to interference effects and exhibit lower warm-up sensitivity. Thus, in some embodiments provided herein, a unified composite slope may be determined from PPM current signal parameters such as Rl, R4, and y45, or another suitable PPM current signal. In some embodiments, rather than using a linear conversion function, a nonlinear conversion function such as a polynomial may be used (e.g., to better fit the various responses of the sensor). For example, FIGS. 7A, 7B, and 7C show the conversion of glucose G 基準1 shows polynomial fits of R1, R4, and y45 to y = 0. These polynomial fits can serve as connection functions to determine initial or raw glucose values ​​from R1, R4, or y45. For R1: G 生 =4351.9*(Rl) 2 -4134.4*(R1)+1031.9 For R4: G 生 =5068*(R4) 2 -2213.3*(R4)+290.05 For y45: G 生 =6266.8*(y45) 2 -1325.2*(y45)+117.49

[0043] Other relationships may be used. Note that the equivalent form of Iw-Ib of the primary data (i10) can be used. However, background subtraction is not used because R1, R4, and y45 are relatively independent of interference effects from other interfering species. In some embodiments, multiple conversion functions may be used.

[0044] The connection function has individual errors (% bias = 100% * ΔG / G = 100% * (G 生 -G 基準 ) / G 基準 ) to obtain a narrow range of glucose fluctuations, a single transformation makes the in vitro to in vivo connection a simple matter without calibration. This connection function is ΔG / G 生 Based on the value of the initial or raw glucose G 生 By narrowing the error band in this way, the connection function is called a calibration-free connection function that connects in vitro to in vivo, meaning that all sensor responses are accommodated within a narrow error band.

[0045] A connection function is said to be a global connection from in vitro glucose to in vivo glucose if the connection function provides predicted in vivo glucose values ​​within a narrow band of error without calibration. In this context, we are not seeking to establish a one-to-one correspondence between in vitro and in vivo sensitivity. Rather, the connection function will provide glucose values ​​from a sensor within its sensitivity range as long as the sensor responds to glucose. The response can be linear or nonlinear.

[0046] Taking advantage of the wealth of information about the CGM sensor from the PPM current, this function is derived from the PPM current and related parameters. Each response data point in a periodic cycle is converted to a glucose value G by a complex transformation function. 生 When converted to 生 =(G 生 -G 基準 ) / G 基準 There exists. G 接続 =G 基準 By setting G 接続 =G 生 / (1+ΔG / G 生 )=G 生 / (1 + connection function), where connection function = ΔG / G 生 = f (PPM parameter). One way to derive the connection function is to use the relative error ΔG / G 生 as the target of the input parameters from the multivariate regression and the PPM parameters.

[0047] In summary, in some embodiments, the PPM parameters of R1, R4, or y45 are used as part of a conversion function to convert the raw current signal information into raw or initial glucose values ​​G 生 It can be converted into G 生 Once is known, a connection function can then be used to find the compensated or final glucose signal or concentration, G 複合 For example, the connection function can be calculated using the SS signal (i10) and the NSS signal (PPM signal) as input parameters and the relative error ΔG / G生 can be derived from in vitro data using as the target for multivariate regression. An exemplary connectivity function CF is provided below. It will be understood that other numbers and / or types of terms may be used. <h2 style=";text-align:left;direction:ltr">CF=30.02672+3.593884*ni23-11.74152*R3-0.915224*z54+0.026557*GR41-0.061011*GR43+0.17876*Gy43+0.355556*R62R54-1.910667*R54R42-0.367626*R54R43-0.010501*GR43R31-4.92585*z61z63-48.9909*z63z32-22.97277*z64z42-2.566353*z64z43+69.93413*z65z52-75.5636*z65z 32-16.28583*z52z32…+0.017588*Gy51y42+0.020281*Gy51y32-1.92665*R62z51-0.348193*R62z53-0.901927*R62z31+75.69296*R64z52-222.675 *R65z52-29.05662*R65z53-142.145*R65z32+15.47396*R51z53+74.8836*R51z32+23.1061*R42z32+0.0018396*GR52z41+0.100615*GR31z32-8.89841*R61y52+1.873765*R61y42+2.459974*R61y43…+4.911592*z4ly31-1.04261*z31y32-0.014889*Gz61y42+0.007133*Gz63y65+0.019989*Gz64y 51+0.004536*Gz64y43-0.01605*Gz65y54+0.00011*Gz52y32+0.004775*G z53y54-0.531827*d32-0.026387*Gdl1-0.010296*Gd21+0.003426*Gd32- 6.350168*d21d31+8.39652*d22d31-0.0329025*Gd11d31-0.039527*av1- 2.342127*av1il0+0.550159*av3i10-4.87669*av14-0.139865*av16+14. 59835*av25-9.31e-5*Gav3-0.000143*Gav4+0.001157*Gav16-0.022394* Gav25-0.000888*Gav26-0.928135*R30+2.307865*R50-4.501269*z60-7.491846*w65w51-3.56458*w65w53+7.147535*w43w32…. .

[0048] The input parameters of the connection function CF can be, for example, of the following type:

[0049] Probing current: Probing potential modulation currents i11, i12, i13, ..., i61, i62, i63, where the first digit (x) in the ixy format indicates the potential step, while the second digit (y) indicates which current measurement was taken after the application of the potential step (e.g., the first, second, or third measurement).

[0050] R parameters: These ratios are calculated by dividing the ending PPM current by the first PPM current within one potential step, e.g., R1 = i13 / i11, R2 = i23 / i21, R3 = i33 / i31, R4 = i43 / i41, R5 = i53 / i51, and R6 = i63 / i61.

[0051] X-type parameters: The general format of this type of parameter is given by the ending PPM current of the later potential step divided by the ending PPM current of the previous potential step. For example, the parameter x is given by i 6 3 / i 1 3, where i63 is the ending PPM current for step 6, while i13 is the ending PPM current for step 1, for the three recorded currents per step. Furthermore, x61 = i63 / i13, x62 = i63 / i23, x63 = i63 / i33, x64 = i63 / i43, x65 = i63 / i53, x51 = i53 / i13, x52 = i53 / i23, x53 = i53 / i33, x54 = i53 / i43, x41 = i43 / i13, x42 = i43 / i23, x43 = i43 / i33, x31 = i33 / i13, x32 = i33 / i23, and x21 = i23 / i13.

[0052] Y-type parameters: The general format of this type of parameter is given by the ending PPM current of the later potential step divided by the beginning PPM current of the previous potential step. For example, the parameter y is given by i 6 3 / i 1 1, where i63 is the ending PPM current of step 6, while i11 is the initial PPM current of step 1, for the three recorded currents per step. Furthermore, y61 = i63 / i11, y62 = i63 / i21, y63 = i63 / i31, y64 = i63 / i41, y65 = i63 / i51, y51 = i53 / i11, y52 = i53 / i21, y53 = i53 / i31, y54 = i53 / i41, y41 = i43 / i11, y42 = i43 / i21, y43 = i43 / i31, y31 = i33 / i11, y32 = i33 / i21, and y21 = i23 / i11.

[0053] Z-type parameters: The general format of this type of parameter is given by the initial PPM current of the later potential step divided by the ending PPM current of the previous potential step. For example, the parameter z is given by i 6 1 / i 1 3, where i61 is the initial PPM current for step 6 and i13 is the ending PPM current for step 1, for the three recorded currents per step. Furthermore, z61 = i61 / i13, z62 = i61 / i23, z63 = i61 / i33, z64 = i61 / i43, z65 = i61 / i53, z51 = i51 / i13, z52 = i51 / i23, z53 = i51 / i33, z54 = i51 / i43, z41 = i41 / i13, z42 = i41 / i23, z43 = i41 / i33, z31 = i31 / i13, z32 = i31 / i23, and z21 = i21 / i13.

[0054] Additional terms include normalized currents: ni11 = i11 / i10, ni12 = i12 / i10, ..., relative differences: d11 = (i11-i12) / i10, d12 = (i12-i13) / i10, ..., average currents for each PPM potential step: av1 = (i11 + i12 + i13) / 3, av2 = (i21 + i22 + i23) / 3, ..., and average current ratios: av12 = av1 / av2, av23 = av2 / av3, .... Other terms include: GR1 = G 生 *R1, Gz61=G 生 *z61, Gy52=G 生 *y52, ..., R63R51=R63 / R51, R64R43=R64 / R43, ..., z64z42=z64 / z42, z65z43=z65 / z43, ..., d11d31=d11 / d31, d12d32=d12 / d32, ..., Gz61y52=G*z61 / y52, ... etc.

[0055] Other types of parameters such as PPM current differences or relative differences, or ratios of intermediate PPM currents, that convey equivalent or similar information may also be used.

[0056] Therefore, the extracted parameters R1, R4, and y45 can be used to indicate the raw glucose analyte concentration, and the connection function can be used with the raw glucose analyte concentration to connect in vitro to in vivo glucose. G 生 transformation function to G 複合 The compensation results with the connection function to y45 are summarized in Figure 8. The results show that R1, R4, and y45 can be used as analytes to indicate signals, and that the connection function can converge the widely scattered responses to a narrow range of glucose values.

[0057] In some embodiments, the PPM cycle or sequence is designed to take at most half the time of the primary data cycle (e.g., 3-5 minutes) to allow sufficient time for the application of constant voltage to the working electrode for steady-state conditions to resume before the next primary data point is recorded. In some embodiments, the PPM cycle can be on the order of about 1-90 seconds, or up to 50% of a regular 180-second primary data cycle.

[0058] In one or more embodiments, a PPM cycle can be approximately 10-40 seconds long and / or can include two or more modulated potential steps around the mediator's redox plateau. In some embodiments, a PPM sequence can be as long as 10-20% of a regular primary data point cycle. For example, if a regular primary data point cycle is 180 seconds (3 minutes), a 36-second PPM cycle is 20% of the primary data point cycle. The remaining time in the primary data cycle allows steady-state conditions to resume at a constant applied voltage. For the potential steps in a PPM cycle, the succession time is transient, and as a result, the boundary conditions of the measurable species created by these potential steps are non-steady-state. Thus, each potential step can be as long as 1-15 seconds in some embodiments, approximately 3-10 seconds in other embodiments, and approximately 4-6 seconds in still other embodiments.

[0059] In some embodiments, the probing potential modulation (PPM) can be stepped into the potential region of non-diffusion-limited redox conditions, or the kinetic region of the mediator (meaning that the output current depends on the applied voltage, with higher applied voltages producing higher output currents from the electrode). For example, E2 and E3 in FIG. 3D (steps 2 and 3 in FIG. 3B) are two potential steps in the kinetic region of the mediator that produce non-steady-state output currents from the electrode. When the potential steps are reversed, applied voltages E2 and E1 of the same magnitude are resumed to probe the non-steady-state output current from the electrode.

[0060] Different embodiments may be used to accommodate non-steady-state conditions. For example, non-steady-state conditions may also be probed by one step going directly to target potential E2 and returning to starting potential E1, followed by a second probing potential step going directly to a different potential E3 in a kinetic region having a different non-steady-state condition and then returning directly to starting potential E1. The idea is to modulate the applied potential to create alternating steady-state and non-steady-state conditions for the measurable species at the electrode surface, so that the signal from the non-steady-state condition can be used to determine the analyte concentration.

[0061] Exemplary CGM Systems 9A illustrates a high-level block diagram of an exemplary CGM device 900 according to embodiments provided herein. While not shown in FIG. 9A , it should be understood that various electronic components and / or circuits are configured to couple to a power source, such as, but not limited to, a battery. The CGM device 900 includes a bias circuit 902 that can be configured to couple to a CGM sensor 904. The bias circuit 902 can be configured to apply a bias voltage, such as a continuous DC bias, to the analyte-containing fluid through the CGM sensor 904. In this exemplary embodiment, the analyte-containing fluid can be human interstitial fluid, and the bias voltage can be applied to one or more electrodes 905 (e.g., working electrode, background electrode, etc.) of the CGM sensor 904.

[0062] The bias circuit 902 can also be configured to apply a PPM sequence to the CGM sensor 904, such as shown in FIG. 1C or another PPM sequence. For example, the PPM sequence can be applied initially and / or intermediately, or for each primary data point. The PPM sequence can be applied, for example, before, after, or both before and after the measurement of the primary data point.

[0063] In some embodiments, the CGM sensor 904 may include two electrodes, and a bias voltage and probing potential modulation (PPM) may be applied between the pair of electrodes. In such cases, a current may be measured through the CGM sensor 904. In other embodiments, the CGM sensor 904 may include three electrodes, such as a working electrode, a counter electrode, and a reference electrode. In such cases, a bias voltage and probing potential modulation may be applied between the working electrode and the reference electrode, and a current may be measured, for example, through the working electrode. The CGM sensor 904 includes a chemical that reacts with the glucose-containing solution in a reduction-oxidation reaction, affecting the concentration of charge carriers and the time-dependent impedance of the CGM sensor 904. Exemplary chemicals include glucose oxidase, glucose dehydrogenase, or the like. In some embodiments, a mediator such as ferricyanide or ferrocene may be used.

[0064] The continuous bias voltage generated and / or applied by bias circuit 902 can be, for example, in the range of about 0.1 to 1 volts relative to a reference electrode. Other bias voltages can be used. Exemplary PPM values ​​are described previously.

[0065] The PPM current and the non-PPM (NPPM) current through the CGM sensor 904 in the analyte-containing fluid in response to the PPM and constant bias voltage are measured as current measurements (I 測定 ) circuit 906 (also referred to as a current sensing circuit). The current measurement circuit 906 may be configured to sense and / or record (e.g., using a suitable current-to-voltage converter (CVC)) a current measurement signal having a magnitude indicative of the magnitude of the current transmitted from the CGM sensor 904. In some embodiments, the current measurement circuit 906 may include a resistor having a known nominal value and a known nominal accuracy (e.g., in some embodiments, 0.1% to 5%, or even less than 0.1%) through which the current transmitted from the CGM sensor 904 passes. The voltage developed across the resistor in the current measurement circuit 906 represents the magnitude of the current and may be referred to as the current measurement signal.

[0066] In some embodiments, the sample circuit 908 may be coupled to the current measurement circuit 906 and configured to sample the current measurement signal. The sample circuit 908 can then generate digitized time-domain sample data representing the current measurement signal (e.g., a digitized glucose signal). For example, the sample circuit 908 may be any suitable A / D converter circuit configured to receive the analog current measurement signal and convert it into a digital signal having a desired number of bits as an output. In some embodiments, the number of bits output by the sample circuit 908 may be 16 bits, although more or fewer bits may be used in other embodiments. In some embodiments, the sample circuit 908 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, a sampling rate such as about 10 kHz to 100 kHz may be used for downsampling to further reduce the signal-to-noise ratio. Any suitable sampling circuit may be used.

[0067] 9A , processor 910 may be coupled to sample circuit 908 and may be coupled to memory 912. In some embodiments, processor 910 and sample circuit 908 are configured to communicate directly with each other via a wired path (e.g., via a serial or parallel connection). In other embodiments, processor 910 and sample circuit 908 may be coupled by memory 912. In this arrangement, sample circuit 908 writes digital data to memory 912, and processor 910 reads digital data from memory 912.

[0068] The memory 912 may have stored therein one or more predictive formulas 914 for use in determining glucose values ​​based on the primary data points (NPPM current) and PPM current (from the current measurement circuit 906 and / or sample circuit 908). In some embodiments, these predictive formulas may include one or more conversion functions and / or connection functions, as described below. For example, in some embodiments, two or more predictive formulas may be stored in the memory 912, each for use with a different segment (time period) of CGM collected data. In some embodiments, the memory 912 may include a predictive formula based on a primary current signal generated by application of a constant voltage potential applied to a reference sensor, and multiple PPM current signals generated by application of a PPM sequence applied between primary current signal measurements.

[0069] The memory 912 may also store instructions therein. In various embodiments, the processor 910 may be a computational resource such as, but not limited to, a microprocessor, a microcontroller, an embedded microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA) configured to operate as a microcontroller, or the like.

[0070] In some embodiments, the instructions stored in memory 912 may include instructions that, when executed by the processor 910, cause the processor 910 to (a) cause the CGM device 900 to measure current signals (e.g., primary current signals and PPM current signals) from the interstitial fluid (via the bias circuit 902, the CGM sensor 904, the current measurement circuit 906, and / or the sample circuit 908); (b) store the current signals in memory 912; (c) calculate prediction formula parameters, such as ratios (and / or other relationships) of currents from different pulses, voltage steps, or other voltage changes in a PPM sequence; (d) employ the calculated prediction formula parameters to calculate a glucose value (e.g., concentration) using the prediction formula; and / or (e) communicate the glucose value to a user.

[0071] The memory 912 may be any suitable type of memory, such as, but not limited to, one or more of volatile memory and / or nonvolatile memory. Volatile memory may include, but is not limited to, static random access memory (SRAM) or dynamic random access memory (DRAM). Nonvolatile 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 types in either NOR or NAND configurations, and / or in either stacked or planar arrangements, and / or in either single-level cell (SLC), multi-level cell (MLC), or combined SLC / MLC arrangements), resistive memory, filamentary memory, metal oxide memory, phase-change memory (e.g., chalcogenide memory), or magnetic memory. The memory 912 may be packaged, for example, as a single chip or as multiple chips. In some embodiments, the memory 912 may be embedded with one or more other circuits in an integrated circuit, such as, for example, an application-specific integrated circuit (ASIC).

[0072] As mentioned above, memory 912 may have instructions stored therein that, when executed by processor 910, cause processor 910 to perform various operations specified by one or more of the stored instructions. Memory 912 may further have a portion reserved for one or more "scratch pad" storage areas that may be used for read or write operations by processor 910 in response to execution of one or more of the instructions.

[0073] 9A , the bias circuit 902, CGM sensor 904, current measurement circuit 906, sample circuit 908, processor 910, and memory 912 including predictive calculation formula 914 may be disposed within a wearable sensor portion 916 of CGM device 900. In some embodiments, wearable sensor portion 916 may include a display 917 for displaying information such as glucose concentration information (e.g., without the use of external equipment). Display 917 may be any suitable type of human-sensitive display, such as, but not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, or an organic light-emitting diode (OLED) display.

[0074] 9A , the CGM device 900 may further include a portable user device portion 918. A processor 920 and a display 922 may be disposed within the portable user device portion 918. The display 922 may be coupled to the processor 920. The processor 920 may control text or images shown by the display 922. The wearable sensor portion 916 and the portable user device portion 918 may be communicatively coupled. In some embodiments, the communicative coupling between the wearable sensor portion 916 and the portable user device portion 918 may be by wireless communication via transmitter and / or receiver circuitry, such as, for example, transmit / receive circuit TxRx 924a of the wearable sensor portion 916 and transmit / receive circuit TxRx 924b of the portable user device 918. Such wireless communication may be by any suitable means, including, but not limited to, a standards-based communication protocol, such as the Bluetooth® communication protocol. In various embodiments, wireless communication between the wearable sensor portion 916 and the portable user device portion 918 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 916 and the portable user device portion 918 may be connected by one or more wires.

[0075] Display 922 may be any suitable type of human-sensitive display, such as, but not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, or an organic light-emitting diode (OLED) display.

[0076] 9B, an exemplary CGM device 950 is shown that is similar to the embodiment illustrated in FIG. 9A but has a different division of components. In CGM device 950, wearable sensor portion 916 includes bias circuitry 902 coupled to CGM sensor 904 and current measurement circuitry 906 coupled to CGM sensor 904. Portable user device portion 918 of CGM device 950 includes sample circuitry 908 coupled to processor 920 and display 922 coupled to processor 920. Processor 920 is further coupled to memory 912, which may have prediction formulas 914 stored therein. In some embodiments, processor 920 in CGM device 950 can also perform the functions described above, for example, performed by processor 910 of CGM device 900 of FIG. 9A. The wearable sensor portion 916 of the CGM device 950 may be smaller and lighter, and therefore less invasive, than the CGM device 900 of FIG. 9A because it does not include the sample circuit 908, processor 910, memory 912, etc. Other component configurations may be used. For example, as a variation on the CGM device 950 of FIG. 9B , the sample circuit 908 could still remain on the wearable sensor portion 916 (such that the portable user device 918 receives the digitized glucose signal from the wearable sensor portion 916).

[0077] FIG. 10 is a side schematic view of an exemplary glucose sensor 904 according to embodiments provided herein. In some embodiments, the glucose sensor 904 may include a working electrode 1002, a reference electrode 1004, a counter electrode 1006, and a background electrode 1008. The working electrode 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 charge carriers and the time-dependent impedance of the CGM sensor 904). In some embodiments, the working electrode 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 1002 include glucose oxidase, glucose dehydrogenase, or the like. The enzyme component may be immobilized on the electrode surface by a cross-linking agent such as glutaraldehyde. An outer membrane layer may be applied over the enzyme layer to protect the entire interior components, including the electrode and enzyme layer. In some embodiments, a mediator such as ferricyanide or ferrocene may be used. Other chemical catalysts and / or mediators may be used.

[0078] In some embodiments, the reference electrode 1004 may be formed from Ag / AgCl. The counter electrode 1006 and / or background electrode 1008 may be formed from a suitable conductor, such as platinum, gold, palladium, or the like. Other materials may be used for the reference, counter, and / or background electrodes. In some embodiments, the background electrode 1008 may be identical to the working electrode 1002, but may not include a chemical catalyst and / or mediator. The counter electrode 1006 may be separated from the other electrodes by a separation layer 1010 (e.g., polyimide or another suitable material).

[0079] FIG. 11 illustrates an example method 1100 for determining a glucose value during a continuous glucose monitoring measurement according to embodiments provided herein. Method 1100 includes, at block 1102, providing a CGM device (e.g., CGM device 900 or 950 of FIGS. 9A and 9B ) including a sensor, memory, and a processor. Method 1100 also includes, at block 1104, applying a constant voltage potential to the sensor (e.g., E0 of FIG. 1A ). At block 1106, method 1100 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in memory. At block 1108, method 1100 includes applying a probing potential modulation sequence to the sensor (e.g., the PPM sequence of FIG. 3B ). At block 1110, method 1100 includes measuring a probing potential modulation current signal resulting from the probing potential modulation sequence and storing the measured probing potential modulation current signal in memory. Method 1100 further includes determining an initial glucose concentration based on the conversion function and a ratio of the measured probing potential-modulated current signals at block 1112, determining a connection function value based on the primary current signal and the plurality of probing potential-modulated current signals at block 1114, and determining a final glucose concentration based on the initial glucose concentration and the connection function value at block 1116. The final glucose concentration may be communicated to a user (e.g., via display 917 or 922 of FIG. 9A or 9B ).

[0080] Some embodiments, or portions thereof, may be provided as a computer program product or software that may include a machine-readable medium having non-transitory instructions stored therein, which can be used to program a computer system, controller, or other electronic device according to one or more embodiments.

[0081] While the present disclosure is susceptible to various modifications and alternative forms, specific method and apparatus embodiments have been shown by way of example in the drawings and are herein described in detail. It will be understood, however, that the specific methods and apparatus disclosed herein are not intended to limit the scope of the disclosure or the claims.

Claims

1. 1. A method of operating a continuous glucose monitoring (CGM) system, the CGM system including a sensor, a memory storing computer program code, and a processor in communication with the memory and the sensor, the computer program code, when executed by the processor, performing: applying a constant voltage potential to the sensor in the CGM system; measuring a primary current signal resulting from the constant voltage potential in the CGM system and storing the primary current signal in the memory; applying a probing potential modulation sequence to the sensor in the CGM system; measuring a plurality of probing potential modulation current signals resulting from the probing potential modulation sequence in the CGM system and storing the plurality of probing potential modulation current signals in the memory; calculating a plurality of ratio parameters from the plurality of probing potential modulated current signals in the CGM system; determining an initial glucose concentration by applying one of the ratio parameters to a conversion function in the CGM system; determining a connection function value based on the primary current signal and the plurality of ratio parameters in the CGM system; determining a final glucose concentration based on the initial glucose concentration and the connection function value.

2. The operating method of claim 1, wherein applying the probing potential modulation sequence includes providing a first voltage potential greater than the constant voltage potential, a second voltage potential less than the constant voltage potential, a third voltage potential less than the second voltage potential, and a fourth voltage potential greater than the third voltage potential.

3. The operating method described in claim 2, wherein determining the initial glucose concentration is calculated based on the conversion function and a ratio of a probing potential modulated current signal measured during the first voltage potential.

4. The operating method described in claim 2, wherein determining the initial glucose concentration is calculated based on the conversion function and the ratio of a probing potential modulated current signal measured during the fourth voltage potential.

5. The operating method of claim 1, wherein applying the probing potential modulation sequence includes providing a first voltage potential greater than the constant voltage potential, a second voltage potential less than the constant voltage potential, a third voltage potential less than the second voltage potential, a fourth voltage potential greater than the third voltage potential, and a fifth voltage potential greater than the fourth voltage potential.

6. The operating method described in claim 5, wherein determining the initial glucose concentration is calculated based on the conversion function and the ratio of probing potential modulated current signals measured during the fourth voltage potential and the fifth voltage potential.

7. The method of claim 1 , wherein the primary current signal and the plurality of probing potential-modulated current signals are working electrode current signals.

8. The method of claim 1, wherein the primary current signal is measured every 3 to 15 minutes.

9. The method of claim 1 , wherein the ratio of the measured probing potential-modulated current signal is independent of electrode size.

10. 1. A continuous glucose monitoring (CGM) device comprising: A wearable portion, a sensor configured to generate a current signal from the interstitial fluid; a processor; a memory coupled to the processor; a wearable portion having a transmitter circuit coupled to the processor; the memory includes a connection function and computer program code stored in the memory, the computer program code, when executed by the processor, causing the CGM device to: applying a constant voltage potential to the sensor; measuring a primary current signal produced by the constant voltage potential; storing the primary current signal in the memory; applying a probing potential modulation sequence to the sensor that is different from the constant voltage potential; measuring a plurality of probing potential modulation current signals resulting from the probing potential modulation sequence; storing the plurality of probing potential-modulated current signals in the memory; calculating a plurality of ratio parameters from the plurality of probing potential-modulated current signals; calculating an initial glucose concentration by applying one of the plurality of ratio parameters to a conversion function; determining a connection function value based on the primary current signal and the plurality of ratio parameters; and calculating a final glucose concentration from the initial glucose concentration and the connection function value.

11. A CGM device as described in claim 10, wherein the probing potential modulation sequence includes a first voltage potential greater than the constant voltage potential, a second voltage potential less than the constant voltage potential, a third voltage potential less than the second voltage potential, and a fourth voltage potential greater than the third voltage potential.

12. A CGM device as described in claim 11, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of a probing potential modulated current signal measured during the first voltage potential.

13. The CGM device of claim 11, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of a probing potential modulated current signal measured during the fourth voltage potential.

14. A CGM device as described in claim 10, wherein the probing potential modulation sequence includes a first voltage potential greater than the constant voltage potential, a second voltage potential less than the constant voltage potential, a third voltage potential less than the second voltage potential, a fourth voltage potential greater than the third voltage potential, and a fifth voltage potential greater than the fourth voltage potential.

15. A CGM device as described in claim 14, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of probing potential modulated current signals measured during the fourth voltage potential and the fifth voltage potential.

16. The CGM device of claim 10 , wherein the primary current signal and the probing potential-modulated current signal are working electrode current signals.

17. The CGM device of claim 10, wherein the primary current signal is measured every 3 minutes and every 15 minutes.

18. The CGM device of claim 10 , wherein the ratio of the measured probing potential-modulated current signal is independent of electrode size.

19. A current sensing circuit coupled to the sensor and configured to measure the current signal generated by the sensor. The CGM device of claim 10 , further comprising: a sampling circuit coupled to the current sensing circuit and configured to generate a digitized current signal from the measured current signal.

20. A CGM device as described in claim 10, wherein the transmitter circuit is configured to transmit glucose values ​​to a portable user device for presentation to a user of the CGM device.

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