Biosensor with membrane structure for determining analyte concentration under steady-state and non-steady-state conditions

Abstract

The biosensor system is configured to establish a steady-state condition and alternate between the steady-state condition and a non-steady-state condition to determine an analyte concentration. The biosensor system includes an electrode system having at least one working electrode and one counter electrode. The working electrode is coated with an analyte catalyst layer for converting the analyte to a measurable species. A membrane system surrounds the electrode system and includes an analyte-permeable membrane. The membrane has an analyte permeability with an analyte solubility lower than the analyte solubility outside the membrane. The membrane is configured to capture the measurable species within the membrane such that a steady state of the measurable species generated from the analyte is established near the electrode surface. The bias circuit is configured to apply a potential modulation sequence to the working electrode to induce alternating steady-state and non-steady-state conditions within the electrode system for determining the analyte concentration.

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] The present invention relates generally to continuous sensor monitoring of analytes in bodily fluids (also referred to as continuous analyte monitoring or continuous analyte sensing), and more specifically to continuous glucose monitoring (also referred to as continuous glucose monitoring or 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 biosensor system is configured to establish a steady-state condition and alternate between steady-state and non-steady-state conditions to determine an analyte concentration. The biosensor system includes an electrode system having at least one working electrode and one counter electrode, the working electrode coated with an analyte catalyst layer for converting an analyte to a measurable species at and near the working electrode. The biosensor system also includes a membrane system surrounding the electrode system and including an analyte-permeable membrane. The analyte-permeable membrane has an analyte permeability with an analyte solubility lower than the analyte solubility outside the membrane. The membrane is configured to capture the measurable species within the membrane such that a steady state of the measurable species resulting from the analyte is established near the electrode surface. The biosensor system further includes a bias circuit configured to apply a potential (also referred to as voltage or voltage potential) modulation sequence to the working electrode to induce alternating steady-state and non-steady-state conditions within the electrode system for determining an analyte concentration. The biosensor system further includes a processor and a memory coupled to the processor. The memory includes computer program code stored in the memory, which, when executed by the processor, causes the processor to (a) measure and store a primary current signal (also referred to as a current or a signal) using the working electrodes and the memory; (b) measure and store a plurality of probing voltage-modulated current signals associated with the primary current signal; (c) determine an initial glucose concentration based on the transfer function and the measured current signals; (d) determine a connection function value based on the primary current signal and the plurality of probing voltage-modulated current signals; and (e) determine a final glucose concentration based on the initial glucose concentration and the connection function value.

[0006] In some embodiments, a method for determining a glucose level during a continuous glucose monitoring (CGM) measurement includes providing a CGM device. The CGM device includes a sensor, a memory, and a processor. The sensor includes an electrode system and a membrane system surrounding the electrode system, the membrane system including an analyte-permeable membrane having an analyte permeability with an analyte solubility lower than the analyte solubility outside the membrane. The method also includes 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-modulated current signal resulting from the probing potential modulation sequence and storing the measured probing potential-modulated current signal in memory, determining an initial glucose concentration based on a transfer function and a ratio of the measured probing potential-modulated current signal, determining a connection function value based on the primary current signal and the plurality of probing potential-modulated current signals, and determining 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.

[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. [Brief explanation of the drawings]

[0009] [Figure 1A] 1 illustrates 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] 1 illustrates a graph of steady state conditions associated with an electrode and its nearby boundary environment, in accordance with one or more embodiments of the present disclosure. [Figure 1C] 1 illustrates a graph of an example of a probing potential modulation (PPM) sequence, in accordance with one or more embodiments of the present disclosure. [Figure 1D] 10 illustrates a graph of the non-steady-state conditions associated with an electrode and its nearby boundary environment during the E2 and E3 potential steps, in accordance with one or more embodiments of the present disclosure. [Figure 1E] 1D illustrates the IV curve and graphs of the individual potential steps of the PPM sequence of FIG. 1C implemented in accordance with one or more embodiments of the present disclosure. [Figure 1F] 1D illustrates a graph of a typical output current from the PPM sequence shown in FIG. 1C with labeling of the current at each potential step, in accordance with one or more embodiments of the present disclosure. [Figure 2A] 1C illustrates graphs of the initial and ending currents for each potential step from a sensor in an in vitro linearity test, in particular, graphs of the initial and ending currents for potential step 1 of FIG. 1C, in accordance with one or more embodiments of the present disclosure. [Figure 2B] 1C illustrates graphs of the initial and ending currents for each potential step from a sensor in an in vitro linearity test, in particular, graphs of the initial and ending currents for potential step 2 of FIG. 1C, in accordance with one or more embodiments of the present disclosure. [Figure 2C] 1C illustrates graphs of the initial and ending currents for each potential step from a sensor in an in vitro linearity test, in particular, graphs of the initial and ending currents for potential step 3 of FIG. 1C, in accordance with one or more embodiments of the present disclosure. [Figure 2D]1C illustrates graphs of the initial and ending currents for each potential step from a sensor in an in vitro linearity test, in particular, graphs of the initial and ending currents for potential step 4 of FIG. 1C, in accordance with one or more embodiments of the present disclosure. [Figure 2E] 1C illustrates graphs of the initial and ending currents for each potential step from a sensor in an in vitro linearity test, in particular, graphs of the initial and ending currents for potential step 5 of FIG. 1C, in accordance with one or more embodiments of the present disclosure. [Figure 2F] 1C illustrates graphs of the initial and ending currents for each potential step from a sensor in an in vitro linearity test, in particular, graphs of the initial and ending currents for potential step 6 of FIG. 1C, in accordance with one or more embodiments of the present disclosure. [Figure 3A] 10 illustrates a graph comparing damping constants K1 and K4, in accordance with one or more embodiments of the present disclosure. [Figure 3B] 10 illustrates a graph comparing ratio constants R1 and R4, in accordance with one or more embodiments of the present disclosure. [Figure 3C] 10 illustrates a graph of the correlation between ratio constants R1 and R4 and damping constants K1 and K4, in accordance with one or more embodiments of the present disclosure. [Figure 3D] 10 illustrates a graph comparing ratio constants R5 and y45, in accordance with one or more embodiments of the present disclosure. [Figure 3E] 10 illustrates a graph comparing the ratio constant R2 in accordance with one or more embodiments of the present disclosure. [Figure 3F] 10 illustrates a graph of the ratio constant 1 / R6, in accordance with one or more embodiments of the present disclosure. [Figure 4A] 1 illustrates a high-level block diagram of an exemplary CGM device, in accordance with one or more embodiments of the present disclosure. [Figure 4B] 1 illustrates a high-level block diagram of another exemplary CGM device, in accordance with one or more embodiments of the present disclosure. [Figure 5] FIG. 1 is a schematic side view of an exemplary glucose sensor in accordance with one or more embodiments of the present disclosure. [Figure 6]1 illustrates a table summarizing G raw and G complexes from i10, R4, y45, and R1 along with an in vitro dataset, according to one or more embodiments of the present disclosure. [Figure 7] 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

[0010] Embodiments described herein include systems and methods for applying probing potential modulation (PPM) on top of a 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 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 is sometimes referred to as a PP or PPM method, while performing continuous analyte sensing without PPM is sometimes referred to as a NP or NPPM method.

[0011] 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 may be taken and subsequent PPM may 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 may be approximately 0.55 volts. Other voltage potentials may be used.

[0012] FIG. 1A shows 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 to 15 minutes, during continuous glucose monitoring and are used to calculate a user's glucose level. Primary data points can be, for example, the working electrode current measured for an analyte sensor during continuous analyte monitoring. Rather than showing the primary data points, FIG. 1A shows the time and voltage at which each primary data point is measured. For example, circle 102 in FIG. 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 E0. Similarly, circle 104 in FIG. 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 E0.

[0013] PPM current refers to the measurement of the current signal generated in response to PPM applied to the sensor during continuous analyte detection. PPM is described in more detail below in connection with FIG. 1C.

[0014] 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., the primary currents and PPM currents measured to determine predictive equations that are then stored in a continuous analyte monitoring (CAM) device and used during continuous analyte sensing to determine analyte concentration).

[0015] 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.

[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. The 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 a 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 sensor / electrode status and / or condition.

[0019] PPM can include one or more steps at a different potential than the constant voltage potential typically used during continuous analyte monitoring. For example, 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. 1C.

[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 monitoring with sensors, 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, the 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, since 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 a constant voltage applied to an analyte sensor. Methods are provided for developing parameters for predictive equations that can be used to continuously and accurately determine analyte concentration from an analyte sensor. Additionally, methods and systems are provided for determining analyte concentration using a probing potential modulation (PPM) self-contained signal. Such methods and systems may enable the determination of analyte concentration 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) compensating 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-7.

[0023] Disclosed herein are sensor boundary conditions related to non-steady state (NSS) conditions during a PPM cycle for determining analyte concentration in continuous analyte monitoring operations. Sensor membrane structure and boundary conditions play a unique role in establishing steady state (SS) conditions, NSS conditions, and alternation of SS and NSS conditions for determining analyte concentration. Below, boundary conditions for establishing SS and NSS and for determining analyte concentration are described.

[0024] Steady-state conditions: Conventional biosensors used for continuous analyte sensing operate under steady-state conditions, where the continuous monitoring sensor has a constant potential applied to the working electrode (WE) that stabilizes after a settling time. Under these conditions, current is drawn from a constant flow of incoming analyte molecules in a steady-state diffusion condition created by the outer membrane. This condition is illustrated in Figure 1B.

[0025] The typical dry thickness of the outer membrane is about 5-15 μm, more likely about 8-12 μm. When the sensor is immersed in a liquid sample or inserted subcutaneously into the skin, the membrane structure rehydrates and expands to a stable thickness of about 30-60 μm, and more likely about 40-50 μm. During rehydration, the sensor response will change over time. The typical dry thickness of the enzyme layer is about 1-3 μm, more likely below 2 μm. Upon rehydration, the enzyme layer does not expand significantly due to cross-linking by the binder, firmly securing the structure in place. For effective sensor operation, the ratio of enzyme layer to outer membrane layer may be about 1:10 upon rehydration of the stabilized membrane. Other membrane and / or enzyme layer thicknesses may be used.

[0026] The boundary structure, such as that defined by the enzyme layer and the outer membrane, theoretically creates a boundary environment that allows the line C 媒介物質 In the absence of a change in analyte concentration, the current is proportional to the concentration gradient of the measurable species at the electrode surface, which in turn depends on the analyte concentration gradient as defined by the boundary conditions.

[0027] Boundary environment: The boundary conditions in FIG. 1B can be theoretically interpreted as follows: analyte concentration C 外部 is the film concentration C at the outer interface of the film 膜 The concentration in the membrane, C, is at some value that is in equilibrium with 膜 The lower 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, such as H2O2 via oxygen as a mediator for glucose oxidase. Once generated, the measurable species will diffuse toward the electrode and toward the membrane.

[0028] Under a constant applied voltage that completely oxidizes the measurable species, a constant flux of the measurable species will be drawn towards the electrode. Soon, a current will flow along the concentration gradient (dC) of the measurable species at the electrode surface. 媒介物質 Under diffusion-limited conditions (meaning that the rate of oxidation / consumption of the measurable species is greatest and limited only by the diffusion of the measurable species), a steady state is established where the concentration gradient C 媒介物質 is predicted 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 (analyte flux entering the enzyme, analyte consumption and conversion by the enzyme, and diffusion of the measurable species). 媒介物質 is slowly determined by diffusion. Because the measurable species has a slower diffusion rate within the membrane than outside it, the measurable species is preferably trapped within the membrane starting at the enzyme layer. This steady-state condition is dynamically changing as the external analyte concentration changes. Under operating conditions governed by PPM cycles, primary data points are sampled and recorded under essentially steady-state conditions, as the boundary environment resumes steady-state conditions after the non-steady-state potential modulation cycle.

[0029] Potential modulation and non-steady-state conditions: If the applied potential is modulated away from a constant voltage, such as the 0.55 V to 0.6 V potential step (step 1 in Figure 1C and E0 to E1 in Figure 1E), but still within the mediator oxidation plateau (diffusion-limited region in the V axis), there will be some finite current generated with little decay. This is expressed as exp(E app -E 0’) is an induced current process resulting from an asymmetric plateau governed by E app 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 the mediator is illustrated schematically in Figure 1F. Examples of such output currents are shown and labeled in Figure 1F as i11, i12, and i13, with i10 being 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, from E1 to E2 in FIG. 1E to E3 (steps 2 and 3 in FIG. 1C), 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 production 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 the otherwise linear state that reaches zero at the electrode surface. This state is referred to as the non-steady state and is illustrated in FIG. 1D, 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 FIG. 1F for steps 2 and 3 in FIG. 1C. The negative currents suggest partial reduction due to the potential step from high to low. The disturbance of the steady-state conditions is due to the internal and external boundary environments (C 膜 and C 外部 ) remains unchanged and the process is short, it occurs only near the electrode surface.

[0031] Alternating NSS and SS Conditions: When the potential is reversed again in step 4 from E3 to E2, as shown in Figures 1C and 1E, some of the accumulated measurable species is consumed, with oxidation occurring at a higher rate than 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 in Figure 1C, from E2 to E1 (Figure 1E), further completes the non-steady-state oxidation of the excess species, restoring the sensor to an operating potential above the plateau region. Step 6 in Figure 1C undergoes a negative plateau degeneration step, returning the sensor to the original potential, which leads to resuming steady-state conditions before the next potential modulation cycle. Such conditions are shown in Figure 1B. Thus, as the PPM cycle is repeated, steady-state and non-steady-state conditions alternate, resulting in a signal for analyte concentration determination.

[0032] The PPM method described above provides primary data (e.g., the primary current obtained during SS) as an indication of analyte concentration, while the associated PPM current and PPM parameters are parameters that provide information regarding sensor and electrode condition compensation. The exemplary PPM sequences and output current profiles all have a potential step from high to low before reversing back to high, thus alternating between steady-state and non-steady-state conditions.

[0033] An important aspect of the boundary environment and alternation of steady-state and unsteady-state conditions to achieve unsteady state is that the membrane 外部 =C 膜 / C 外部 <1 relationship and further D 外部 A diffusion coefficient D lower than 膜 In addition, the permeability of the membrane to the analyte, P 膜 =D 膜 *C 膜 defines the throughput of the analyte. This relationship indicates that the analyte solubility is lower than the solubility outside the membrane. In some embodiments, K 外部may be about 0.1 to 0.9, in some embodiments about 0.2 to 0.7, and in some embodiments about 0.2 to 0.4.

[0034] The multilayer structure of the enzyme and membrane is such that the outer membrane interface is K 外部 If the diffusion coefficient is fixed by <1, it provides a complex mass transport process with diffusion across two different media. For the incident analyte, diffusion through the outer membrane is the dominant process, and its concentration quickly decays to zero through the enzyme layer. For the enzyme reaction product, or measurable species, mass transport through the enzyme layer will be transient due to the very thin enzyme layer. The commonly known definition (Dt) 1 / 2 Taking the diffusion layer thickness by , the diffusion species is 5 × 10 for measurable species. -7 cm 2 It would take only 0.18 seconds for a diffusing species to cross a 3 μm enzyme layer with a diffusion coefficient of 1 / s. In contrast, it would take 18 seconds for a diffusing species to cross a 30 μm membrane thickness with the same diffusion coefficient. This means that the diffusion process within the enzyme layer is virtually negligible compared to the process within the membrane layer during the potential modulation cycle. The membrane acts as a trap for the measurable species, preventing their diffusion outward (under conditions of slow diffusion starting from the enzyme layer). The alternating cycles of steady and unsteady states, particularly in potential step 4 (Figure 1C), trap almost all of the excess measurable species accumulated during reverse potential steps 2 and 3 (Figure 1C). As long as a sufficient amount of mediator is present, the enzyme reaction proceeds at an equilibrium constant K 外部 <1, maintaining a constant rate with a constant supply of incident analyte governed by .times. ...

[0035] 2A-2F present exemplary output current signals of initial and ending currents from five different sensors in line plots resulting from potential steps 1-6 in FIG. 1C, respectively, according to embodiments provided herein. The following observations are made. First, the initial transient decay of the current is smallest for potential steps with potential reversals (see FIGS. 2B, 2D, and 2F). This includes potential steps 2, 4, and 6 in FIG. 1C. Second, the response current is clearly defined in steps versus analyte concentration, even for potential steps 2, 4, and 6. Third, in contrast, potential steps extending from a steady state or positively to higher potentials give strong initial decays for individual sensors that last for at least 1 hour in in vitro studies. This includes potential steps 1 and 5 in FIG. 1C (see the rounded areas in FIGS. 2A and 2E, which are the beginning of each sensor in the line plots). Fourth, as shown in Figure 2C, the current in a potential step extending to a negative potential (step 3 in Figure 1C) does not have a clearly defined step corresponding to the analyte concentration. Fifth, the difference in current between the initial and final currents of a potential step is small for plateau degeneration conditions, such as potential steps 1 and 6 in Figure 1C (see Figures 2A and 2F). Sixth, the difference in current between the initial and final currents of a potential step is relatively large for potential steps where the potential direction switches (see Figures 2B and 2D). This includes potential steps 2 and 4 in Figure 1C.

[0036] From the data presentation and observations above, it can be seen that potential step 4 in Figure 1C (see also Figure 2D) leads to several desirable features, such as minimal initial decay, a well-defined response to analyte concentration, and a large separation between the initial and ending currents within one potential step. This is not coincidental, given the description and analysis of the data from non-steady-state conditions.

[0037] To capture the current change, or current decay, at the potential step, a decay constant is defined to describe the decay process. In this regard, two decay constants are devised below. One is expressed as a ln (natural logarithm) function, and the other is expressed as a simple current ratio. In the ln function formula, the decay constant is defined as K = (ln(i2) - ln(i1)) / (ln(t2) - ln(t1)). In this formula, if there is no decay, K = 0. Furthermore, if the decay constant value is close to 0, the decay is small / shallow, whereas if the decay constant is far from 0, the decay is relatively large / high. In the current ratio formula, the constant is defined as R = i _t2 / / i _t1 In the definitions of both K and R, t2 > t1 indicates that t2 is later than t1. For current recording formats with three PPM currents per potential step, there are two constants associated with one step. For example, for potential step 1, these are the constants defined by i13 / i12 and i13 / i11. For the embodiment described in the next section, the decay constants are defined as R1 = i13 / i11, R2 = i23 / i21, R3 = i33 / i31, R4 = i43 / i41, R5 = i53 / i51, and R6 = i63 / i61, i.e., ratio = last current / first current. In the R formula, if there is no decay, R = 1. In a small decay process, the R value approaches 1, whereas in a large decay process, the R value moves away from 1.

[0038] 3A-3F present line plots converted from the currents of FIGS. 2A-2F, respectively, to illustrate the concept of decay constants and their relationship to steady-state and unsteady-state conditions, according to embodiments provided herein. FIG. 3A shows the PPM currents for potential steps 1 and 4 of FIG. 1C, from which K=(ln(i t=6秒 )-ln(i t=2秒)) / (ln(6 sec)-ln(2 sec)), where the same data set was obtained from different linearity tests of different sensors. By comparing the K constants from different steps, the relative magnitudes of the decay constants, whether shallow or steep, reflect the nature of the decay and, therefore, the nature of the electrochemical process. For example, K = (ln(i13)-ln(i11)) / (ln(6)-ln(2)). In the process at potential step 4, the K value is derived from a non-steady-state condition as a result of oxidizing excess measurable species accumulated during reverse potential steps 2 and 3. The decay constant value is substantially away from the decay state or is zero otherwise.

[0039] Figure 3B shows the PPM currents from potential steps 1 and 4 in Figure 1C, with the ratio = i t=6秒 / i t=2秒 The comparison of the ratio constants R1 and R4, which are the counterparts of K1 and K4, is performed by (1) = (i13 / i11). For example, R1 = i13 / i11. By comparing the R constants from different steps, the relative magnitudes of the decay constants, whether shallow or steep, reflect the nature of the decay and, therefore, the nature of the electrochemical process. The R1 value from potential step 1 is close to 1, which is the no-decay condition, whereas the R4 value from potential step 4 is substantially away from 1 (decay condition). These seemingly implicit constants, which reflect sensor information adjacent to each primary data point, are fed into a multivariate regression (described below) in which the most representative parameters are selected for the compensation equation.

[0040] Figure 3C is the correlation between the R1, R4 ratios and the K1, K4 values ​​from Figures 3A and 3B. Overall, the correlation curves reflect different mathematical expressions of the decay process. The R1 and K1 constants are close to their limit of no decay, while the R4 and K4 constants are farther from these limits. R1 = 0.3731 * K1 for the top of the curve. 2The curve-fitting equation of +1.0112*K1+0.9894 indicates that as the K1 value approaches zero, the R1 ratio approaches 1 (intercept of 0.9894). This is the no-decay condition, and is not coincidental. Both the R and K constants indicate that the decay from Step 1, or plateau degeneration process, is shallow with only finite electrochemical reaction.

[0041] A comparison of Figures 3A, 3B, and 3C reveals that potential step 4 is more substantially involved in the electrochemical reaction than potential step 1. In terms of response, both the current signal and the ratio constant from potential step 4 are more responsive to analyte concentration than those from potential step 1. Furthermore, the signal / ratio from step 4 provides a much shorter initial warm-up time than those from step 1. The fact that the decay constants provide a graded response to analyte concentration arises from the fact that their underlying current signal responds to analyte concentration. However, the extracted parameters provide a different dimension of sensor response information, such as the decay of the electrochemical process.

[0042] FIG. 3D shows the ratio values ​​of R5 (= i53 / i51) and the ratio values ​​of the inter-step y45 (= i43 / i51) according to embodiments provided herein. By comparison, the range of R5 lies between the R1 and R4 values. This indicates that the decay process is steeper than that of potential step 1, but less steep than that of potential step 4. While the ratio y45 is not a decay constant in the sense defined above, it still exhibits similar behavior to R4, providing a very strong response to analyte concentration. Finally, parameter y45 provides a relative measure of the process over two potential steps back to the redox plateau. In addition to providing a graded response to glucose, this parameter has the lowest intercept, or background, value of all the positive response parameters.

[0043] Figure 3E shows the R2 (= i23 / i21) and R3 (= i33 / i31) constants. Potential step 2 in Figure 1C induces a negative current reversal due to a negative potential switch. The resulting negative current switch is due, in part, to setting the redox state to less than complete oxidation / partial reduction of the measurable species at potential E2. However, the current decay is still positive, meaning that the current later in the potential step is lower than the previous current (absolute value) in either the positive or negative region. The R2 ratio provides a strong, but not linear, response to analyte concentration. Potential step 3 in Figure 1C further reduces the voltage to different redox ratios, in which case the overall R3 ratio is not clearly defined in response to analyte concentration, even though they still provide a positive response.

[0044] According to the literature, hydrogen peroxide H2O2 has a concentration of about 1.5 x 10 -5 ~2×10 -5 cm 2 s -1 HO diffuses into water with an effectiveness factor of 0.01, and as the amount of water in the membrane decreases, the effect on the diffusivity becomes more pronounced. Thus, the limiting factor for diffusion becomes water, whereas at low levels of water in the membrane, the limiting factor becomes the polymer chains. This property of HO traps the membrane before it can diffuse out, rather than remaining nearby as an excess of a measurable species.

[0045] Various commercially available polymers have been tested for hydrogen peroxide permeability. The effective diffusion coefficient was obtained from the concentration evolution of H2O2 in two compartments separated by a polymer membrane. The measured value was 5.12 x 10 for polyurethane. -9 ±8.50×10 -10 ~2.25×10 -6 ±1.00×10 -7 , 1.50 × 10 for perfluorinated ion exchange membranes such as Nafion® 117 (available from The Chemours Company, Wilmington, Delaware). -6 ±7.00×10 -8, and 5.76 × 10 for polymethyl methacrylate (PMMA). -7 ±4.60×10 -8 The range was.

[0046] 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 to resume for steady-state conditions 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.

[0047] 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.

[0048] In some embodiments, the probing potential modulation 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. 1E (steps 2 and 3 in FIG. 1C) 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.

[0049] Different embodiments can be used to accommodate non-steady-state conditions. For example, non-steady-state conditions can also be probed by one step going directly to the target potential E2 and returning to the 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 the 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.

[0050] 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 making the connection from in vitro glucose to in vivo glucose by applying a unified "transformation function" to a wide range of data for the sensor response, followed by a "connection function" to reduce the glucose error to a narrow band. The unified conversion function is 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.

[0051] 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, R1, R4, y45, or another PPM current signal or parameter), 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 can then be used to calculate the initial or raw glucose G 生 can be used to calculate as follows: G 生 = signal / gradient 複合

[0052] PPM current signal parameters such as R1, R4, and y45 may be less sensitive to interference effects and exhibit lower warm-up sensitivity. For this reason, in some embodiments provided herein, a unified composite slope may be determined from PPM current signal parameters such as R1, R4, and y45, or another suitable PPM current signal parameter. 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, for glucose G 基準 Polynomial fitting of R1, R4, and y45 to reference y45 can serve as a connection function to determine the initial or raw glucose value from R1, R4, or y45. The following are exemplary calculations for R1, R4, and y45: For R1: G 生 =4351.9*(R1) 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

[0053] Other relationships may be used. Note that the equivalent form of Iw-Ib of the primary data (i10) may be used. However, background subtraction is not used because R1, R4, and y45 are relatively immune to interference effects from other interfering species. In some embodiments, multiple conversion functions may be used.

[0054] 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 so much, the connection function is referred to as a calibration-free in vitro to in vivo connection function, meaning that all sensor responses are accommodated within a narrow error band.

[0055] 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 sensors within their sensitivity ranges as long as the sensors respond to glucose. These responses can be linear or nonlinear.

[0056] 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 calculate the relative error ΔG / G 生 as the target of the input parameters from the multivariate regression and the PPM parameters.

[0057] In summary, in some embodiments, the R1, R4, or y45PPM parameters convert the raw current signal information into a raw or initial glucose value G 生 It can be used as part of a transformation function to convert G 生 Knowing , the connection function can then be calculated to find the compensated or final glucose signal or concentration, G 複合 For example, the connection function can be used to calculate the relative error ΔG / G using the SS signal (i10) and the NSS signal (PPM signal) as input parameters. 生 can be derived from in vitro data using as the target for multivariate regression. An exemplary connection function C is provided below for parameter R4. It will be understood that other numbers and / or types of terms can be used. 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*Gdll-0.010296*Gd21+0.003426*Gd32- 6.350168*d21d31+8.39652*d22d31-0.0329025*Gdlld31-0.039527*avl- 2.342127*avlil0+0.550159*av3i10-4.87669*avl4-0.139865*avl6+14. 59835*av25-9.31e-5*Gav3-0.000143*Gav4+0.001157*Gavl6-0.022394* Gav25-0.000888*Gav26-0.928135*R30+2.307865*R50-4.501269*z60-7.491846*w65w51-3.56458*w65w53+7.147535*w43w32…. .

[0058] The input parameters of the connection function CF can be, for example, of the following type:

[0059] Probing Currents: The probing potential modulation currents are 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).

[0060] R parameters: These ratios are calculated by dividing the ending PPM current by the first PPM current within one potential step. For example, R1 = i13 / i11, R2 = i23 / i21, R3 = i33 / i31, R4 = i43 / i41, R5 = i53 / i51, and R6 = i63 / i61.

[0061] X-type parameters: The general format of this type of parameter is given by the ending PPM current of the subsequent 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 of the three recorded currents per step, and i13 is the ending PPM current for step 1. 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.

[0062] 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 starting 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 first PPM current of step 1, out of 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.

[0063] Z-type parameters: The general format of this type of parameter is given by the initial PPM current of the subsequent 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 out of 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.

[0064] 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 are: 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...

[0065] 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.

[0066] Therefore, the extracted parameters R1, R4, and y45 can be used to indicate the raw glucose analyte concentration, and a connection function can be used with the raw glucose analyte concentration to connect in vitro to in vivo glucose. 生 Transformation functions for G 複合 The results of the connection function compensation for y45 are summarized in Table 600 of Figure 6. The results show that R1, R4, and y45 can be used as the analytes to represent the signals, and the connection function can converge the wide diffusion response to a narrow range of glucose values.

[0067] 4A illustrates a high-level block diagram of an exemplary CGM device 400 according to embodiments provided herein. While not shown in FIG. 4A , 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 400 includes a bias circuit 402 that can be configured to couple to a CGM sensor 404. The bias circuit 402 can be configured to apply a bias voltage, such as a continuous DC bias, to the analyte-containing fluid through the CGM sensor 404. 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 405 (e.g., working electrode, background electrode, etc.) of the CGM sensor 404.

[0068] 1C, or another PPM sequence to the CGM sensor 404. 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.

[0069] In some embodiments, the CGM sensor 404 may include two electrodes, and a bias voltage and probing potential modulation (PPM) may be applied between the pair of electrodes. In such cases, current may be measured through the CGM sensor 404. In other embodiments, the CGM sensor 404 may include three electrodes, such as a working electrode, a counter electrode, and a reference electrode. In such cases, a bias voltage and PPM may be applied between the working electrode and the reference electrode, and current may be measured, for example, through the working electrode. The CGM sensor 404 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 404. Exemplary chemicals include glucose oxidase, glucose dehydrogenase, or the like. In some embodiments, a mediator such as ferricyanide or ferrocene may be used.

[0070] The continuous bias voltage generated and / or applied by bias circuit 402 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.

[0071] The PPM current and the non-PPM (NPPM) current through the CGM sensor 404 in the analyte-containing fluid in response to the PPM and constant bias voltage are measured as current measurements (I 測定 ) circuit 406 (also referred to as a current sensing circuit). Current measurement circuit 406 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 CGM sensor 404. In some embodiments, current measurement circuit 406 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 CGM sensor 404 passes. The voltage developed across the resistor in current measurement circuit 406 represents the magnitude of the current and may be referred to as a current measurement signal.

[0072] In some embodiments, the sample circuit 408 may be coupled to the current measurement circuit 406 and configured to sample the current measurement signal. The sample circuit 408 may generate digitized time-domain sample data representing the current measurement signal (e.g., a digitized glucose signal). For example, the sample circuit 408 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 408 may be 16 bits, although more or fewer bits may be used in other embodiments. In some embodiments, the sample circuit 408 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 of 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.

[0073] 4A , the processor 410 may be coupled to the sample circuit 408 and may be coupled to the memory 412. In some embodiments, the processor 410 and the sample circuit 408 are configured to communicate directly with each other via a wired path (e.g., via a serial or parallel connection). In other embodiments, the processor 410 and the sample circuit 408 may be coupled by the memory 412. In this arrangement, the sample circuit 408 writes digital data to the memory 412, and the processor 410 reads digital data from the memory 412.

[0074] The memory 412 may have stored therein one or more predictive formulas 414 for use in determining glucose values ​​based on the primary data points (NPPM current) and the PPM currents (from the current measurement circuit 406 and / or the sample circuit 408). In some cases, the predictive formulas may include conversion functions and / or connection functions. For example, in some embodiments, two or more predictive formulas may be stored in the memory 412, each for use with a different segment (time period) of CGM collected data. In some embodiments, the memory 412 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 during primary current signal measurement.

[0075] The memory 412 may also store a plurality of instructions therein. In various embodiments, the processor 410 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.

[0076] In some embodiments, the instructions stored in memory 412 may include instructions that, when executed by processor 410, cause processor 410 to (a) cause CGM device 400 to measure current signals (e.g., primary current signals and PPM current signals) from interstitial fluid (via bias circuit 402, CGM sensor 404, current measurement circuit 406, and / or sample circuit 408); (b) store the current signals in memory 412; (c) calculate prediction formula parameters, such as ratios (and / or other relationships) of currents from different voltage steps in a PPM sequence or other voltage changes; (d) employ the calculated prediction formula parameters to calculate a glucose value (e.g., concentration) using a prediction formula; and / or (e) communicate the glucose value to a user.

[0077] The memory 412 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 412 may be packaged, for example, as a single chip or as multiple chips. In some embodiments, the memory 412 may be embedded with one or more other circuits in an integrated circuit, such as, for example, an application-specific integrated circuit (ASIC).

[0078] As mentioned above, memory 412 may have a plurality of instructions stored therein that, when executed by processor 410, cause processor 410 to perform various operations specified by one or more of the stored instructions. Memory 412 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 410 in response to execution of one or more of the instructions.

[0079] 4A , the bias circuit 402, CGM sensor 404, current measurement circuit 406, sample circuit 408, processor 410, and memory 412 including predictive calculation formula 414 may be disposed within a wearable sensor portion 416 of CGM device 400. In some embodiments, wearable sensor portion 416 may include a display 417 for displaying information such as glucose concentration information (e.g., without the use of external equipment). Display 417 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.

[0080] 4A , the CGM device 400 may further include a portable user device portion 418. A processor 420 and a display 422 may be disposed within the portable user device portion 418. The display 422 may be coupled to the processor 420. The processor 420 may control the text or images shown by the display 422. The wearable sensor portion 416 and the portable user device portion 418 may be communicatively coupled. In some embodiments, the communicative coupling of the wearable sensor portion 416 and the portable user device portion 418 may be by wireless communication via transmitter and / or receiver circuitry, such as, for example, a transmit / receive (Tx / Rx) circuit 424a of the wearable sensor portion 416 and a transmit / receive (Tx / Rx) circuit 424b of the portable user device portion 418. 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 416 and the portable user device portion 418 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 416 and the portable user device portion 418 may be connected by one or more wires.

[0081] Display 422 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.

[0082] 4B, an exemplary CGM device 450 is shown that is similar to the embodiment illustrated in FIG. 4A but has a different division of components. In CGM device 450, wearable sensor portion 416 includes bias circuitry 402 coupled to CGM sensor 404 and current measurement circuitry 406 coupled to CGM sensor 404. Portable user device portion 418 of CGM device 450 includes sample circuitry 408 coupled to processor 420 and display 422 coupled to processor 420. Processor 420 is further coupled to memory 412, which may have predictive formulas 414 stored therein. In some embodiments, processor 420 in CGM device 450 may also perform the functions previously described, for example, performed by processor 410 of CGM device 400 of FIG. 4A. The wearable sensor portion 416 of the CGM device 450 may be smaller and lighter, and therefore less invasive, than the CGM device 400 of FIG. 4A because it does not include the sample circuit 408, processor 410, memory 412, etc. Other component configurations may be used. For example, as a variation on the CGM device 450 of FIG. 4B, the sample circuit 408 may still remain on the wearable sensor portion 416 (such that the portable user device portion 418 receives the digitized glucose signal from the wearable sensor portion 416).

[0083] FIG. 5 is a side schematic view of an exemplary glucose sensor 404 according to embodiments provided herein. In some embodiments, the glucose sensor 404 may include a working electrode 502, a reference electrode 504, a counter electrode 506, and a background electrode 508. 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 404). 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 502 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.

[0084] In some embodiments, the reference electrode 504 may be formed from Ag / AgCl. The counter electrode 506 and / or background electrode 508 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 508 may be identical to the working electrode 502, but does not include the chemical catalyst and / or mediator. The counter electrode 506 may be separated from the other electrodes by a separation layer 510 (e.g., polyimide or another suitable material).

[0085] 7 illustrates an exemplary method 700 for determining a glucose value during a continuous glucose monitoring measurement according to embodiments provided herein. Method 700 includes, at block 702, providing a CGM device (e.g., CGM device 400 or 450 of FIGS. 4A and 4B ) including a sensor, a memory, and a processor, the sensor comprising an electrode system and a membrane system surrounding the electrode system, the membrane system comprising an analyte-permeable membrane having an analyte solubility lower than the analyte solubility outside the membrane.

[0086] The method 700 also includes applying a constant voltage potential to the sensor at block 704 (e.g., E0 of FIG. 1A). At block 706, the method 700 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in memory. At block 708, the method 700 includes applying a probing potential modulation sequence to the sensor (e.g., the PPM sequence of FIG. 1C). At block 710, the method 700 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. The method 700 further includes determining an initial glucose concentration based on the transfer function and the plurality of measured probing potential modulation current signals at block 712, determining a connection function value based on the primary current signal and the plurality of probing potential modulation current signals at block 714, and determining a final glucose concentration based on the initial glucose concentration and the connection function value at block 716. The final glucose concentration may be communicated to the user (eg, via display 417 or 422 of FIG. 4A or 4B).

[0087] 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 may be used to program a computer system, controller, or other electronic device according to one or more embodiments.

[0088] 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 biosensor system configured to establish a steady-state condition and alternate between said steady-state condition and a non-steady-state condition to determine an analyte concentration, comprising: an electrode system having at least one working electrode and one counter electrode, the at least one working electrode being coated with an analyte catalyst layer for converting an analyte to a measurable species at and near the at least one working electrode; a membrane system surrounding the electrode system and comprising an analyte-permeable membrane, the analyte permeable membrane has an analyte permeability with an analyte solubility that is lower than the analyte solubility outside the analyte permeable membrane; a membrane system, the analyte permeable membrane configured to trap the measurable species within the analyte permeable membrane to establish a steady state within the electrode system; a bias circuit configured to apply a probing potential modulation sequence to the at least one working electrode to establish a non-steady state in the electrode system, a bias circuit in which applying the probing potential modulation sequence causes alternating steady-state and non-steady-state conditions; a processor; a memory coupled to the processor; The memory includes computer program code stored in the memory, the computer program code, when executed by the processor, causing the processor to: applying a constant voltage potential to the electrode system during the steady state condition to generate a primary current signal; applying the constant voltage potential completely oxidizes the measurable species; and measuring and storing the primary current signal using the at least one working electrode and the memory; applying the probing potential modulation sequence to the electrode system to establish a non-steady state within the electrode system, resulting in a plurality of probing potential modulation current signals; applying the probing potential modulation sequence attenuates oxidation of the measurable species; measuring and storing the plurality of probing potential-modulated current signals; determining an initial glucose concentration based on the transfer function and a ratio of the probing voltage-modulated current signals; determining a connection function value based on the primary current signal and the plurality of probing potential-modulated current signals; determining a final glucose concentration based on the initial glucose concentration and the connection function value.

2. 10. The biosensor system of claim 1, wherein the analyte permeable membrane has a dry thickness in the range of 5 μm to 15 μm.

3. 10. The biosensor system of claim 1, wherein the analyte permeable membrane has a stable thickness in the range of 30 μm to 60 μm in response to subcutaneous insertion of a sensor of the biosensor system into skin, the sensor comprising the electrode system.

4. 10. The biosensor system of claim 1, wherein the analyte catalyst layer has a dry thickness in the range of 1 μm to 3 μm.

5. 10. The biosensor system of claim 1, wherein a thickness ratio of the analyte catalytic layer to the analyte permeable membrane is 1:10 in response to subcutaneous insertion of a sensor of the biosensor system into skin, the sensor comprising the electrode system.

6. 2. The biosensor system of claim 1, wherein the probing potential modulation sequence has a duration of 10% to 20% of a primary data point cycle, a primary data point comprising a measurement of a current signal used to calculate the analyte concentration, and the measurement of the current signal is taken during the steady state condition.

7. The biosensor system of claim 6, wherein the primary data point cycle is in the range of 3 minutes to 15 minutes.

8. 2. The biosensor system of claim 1, wherein the probing potential modulation sequence includes a first voltage potential greater than the constant voltage potential applied during the steady state condition, 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.

9. 2. The biosensor system of claim 1, wherein the probing potential modulation sequence includes a first voltage potential greater than the constant voltage potential applied during the steady state condition, 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.

10. 1. A method of operating a continuous glucose monitoring (CGM) system, comprising: the CGM system includes a sensor, a memory storing computer program code, and a processor in communication with the memory and the sensor; the sensor comprises an electrode system and a membrane system surrounding the electrode system; the membrane system comprises an analyte permeable membrane having an analyte permeability with an analyte solubility lower than the analyte solubility outside the analyte permeable membrane; When the computer program code is executed by the processor, it performs the following steps: applying a constant voltage potential to the sensor in the CGM system to establish a steady state condition of the electrode system and generate a primary current signal; applying the constant voltage potential completely oxidizes the measurable species; and 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 probing potential modulation sequences to the sensor to produce a plurality of probing potential modulation current signals and establish a non-steady state condition in the CGM system; applying the probing potential modulation sequence attenuates oxidation of the measurable species; measuring the plurality of probing potential-modulated current signals in the CGM system and storing the plurality of probing potential-modulated current signals in the memory; determining an initial glucose concentration in the CGM system based on a transfer function and a ratio of the plurality of probing voltage-modulated current signals; determining a connection function value based on the primary current signal and the plurality of probing potential-modulated current signals in the CGM system; determining a final glucose concentration based on the initial glucose concentration and the connection function value in the CGM system; The method of operation is carried out.

11. 11. The method of claim 10, wherein the CGM system further comprises a bias circuit configured to apply the probing potential modulation sequence to a working electrode of the CGM system to induce alternating steady-state and non-steady-state conditions within the electrode system for analyte concentration determination.

12. 11. The method of claim 10, wherein the electrode system has at least one working electrode and one counter electrode coated with an analyte catalyst layer at and near the at least one working electrode for converting an analyte to the measurable species.

13. 11. The method of claim 10, wherein the analyte permeable membrane is configured to trap the measurable species within the analyte permeable membrane such that a steady state of the measurable species resulting from the analyte is established near a surface of the electrode system.

14. 11. The method of claim 10, wherein the analyte permeable membrane has a dry thickness in the range of 5 μm to 15 μm.

15. 11. The method of claim 10, wherein the analyte permeable membrane has a consistent thickness in the range of 30 μm to 60 μm.

16. 11. The method of claim 10, 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.

17. 17. The method of claim 16, wherein the transfer function and the ratio of the plurality of probing potential-modulated current signals are based on the first voltage potential.

18. 17. The method of claim 16, wherein the transfer function and the ratio of the plurality of probing potential-modulated current signals are based on the fourth voltage potential.

19. 11. The method of claim 10, 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.

20. 20. The method of claim 19, wherein the transfer function and the ratio of the plurality of probing potential-modulated current signals are based on the fourth voltage potential and the fifth voltage potential.

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