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

Abstract

A biosensor system is configured to establish steady state conditions and alternate between the steady state conditions 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 is covered with an analyte catalytic layer to convert an analyte to a measurable species. A membrane system includes the electrode system and includes an analyte permeable membrane. The membrane has an analyte permeability, the membrane has a lower analyte solubility than an analyte solubility outside the membrane. The membrane is configured to trap the measurable species within the membrane such that a steady state of the measurable species produced by the analyte is established near the electrode surface. A biasing circuit is configured to apply a potential modulation sequence to the working electrode to alternate steady state conditions and non-steady state conditions within the electrode system in order to determine an analyte concentration.

Description

[0001] Cross-references to related applications

[0002] This application claims 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 CONTINUOUSGLUCOSE MONITORING BY POTENTIAL MODULATION"; and U.S. Provisional Patent Application No. 63 / 061,152, filed August 4, 2020, entitled "EXTRACTING PARAMETERS FOR ANALYTE CONCENTRATION". The benefits of U.S. Provisional Patent Application No. 63 / 061,157 entitled “Biosensor with membrane structure for steady-state and non-steady-state conditions for determining analyte concentrations”, filed August 4, 2020, and U.S. Provisional Patent Application No. 63 / 061,167 entitled “Biosensor with membrane structure for steady-state and non-steady-state conditions for determining analyte concentrations”, each disclosure of which is hereby incorporated herein by reference in its entirety for all purposes. Technical Field

[0003] The present invention generally relates to continuous sensor monitoring of analytes in body fluids, and more specifically, to continuous glucose monitoring (CGM). Background Technology

[0004] Continuous analyte sensing in in vivo or in vitro samples, such as CGM, has become routine sensing operations in the medical device field, and more specifically, in diabetes care. For biosensors that utilize discrete sensing to measure analytes in whole blood samples, such as by pricking a finger to obtain a blood sample, the sample temperature and hematocrit of the blood sample are likely to be the main sources of error. However, for sensors deployed in non-whole blood environments with relatively constant temperatures, such as those used in continuous in vivo sensing operations, other sensor error sources may exist.

[0005] Therefore, there is a need for improved devices and methods for determining glucose levels using CGM sensors. Summary of the Invention

[0006] In some embodiments, a biosensor system is configured to establish steady-state conditions and alternate between steady-state and non-steady-state conditions to determine analyte concentration. The biosensor system includes an electrode system having at least one working electrode and a counter electrode, wherein the working electrode is covered with an analyte catalytic layer to convert the analyte into a measurable substance at and near the working electrode. The biosensor system further includes a membrane system comprising the electrode system and including an analyte-permeable membrane. The analyte-permeable membrane is analyte-permeable, and the analyte solubility in the analyte-permeable membrane is lower than the analyte solubility outside the membrane. The membrane is configured to retain the measurable substance within the membrane, such that a steady state of the measurable substance generated by the analyte is established near the electrode surface. The biosensor system further includes a bias circuit configured to apply a potential modulation sequence to the working electrode to alternate between steady-state and non-steady-state conditions within the electrode system to determine the analyte concentration. The biosensor system further includes a processor and a memory coupled to the processor. The memory contains computer program code stored therein, which, when executed by the processor, causes the processor to: (a) measure and store a primary current signal using the working electrode and the memory; (b) measure and store a plurality of probe potential modulated current signals associated with the primary current signal; (c) determine an initial glucose concentration based on a conversion function and the measured current signal; (d) determine a connection function value based on the primary current signal and the plurality of probe potential modulated current signals; and (e) determine a final glucose concentration based on the initial glucose concentration and the connection function value.

[0007] In some embodiments, a method for determining a glucose value during 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, wherein the membrane system includes an analyte-permeable membrane having analyte permeability with a lower analyte solubility than the analyte solubility outside the membrane. The method further includes: applying a constant voltage potential to the sensor; measuring a primary current signal generated by the constant voltage potential and storing the measured primary current signal in the memory; applying a probe potential modulation sequence to the sensor; measuring a probe potential modulation current signal generated by the probe potential modulation sequence and storing the measured probe potential modulation current signal in the memory; determining an initial glucose concentration based on a transfer function and a ratio of the measured probe potential modulation current signals; determining a connection function value based on the primary current signal and a plurality of the probe potential modulation current signals; and determining a final glucose concentration based on the initial glucose concentration and the connection function value.

[0008] Other aspects, features, and advantages of this disclosure will become apparent from the detailed description and illustrations of the following numerous exemplary embodiments and implementations, including the best mode contemplated for carrying out the invention. Other and different embodiments of this disclosure are also possible, and modifications may be made to various aspects thereof, all without departing from the scope of the invention. For example, although the following description relates to continuous glucose monitoring, the apparatus, systems, and methods described below can be readily adapted to monitor other analytes, such as cholesterol, lactate, uric acid, alcohol, etc., in other continuous analyte monitoring systems. Attached Figure Description

[0009] The accompanying drawings described below are for illustrative purposes and are not necessarily drawn to scale. Therefore, the drawings and description are to be regarded in an illustrative rather than restrictive manner. The drawings are not intended to limit the scope of the invention in any way.

[0010] Figure 1A A graph of the applied voltage E0 versus time is shown for a continuous glucose monitoring (CGM) sensor according to one or more embodiments of the present disclosure.

[0011] Figure 1B A diagram illustrating steady-state conditions occurring at the electrode and its surrounding boundary environment according to one or more embodiments of the present disclosure.

[0012] Figure 1C A diagram illustrating an example of a probe potential modulation (PPM) sequence according to one or more embodiments of the present disclosure is shown.

[0013] Figure 1D A graph illustrating the unsteady-state conditions that occur at the electrode and its vicinity during potential steps E2 and E3, according to one or more embodiments of the present disclosure.

[0014] Figure 1E The following describes implementations based on one or more embodiments of the present disclosure. Figure 1C The IV curves and individual potential steps of the PPM sequence.

[0015] Figure 1F One or more embodiments of the present disclosure are shown. Figure 1C The diagram shows the typical output current of the PPM sequence and the current markings at each potential step.

[0016] Figure 2A -F illustrates a graph of the initial and final currents from a sensor response potential step in an in vitro linear test according to one or more embodiments of this disclosure; specifically, Figure 2A Showing Figure 1C A graph showing the initial and final currents during a potential step 1. Figure 2B Showing Figure 1C A graph showing the initial and final currents during the potential step 2. Figure 2C Showing Figure 1C A diagram of the initial and final currents during a potential step of 3; Figure 2D Showing Figure 1C A diagram of the initial and final currents in potential step 4; Figure 2E Showing Figure 1C The graph shows the initial and final currents during the potential step 5; and Figure 2F Showing Figure 1C The diagram shows the initial and final currents during the potential step 6.

[0017] Figure 3A A graph showing the comparison of attenuation constants K1 and K4 according to one or more embodiments of the present disclosure is presented.

[0018] Figure 3B A graph showing comparison ratio constants R1 and R4 according to one or more embodiments of the present disclosure is presented.

[0019] Figure 3C A graph showing the correlation between the ratio constants R1 and R4 and the attenuation constants K1 and K4 according to one or more embodiments of the present disclosure is provided.

[0020] Figure 3D A graph showing the comparison ratio constants R5 and y45 according to one or more embodiments of the present disclosure is presented.

[0021] Figure 3EA graph showing the comparison ratio constant R2 according to one or more embodiments of the present disclosure is presented.

[0022] Figure 3F A graph showing the ratio constant 1 / R6 according to one or more embodiments of the present disclosure is presented.

[0023] Figure 4A A high-level block diagram of an example CGM device according to one or more embodiments of the present disclosure is shown.

[0024] Figure 4B A high-level block diagram of another example CGM device according to one or more embodiments of the present disclosure is shown.

[0025] Figure 5 This is a side view of an example glucose sensor according to one or more embodiments of the present disclosure.

[0026] Figure 6 This demonstrates a summary of G data from i10, R4, y45, and R1, based on one or more embodiments of this disclosure, using an in vitro dataset. 原始 and G comp The table.

[0027] Figure 7 An example method for determining glucose values ​​during continuous glucose monitoring measurements is shown according to embodiments provided herein. Detailed Implementation

[0028] The embodiments described herein include systems and methods for applying probe potential modulation (PPM) over a constant voltage applied to other aspects of an analyte sensor. The terms “voltage,” “potential,” and “voltage-potential” are used interchangeably herein. Similarly, “current,” “signal,” and “current signal” are used interchangeably herein, such as in “continuous analyte monitoring” and “continuous analyte sensing.” As used herein, PPM refers to the intentional periodic alteration of a constant voltage potential applied to other aspects of the sensor during continuous analyte sensing, such as applying a probe potential step, pulse, or other potential modulation to the sensor. Using PPM during continuous analyte sensing can be referred to as PP or the PPM method, while performing continuous analyte sensing without PPM can be referred to as NP or the NPPM method.

[0029] Primary data points, or primary currents, refer to the measured current signal generated during continuous analyte sensing in response to the analyte at a constant voltage potential applied to the sensor. For example, Figure 1A A graph of the applied voltage E0 versus time is shown for a continuous glucose monitoring (CGM) sensor according to one or more embodiments of this disclosure. Example times for which initial data points can be measured and for which subsequent PPMs can be applied are illustrated. Figure 1A As shown, in this example, the constant voltage potential E0 applied to the working electrode of the analyte sensor can be approximately 0.55 volts. Other voltage potentials can be used.

[0030] Figure 1A An example of a typical cycle of primary data points obtained under a constant applied voltage is shown. Primary data points are data points measured or sampled at regular intervals (e.g., 3–15 minutes) during continuous glucose monitoring with a constant applied voltage, and are used to calculate the user's glucose values. For example, a primary data point could be the working electrode current measured for an analyte sensor during continuous analyte monitoring. Figure 1A The primary data points are not shown, but the time and voltage measured at each primary data point are shown. For example, Figure 1A The circle 102 indicates a time / voltage (3 minutes / 0.55 volts) at which, for a sensor biased at voltage E0, the first primary data point (e.g., the first working electrode current) is measured. Similarly, Figure 1A The circle 104 in the figure represents time / voltage (6 minutes / 0.55 volts), at which a second primary data point (e.g., the second working electrode current) is measured for a sensor biased at voltage E0.

[0031] PPM current refers to the measured value of the current signal generated in response to the PPM applied to the sensor during continuous analyte sensing. The following text combines... Figure 1C PPM is described in more detail.

[0032] A reference sensor is a sensor used to generate primary data points and PPM current in response to a reference glucose concentration, such as that represented by a blood glucose meter (BGM) reading (e.g., primary current and PPM current measured for the purpose of determining a predictive equation for determining analyte concentration that is subsequently stored in a continuous analyte monitoring (CAM) device and used during continuous analyte sensing).

[0033] For sensors deployed in non-whole blood environments with relatively constant temperatures, such as those used in continuous in vivo sensing operations, sensor error can be related to both short-term and long-term sensor sensitivity, as well as subsequent calibration methods. Several challenges / problems exist associated with such continuous sensing operations: (1) long break-in (warm-up) times; (2) factory or field calibration; and (3) sensitivity variations during continuous sensing operations. These problems / problems appear to be related to sensor sensitivity as represented by initial decay (break-in / warm-up time), sensitivity variations due to sensor environmental sensitivity during sensor production, and the environmental / conditions of subsequent sensor deployment.

[0034] For continuous glucose monitoring (CGM) biosensors that typically operate under a constant applied voltage, the current from the mediator is continuously measured due to the enzymatic oxidation of the target analyte, glucose. In practice, the current is usually measured or sensed every 3 to 15 minutes, or at another regular interval, although it is referred to as continuous. There is an initial break-in period when the CGM sensor is first inserted / implanted into the user, which can last from 30 minutes to several hours. Once the CGM sensor has been broken in, its sensitivity may still change for various reasons. Therefore, it is necessary to sense the sensor's operating conditions during its initial period and after the break-in period to identify any changes in its sensitivity.

[0035] After the CGM sensor is subcutaneously inserted / implanted into the user, operation of the sensor begins with the applied voltage E0. The applied voltage E0 is typically located at a point on the redox plateau of the mediator. For the natural mediator of oxygen-glucose oxidase, in a medium with a chloride concentration of approximately 100-150 mM, the oxidation plateau of hydrogen peroxide (H₂O₂, the oxidation product of the enzyme reaction) relative to an Ag / AgCl reference electrode is in the range of approximately 0.5 to 0.8 V. The operating potential of the glucose sensor can be set at 0.55-0.7 V, which is within the plateau region.

[0036] The embodiments described herein employ PPM as a periodic perturbation of a constant voltage potential applied to other aspects of the working electrode of a subcutaneous biosensor during continuous sensing operation (e.g., for monitoring biological sample analytes, such as glucose). During continuous sensing operation, such as continuous glucose monitoring, the sensor working electrode current is typically sampled every 3–15 minutes (or at some other frequency) for glucose value determination. These current measurements represent primary currents and / or primary data points used for analyte determination during continuous sensing operation. In some embodiments, after each primary current measurement, a periodic cycle of probe potential modulation (PPM) may be employed, such that a set of self-supplied currents is accompanied by each primary data point containing information about the sensor / electrode state and / or condition.

[0037] A PPM can contain one or more potential steps that differ from the constant voltage potential typically used during continuous analyte monitoring. For example, a PPM can contain a first potential step above or below a constant voltage potential, a first potential step above or below a constant voltage potential and then back to a constant voltage potential, a series of potential steps above and / or below a constant voltage potential, voltage steps, voltage pulses, pulses of the same or different durations, square waves, sine waves, triangular waves, or any other potential modulation. Examples of PPM sequences are shown in... Figure 1C middle.

[0038] As described, conventional biosensors for continuous analyte sensing operate by applying a constant potential to the working electrode (WE) of the sensor. 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 current generated by the biosensor is attributed only to changes in analyte concentration, not to changes in the applied potential. That is, there is no unsteady current associated with different applied potentials. While this method simplifies continuous sensing operation, the current signal from the data stream from which a constant potential is applied to the sensor provides minimal information about the sensor's state / condition. In other words, the sensor current signal from which a constant potential is applied to the sensor provides little information related to issues associated with long-term continuous monitoring of the sensor, such as batch-to-batch sensitivity variations, long warm-up times due to initial signal attenuation, changes in sensor sensitivity during long-term monitoring, and the effects of varying background interference signals.

[0039] Subcutaneously implanted continuous glucose monitoring (CGM) sensors require timely calibration against reference glucose levels. Typically, the calibration process involves obtaining a blood glucose meter (BGM) reading, or capillary glucose value, from a finger prick glucose measurement and inputting the BGM value into the CGM device to set the CGM sensor's calibration point for the next operating cycle. This calibration process usually occurs once daily, or at least once daily with a finger prick glucose measurement, as the sensitivity of the CGM sensor can vary daily. This is an inconvenient but necessary step to ensure the accuracy of the CGM sensor system.

[0040] The embodiments described herein include systems and methods for applying a PPM over a constant voltage applied to other aspects of an analyte sensor. Methods are provided for developing parameters for a prediction equation that can be used to continuously and accurately determine analyte concentrations from an analyte sensor. Furthermore, methods and systems are provided for determining analyte concentrations using a probe potential modulation (PPM) self-supplied signal. Such methods and systems can allow analyte concentration determination while (1) overcoming the effects of different background interference signals, (2) eliminating or removing the effects of different sensor sensitivities, (3) shortening the warm-up time at the start of (long-term) continuous monitoring, and / or (4) correcting for changes in sensor sensitivity during continuous monitoring. References below... Figure 1A-7 These and other embodiments are described.

[0041] This paper discloses sensor boundary conditions related to non-steady-state (NSS) conditions used to determine analyte concentrations in continuous analyte monitoring operations during PPM cycles. Sensor membrane structure and boundary conditions play unique roles in establishing steady-state (SS), NSS, and alternating SS and NSS conditions for analyte concentration determination. The boundary conditions used for establishing SS and NSS and for analyte concentration determination are described below.

[0042] Steady-state conditions: Conventional biosensors used in continuous analyte sensing operate under steady-state conditions, which are established when the continuously monitored sensor stabilizes at a constant potential applied to the working electrode (WE) after a settling time. Under these conditions, current is drawn from a constant flow of analyte molecules entering the membrane under steady-state diffusion conditions generated by the outer membrane. This condition is described in... Figure 1B middle.

[0043] The typical dry thickness of the outer membrane is approximately 5–15 μm, more likely approximately 8–12 μm. When the sensor is immersed in a liquid sample or subcutaneously inserted into the skin, the membrane structure will be rehydrated and swell to a stable thickness of 30–60 μm, more likely approximately 40–50 μm. During rehydration, the sensor response will vary over time. The typical dry thickness of the enzyme layer is approximately 1–3 μm, more likely below 2 μm. During rehydration, the enzyme layer does not swell due to cross-linking of the adhesive, thus tightly locking the structure in place. For effective sensor operation, the ratio of the enzyme layer to the outer membrane layer can be approximately 1:10 under stable membrane rehydration. Other membrane and / or enzyme layer thicknesses can be used.

[0044] Theoretically, if the boundary structure defined by the enzyme layer and the outer membrane creates a boundary environment to extract a constant flow rate of measurable substances or reduction mediators, it is approximately determined by a straight line C. med As defined, when the analyte concentration remains constant, the current is proportional to the concentration gradient of the measurable substance at the electrode surface, which further depends on the analyte concentration gradient as defined by the boundary conditions.

[0045] Boundary environment: Figure 1B The boundary conditions can be theoretically explained as follows: analyte concentration C 外 The membrane concentration C at the outer interface with the membrane 膜 A certain equilibrium value. The lower concentration C inside the membrane. 膜 This indicates that the membrane is designed to reduce the influx of analyte molecules, allowing the biosensor to operate under steady-state conditions. C 外 With C 膜 The relationship between them is approximately determined by the equilibrium constant K. 外 =C 膜 / C 外< 1 indicates. It is further influenced by D 外 Compared to a lower diffusion coefficient D 膜 Control. Membrane permeability P of the analyte 膜 = D 膜 * C 膜 Together, they define the flux of the analyte. As analyte molecules move toward the enzyme-coated electrode, they are rapidly decayed to zero by the enzyme. Simultaneously, the enzyme converts the analyte molecules into a measurable substance, such as H₂O₂, which can be oxidized at the electrode, with oxygen acting as a mediator relative to glucose oxidase. Once generated, the measurable substance diffuses toward the electrode and toward the membrane.

[0046] Under a constant applied voltage that fully oxidizes the measurable substance, a constant flow rate of the measurable substance is generated toward the electrode. Soon, a steady state is established where the current is related to the concentration gradient of the measurable substance at the electrode surface (dC). med The concentration gradient C is proportional to ( / dx). Under diffusion-limited conditions (meaning the oxidation / consumption rate of the measurable substance is at its maximum and is limited only by the diffusion of the measurable substance), the concentration gradient C med Projected as a straight line, defined as zero at the electrode surface and as a point at the membrane interface, this point is defined by the equilibrium conditions achieved by multiple processes (analyte flow rate into the enzyme, enzyme consumption and conversion of the analyte, and diffusion of the measurable substance). The concentration C entering the membrane. med Roughly defined by diffusion. Preferably, the measurable substance diffuses more slowly inside the membrane than outside, causing it to be trapped inside the membrane starting from the enzyme layer. This steady-state condition changes dynamically with variations in the concentration of the external analyte. Under operating conditions controlled by PPM cycles, primary data points are effectively sampled and recorded under steady-state conditions because the boundary environment returns to steady-state conditions after a non-steady-state potential modulation cycle.

[0047] Potential modulation and unsteady-state conditions: If the applied potential is modulated away from a constant voltage, such as a potential step from 0.55 V to 0.6 V ( Figure 1C Step 1 and Figure 1E Within the E0 to E1 range, but still within the oxidation plateau of the mediator (the diffusion-restricted region in the V-axis), a finite current with slight decay will be generated. This is due to exp(E... app – E 0' The asymmetric plateau period controlled by E is still a Faraday process, where E app It is the applied voltage, and E 0' This is the redox potential representing the electrochemical properties of the medium. This finite current with a small decay can be called plateau degeneracy, meaning a slightly different oxidation state at the plateau. The current-voltage relationship of the mediator is approximately described by… Figure 1F Examples of this type of output current are in... Figure 1F These are shown and 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.

[0048] If the applied potential is reversed to a lower voltage, or specifically from E1 to E2 and further to... Figure 1E E3 in Figure 1C In the step (2 and 3) in the electrode, two things may happen: (1) due to the lower potential, the measurable substance is no longer completely oxidized at the electrode surface, and (2) with the generation of a negative current, the oxidized form of the measurable substance or mediator is partially reduced. The combined effect of these two events results in an excess of measurable substance accumulating at and near the electrode surface. Therefore, the concentration curve is interrupted from the linear condition where it reaches zero at the electrode surface. This condition is called unsteady state, which is as follows: Figure 1D As shown, where C med It is not zero at the electrode surface. For Figure 1C For steps 2 and 3, the output current of this type of effect is shown as negative, and... Figure 1F The components are labeled i21, i22, i23 and i31, i32, i33. Negative currents indicate a partial decrease in the potential step from high to low. The disruption of steady-state conditions occurs only near the electrode surface if the process is short, and the boundary environment (C) between the inside and outside of the membrane... 膜 and C 外 () remains unchanged.

[0049] The alternation of NSS and SS conditions: when the potential reverses again from E3 to E2 in step 4, as... Figure 1C and Figure 1E As shown, a portion of the accumulated measurable substance is consumed, with oxidation occurring at a higher rate set by the higher potential E2. Even though E2 is not in the plateau region of redox substances, this step provides a sudden consumption of the measurable substance and a jump in current output from the non-steady-state concentration, thus providing a strong indication of concentration. Figure 1C The step transition from E2 to E1 in the middle (5) Figure 1E The unsteady oxidation of excess material was further completed to reposition the sensor at the operating potential in the plateau region. Figure 1C The step 6 in the equation employs a negative plateau degenerate step to return to the original potential, which results in the restoration of steady-state conditions before the next potential modulation cycle. Such conditions are described in... Figure 1B Therefore, when the PPM cycle is repeated, steady-state and unsteady-state conditions alternate, providing a signal for analyte concentration determination.

[0050] The PPM method described above provides primary data (e.g., primary current obtained during SS) as an indicator of analyte concentration, while the associated PPM current and PPM parameters are parameters that provide information about sensor and electrode condition compensation. The described example PPM sequence and output current curve both exhibit a potential step from high to low and then return to high, and thus represent an alternation between steady-state and unsteady-state conditions.

[0051] An important aspect of achieving boundary environments that alternate between unsteady and steady-state conditions is membrane maintenance K. 外 = C 膜 / C < 1 relationship, and further through D 外 Lower diffusion coefficient D 膜 Perform. Analyze the membrane permeability P of the analyte. 膜 = D 膜 * C 膜 Together, they define the flux of the analyte. This relationship indicates that the analyte solubility is lower inside the membrane than outside. In some embodiments, K 外 It can be about 0.1–0.9, about 0.2–0.7 in some embodiments, and about 0.2–0.4 in some embodiments.

[0052] If the outer membrane interface is K 外 If the anchorage is less than 1, the multilayered structure of the enzyme and membrane provides a complex mass transport process involving diffusion across two different media. For the incoming analyte, diffusion through the outer membrane is the dominant process, and its concentration is rapidly decayed to zero by the enzyme layer. For enzyme reaction products or measurable substances, mass transport within the enzyme layer will be instantaneous due to its very thinness, according to the generally known definition (Dt). 1 / 2 The diffusion layer thickness was measured; the diffused substance traversed the 3 µm enzyme layer in just 0.18 seconds. For measurable substances, the diffusion coefficient was 5 × 10⁻⁶. -7 Square centimeters per second. On the other hand, under the same diffusion coefficient, it takes 18 seconds for the diffusing substance to cross a membrane with a thickness of 30 µm. This means that during the potential-modulated cycle, the diffusion process in the enzyme layer is practically negligible compared to the diffusion process in the membrane layer. The membrane acts as a trap for the measurable substance, preventing diffusion to the outside of the membrane (under conditions of slow diffusion starting from the enzyme layer). Alternating cycles of steady-state and unsteady-state states, especially at potential steps 4 ( Figure 1C In ), the reverse potential steps 2 and 3 were captured. Figure 1C Almost all excess measurable substances accumulate in the medium. As long as a sufficient amount of mediator is present, the equilibrium constant K... 外 With a controlled, constant supply of analyte, the enzyme reaction will continue at a constant rate, ensuring the establishment of... Figure 1B The steady-state conditions are shown.

[0053] Figure 2A -F The online diagram presents the respective components according to the embodiments provided herein. Figure 1C Example output current signals from five different sensors, representing the initial and final currents generated by potential steps 1 through 6. The following observations were made. First, for potential steps with potential reversal, the initial transient decay of the current is minimal (see...). Figure 2B , 2D And 2F). This includes Figure 1C The potential steps 2, 4, and 6. Second, for potential steps 2, 4, and 6, the corresponding currents are also progressively defined with respect to analyte concentration. Third, in contrast, in in vitro tests, potential steps extending from a steady state or forward to higher potentials produce a strong initial decay of at least one hour for individual sensors. This includes... Figure 1C Potential steps 1 and 5 (see Figure 2A and 2E The circular region, which is the starting point of each sensor in the line diagram. Fourth, the potential step extending the negative potential ( Figure 1C The current in step 3) does not have a clear step corresponding to the analyte concentration, such as Figure 2C As shown. Fifth, regarding the degeneracy condition for the plateau period, such as... Figure 1C For potential steps 1 and 6, the current difference between the initial and final currents of the potential step is very small (see [reference]). Figure 2A and 2F Sixth, for potential steps where the potential direction is switched, the current difference between the initial current and the final current of the potential step is relatively large (see...). Figure 2B and 2D This includes Figure 1C The potential steps 2 and 4.

[0054] From the data presented and observed above, it can be seen that... Figure 1C Potential step 4 (see also) Figure 2D This produces several desirable characteristics, such as minimal initial decay, a clear response to analyte concentration, and a large separation between the initial and final currents within a potential step. Given the description and analysis of data under unsteady-state conditions, this is no coincidence.

[0055] To capture current changes or decays during potential steps, a decay constant is defined to describe the decay process. Two decay constants are designed below: one expressed as a function of ln (natural logarithm), and the other as a simple current ratio. In the ln function representation, the decay constant is defined as K = (ln(i2) – ln(i1)) / (ln(t2) – ln(t1)). In this representation, K = 0 if there is no decay. Furthermore, if the decay constant is close to 0, the decay is small / shallow, while if the decay constant is far from 0, the decay is relatively large / steep. In the current ratio representation, the constant is defined as R = i _t2 / i _t1 In both the K and R definitions, t2 > t1 indicates that t2 is a later time than t1. For a current recording format with three PPM currents for each potential step, two constants are associated with a step. For example, they are the constants defined by i13 / i12 and i13 / i11 for potential step 1. For the embodiment explained 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 representation, if there is no decay, R = 1. The R value for a small decay process is close to 1, while the R value for a large decay process is far from 1.

[0056] Figure 3A -F respectively presents the embodiments provided herein from Figure 2A A graph of the current transition in -F is used to explain the concept of the decay constant and its relationship with steady and unsteady states. Figure 3A It is based on Figure 1C The potential steps of 1 and 4 PPM current through K = (ln(i t=6秒 ) – ln(i t=2秒 The decay constants K1 and K4, calculated as (ln(6 sec) – ln(2 sec)), are compared, with the same dataset from different linear tests on different sensors. By comparing the K constants for different steps, the relative magnitudes of the decay constants reflect the nature of the decay, whether they are shallow or steep, and thus reflect the nature of the electrochemical process. For example, K1 = (ln(i13) - ln(i11)) / (ln(6) - ln(2)). During potential step 4, the K4 value originates from the unsteady-state condition as a result of the oxidation of excess measurable material accumulated during the reverse potential steps 2 and 3. The decay constant values ​​are substantially far from other decay-free conditions or zero values.

[0057] Figure 3B It is a comparison of the ratio constants R1 and R4 (corresponding values ​​of K1 and K4), through ratio = it=6秒 / i t=2秒 from Figure 1C The PPM currents for potential steps 1 and 4 are calculated. For example, R1 = i13 / i11. By comparing the R constants for different steps, the relative magnitudes of the decay constants reflect the nature of the decay, whether they are shallow or steep, and thus reflect the nature of the electrochemical process. The R1 value from potential step 1 is close to 1, indicating no decay condition, while the R4 value from potential step 4 is essentially far from 1 (decay condition). These seemingly implicit constants, reflecting sensor information adjacent to each primary data point, are fed into a multivariate regression (described below), where the most representative parameters are selected for the compensation equation.

[0058] Figure 3C It is the ratio of R1 and R4 and Figure 3A and 3B The correlation between the K1 and K4 values ​​is shown. In summary, the correlation curve reflects different mathematical representations of the decay process. The R1 and K1 constants are close to their no-decay limits, while the R4 and K4 constants are far from these limits. The upper portion of the curve shows R1 = 0.3731 * K1. 2 The curve fitting equation of +1.0112*K1 + 0.9894 shows that when the K1 value is close to zero, the R1 ratio is close to 1 (intercept of 0.9894). This is the no-decay condition, and it is not a coincidence. Both the R constant and the K constant indicate that the decay from a potential step or plateau degenerate process is shallow, with only a limited electrochemical reaction.

[0059] from Figure 3A , 3B The comparison with 3C shows that potential step 4 is involved in the electrochemical reaction more extensively than potential step 1. In terms of response, the current signal and ratio constant from potential step 4 are more sensitive to analyte concentration than those from potential step 1. Furthermore, the signal / ratio ratio in step 4 provides a shorter initial warm-up time than in step 1. The fact that the decay constants provide a step-like response to analyte concentration stems from their response to the underlying current signal at analyte concentration. However, the extracted parameters provide different dimensions of sensor response information, such as the decay of the electrochemical process.

[0060] Figure 3DThe ratios R5 (= i53 / i51) and the step ratio y45 (= i43 / i51) according to the embodiments provided herein are shown. In comparison, R5 ranges between the values ​​of R1 and R4. This indicates that the decay process is steeper than the decay process in potential step 1, but shallower than the decay process in potential step 4. The ratio y45 is not the decay constant defined above, but it still represents a decay constant similar to R4 and provides a very strong response to analyte concentration. Finally, the parameter y45 provides a relative measurement of the process across two potential steps returning to the redox plateau. Besides providing a stepwise response to glucose, this parameter has the lowest intercept or background value among all positive response parameters.

[0061] Figure 3E The constants R2 (= i23 / i21) and R3 (= i33 / i31) are shown. Due to the switching of negative potentials, Figure 1C A potential step of 2 causes a reverse negative current. The resulting negative current switching is partly due to setting the redox state to the incomplete oxidation / partial reduction of the measurable substance at potential E2. However, the current decay remains positive, meaning that in either the positive or negative domain, the later current in the potential step is lower than the earlier current (in absolute value). The R2 ratio provides a strong response inversely proportional to the analyte concentration, although not linear. Figure 1C The potential step 3 further reduces the voltage to different redox ratios, where the overall R3 ratio is not well defined in response to analyte concentration, even though they still provide a positive response.

[0062] According to the literature, hydrogen peroxide (H₂O₂) has a flux of approximately 1.5 × 10⁻⁶. −5 Up to 2 × 10 −5 cm 2 s −1 The effective coefficient of diffusion occurs in water, and its effect on the diffusion rate becomes relatively significant as the water content in the membrane decreases. Therefore, the limiting factor for diffusion becomes water content, while when the water content in the membrane is low, the limiting factor becomes the polymer chains. This property of H2O2 causes the membrane to act as a trap rather than remaining nearby as excess measurable substance before diffusing to the outside of the membrane.

[0063] Various commercially available polymers have been tested for hydrogen peroxide permeability. The effective diffusion coefficient was obtained from the change in H2O2 concentration in two compartments separated by a polymer membrane. The measured value for polyurethane was 5.12 × 10⁻⁶. −9 ± 8.50 ×10 −10 Up to 2.25 × 10 −6 ± 1.00 × 10 −7Within this range, perfluorinated ion exchange membranes such as Nafion® 117 (available from The Chemours Company of Wilmington, Delaware) have measured values ​​in the range of 1.50 × 10⁻⁶. −6 ± 7.00 × 10 −8 Within the range, and the measured value of polymethyl methacrylate (PMMA) was 5.76 × 10⁻⁶. −7 ± 4.60 × 10 −8 Within the range.

[0064] In some embodiments, the PPM cycle or sequence is designed to take no more than half the time of the primary data cycle (e.g., 3-5 minutes) to allow sufficient time for a constant voltage to be applied to the working electrode to restore steady-state conditions before recording the next primary data point. In some embodiments, the PPM cycle may be approximately 1 to 90 seconds, or no more than 50% of a typical 180-second primary data cycle.

[0065] In one or more embodiments, the PPM cycle can be about 10–40 seconds, and / or include more than one modulated potential step near the redox plateau of the mediator. In some embodiments, the PPM sequence can be about 10–20% of a conventional primary data point cycle. For example, when a conventional 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 of the primary data cycle allows the steady-state conditions to recover under a constant applied voltage. For potential steps in the PPM cycle, the duration is transient, making the boundary conditions of the measurable substance resulting from these potential steps non-steady-state. Therefore, in some embodiments, each potential step can be about 1–15 seconds, in other embodiments about 3–10 seconds, and in still other embodiments about 4–6 seconds.

[0066] In some embodiments, the probe potential modulation can step into the potential region of non-diffusion-limited redox conditions, or the kinetic region of the mediator (meaning the output current depends on the applied voltage, where the higher the applied voltage, the greater the output current generated from the electrode). For example... Figure 1E E2 and E3 ( Figure 1C Steps 2 and 3) are two potential steps in the dynamic region of the dielectric that generate unsteady output current from the electrodes. When the potential steps reverse, the applied voltages E2 and E1 of the same amplitude are restored to detect the unsteady output current from the electrodes.

[0067] Different embodiments can be employed, accompanied by unsteady-state conditions. For example, the unsteady-state conditions can also be achieved by directly detecting the target potential E2 in one step and returning to the initial potential E1, followed by a second detection potential step, directly reaching a different potential E3 in a kinetic region with different unsteady-state conditions, and then directly returning to the initial potential E1. The aim is to modulate the applied potential, thereby generating alternations between steady-state and unsteady-state conditions for the measurable substance at the electrode surface, whereby the signal from the unsteady-state condition can be used to determine the analyte concentration.

[0068] Use of transformation functions and join functions

[0069] Considering the uncertainty in establishing a one-to-one correlation between in vitro and in vivo sensitivity, this paper discloses a method for establishing a connection between in vitro and in vivo glucose by applying a unified "transformation function" to data from a wide-range sensor response, followed by applying a "connection function" to reduce glucose error to a narrow band. The unified transformation function calculates the original or "initial" glucose value G. 原始 = f(signal), where "signal" is the measured current signal (or a parameter obtained from one or more measured current signals), and "f" can be a linear or nonlinear function. When the transfer function f is nonlinear, the sensitivity or response slope is not applied (as described below).

[0070] In its simplest form, the unified transfer function can be a linear relationship between the measured current signal and a reference glucose level obtained from in vitro test data. For example, the unified transfer function can be a glucose signal (e.g., Iw-Ib, R1, R4, y45, or another PPM current signal or parameter), a slope, and a reference glucose level (G). ref Linear relationship between them:

[0071] Signal = Slope * G ref

[0072] Make

[0073] G ref = Signal / Slope

[0074] Where the slope represents the composite slope (slope) 复合 This is also known as the uniform composite slope. The above relationship can then be used to calculate the initial or raw glucose G during CGM. 原始 :

[0075] G 原始 = Signal / Slope 复合

[0076] PPM current signal parameters such as R1, R4, and y45 may be less sensitive to interference effects and exhibit lower preheating sensitivity. Therefore, in some embodiments provided herein, the uniform composite slope can be determined from PPM current signal parameters such as R1, R4, and y45 or another suitable PPM current signal parameter. In some embodiments, a nonlinear transformation function, such as a polynomial, can be used instead of a linear transformation function (e.g., to better fit the sensor's changing response). For example, R1, R4, and y45 are compared with a reference glucose G... ref A polynomial fit can be used as a connection function to determine initial or raw glucose values ​​based on R1, R4, or y45. Below are example equations for R1, R4, and y45:

[0077] For R1:G 原始 = 4351.9*(R1) 2 - 4134.4*(R1) + 1031.9

[0078] For R4:G 原始 = 5068*(R4) 2 - 2213.3*(R4) + 290.05

[0079] For y45:G 原始 = 6266.8*(y45) 2 - 1325.2*(y45) + 117.49

[0080] Other relations can be used. It should be noted that the equivalent form of Iw–Ib of the primary data (i10) can be used. However, since R1, R4, and y45 are relatively independent of interference from other interfering substances, background subtraction is not used. In some embodiments, multiple transformation functions can be used.

[0081] If the connection function is applied to a single error (deviation % = 100% * ΔG / G = 100% * (G) 原始 – G ref ) / G ref This allows for narrow band glucose delivery, and a single conversion simplifies in vitro-to-in vivo connections without calibration. This connection function is based on ΔG / G... 原始 The value is derived from the PPM parameter. This is achieved by reducing the amount of glucose from the initial or raw glucose G. 原始 The error band approach, the connection function is called establishing a connection from outside to inside without calibration, which means adapting the sensor to all responses of a narrow error band.

[0082] A connection function is referred to as a wide-range connection from in vitro glucose to in vivo glucose when it provides predicted in vivo glucose values ​​to a narrow error band without calibration. In this context, no one-to-one correspondence between in vitro and in vivo sensitivity is sought. Instead, the connection function provides glucose values ​​from the sensor across the sensitivity range, provided the sensor is responsive to glucose. The response can be linear or non-linear.

[0083] Utilizing the rich information about the CGM sensor derived from the PPM current, this function is derived from the PPM current and related parameters. Each response data point in the periodic cycle is then converted to a glucose value G via a composite conversion function. 原始 At that time, there exists an associated error or deviation % ΔG / G 原始 = (G 原始 – G ref ) / G ref By setting G 连接 = G ref Then G 连接 = G 原始 / (1 + ΔG / G 原始 ) = G 原始 / (1 + connect function), where connect function = ΔG / G 原始 = f(PPM parameters). One way to derive the connection function is by using the relative error ΔG / G... 原始 It was performed using the target of multivariate regression and the input parameters from the PPM parameters.

[0084] In summary, in some embodiments, the R1, R4, or y45 PPM parameters can be used as part of a conversion function to convert the raw current signal information into the raw or initial glucose value G. 原始 Once we know G 原始 Then, a connection function can be used to calculate the compensated or final glucose signal or concentration G. comp For example, the SS signal (i10) and the NSS signal (PPM signal) can be used as input parameters, using the relative error ΔG / G. 原始 As the target of multivariate regression, a join function is derived from the in vitro data. An example join function CF is provided below for parameter R4. It should be understood that other quantities and / or types of terms may be used.

[0085] 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*z65z32 -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*z41y31 - 1.04261*z31y32 -0.014889*Gz61y42 + 0.007133*Gz63y65 + 0.019989*Gz64y51 + 0.004536*Gz64y43 -0.01605*Gz65y54 + 0.00011*Gz52y32 + 0.004775*Gz53y54 - 0.531827*d32 -0.026387*Gd11 - 0.010296*Gd21 + 0.003426*Gd32 - 6.350168*d21d31 + 8.39652*d22d31 - 0.0329025*Gd11d31 - 0.039527*av1 - 2.342127*av1i10 + 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.

[0086] For example, the input parameters of the join function CF can be of the following types.

[0087] Detection current: Detection potential modulated currents i11, i12, i13, ..., i61, i62, i63, where the first number (x) in ixy format represents the potential step, and the second number (y) represents the current measurement performed after the potential step is applied (e.g., the first, second, or third measurement).

[0088] R parameters: These ratios are calculated by dividing the final PPM current by the first PPM current within a potential step. For example, R1 = i13 / i11, R2 = i23 / i21, R3 = i33 / i31, R4 = i43 / i41, R5 = i53 / i51, and R6 = i63 / i61.

[0089] X-type parameters: The general format of this type of parameter is given by dividing the final PPM current of the subsequent potential step by the final PPM current of the previous potential step. For example, parameter x61 is given by i 6 3 / i 1 3. It is determined that i63 is the final PPM current of step 6 out of the three recorded currents for each step, and i13 is the final PPM current of step 1. Additionally, 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.

[0090] Y-type parameters: The general format of this type of parameter is given by dividing the final PPM current of the subsequent potential step by the first PPM current of the previous potential step. For example, parameter y61 is given by i 6 3 / i 11. It is determined that i63 is the final PPM current of step 6 out of the three recorded currents for each step, and i11 is the first PPM current of step 1. Additionally, 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.

[0091] Z-type parameters: The general format of this type of parameter is given by dividing the first PPM current of the subsequent potential step by the last PPM current of the previous potential step. For example, parameter z61 is given by i 6 1 / i 1 3. Where i61 is the first PPM current of step 6 out of the three recorded currents for each step, and i13 is the final PPM current of step 1. Additionally, 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.

[0092] Additional terms include normalized current: ni11 = i11 / i10, ni12 = i12 / i10...; relative difference: d11 =(i11 – i12) / i10, d12 = (i12 – i13) / i10...; average current per PPM potential step: av1 = (i11 + i12 + i13) / 3, av2 = (i21 + i22 + i23) / 3, ...; and average current ratio av12 = av1 / av2, av23 = av2 / av3... Other miscellaneous terms include GR1 = G 原始 *R1, Gz61 = G 原始 *z61, Gy52 = G 原始*y52..., R63R51 = R63 / R51, R64R43 = R64 / R43..., z64z42 = z64 / z42, z65z43 = z65 / z43..., d11d31 = d11 / d31, d12d32 = d12 / d32..., Gz61y52 = G*z61 / y52... etc.

[0093] Other types of parameters can also be used, such as the PPM current difference or the ratio of relative difference or medium PPM current carrying equivalent or similar information.

[0094] Therefore, the extracted parameters R1, R4, and y45 can be used to indicate the original glucose analyte concentration, and the linkage function can be used in conjunction with the original glucose analyte concentration to link in vitro glucose to in vivo glucose. Figure 6 Table 600 summarizes the transformation functions to G. 原始 and connect function to G comp The compensation results show that R1, R4, and y45 can be used as analyte indicator signals, and that a wide range of responses can be converged to a narrow glucose value band through a connection function.

[0095] Figure 4A A high-level block diagram of an example CGM device 400 according to embodiments provided herein is shown. Although not shown in... Figure 4A As shown, but it should be understood, various electronic components and / or circuits are configured to be coupled to a power source, such as, but not limited to, a battery. The CGM device 400 includes a bias circuit 402, which can be configured to be coupled 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 example embodiment, the analyte-containing fluid can be a human serous fluid, and the bias voltage can be applied to one or more electrodes 405 of the CGM sensor 404 (e.g., a working electrode, a background electrode, etc.).

[0096] The bias circuit 402 can also be configured to apply a bias to the CGM sensor 404 such as Figure 1C The PPM sequence shown or another PPM sequence. For example, the PPM sequence may be applied initially and / or in the middle of a time period, or applied to each primary data point. For example, the PPM sequence may be applied before, after, or both before and after measuring the primary data points.

[0097] In some embodiments, the CGM sensor 404 may comprise two electrodes, and a bias voltage and probe potential modulation (PPM) may be applied across these electrodes. In such cases, the current through the CGM sensor 404 can be measured. In other embodiments, the CGM sensor 404 may comprise three electrodes, such as a working electrode, a counter electrode, and a reference electrode. In such cases, for example, a bias voltage and PPM may be applied between the working electrode and the reference electrode, and the current through the working electrode can be measured. The CGM sensor 404 contains chemicals that react with a glucose-containing solution in a reduction-oxidation reaction, said chemicals affecting the concentration of charge carriers and the time-dependent impedance of the CGM sensor 404. Example chemicals include glucose oxidase, glucose dehydrogenase, etc. In some embodiments, mediators such as ferricyanide or ferrocene may be employed.

[0098] For example, the continuous bias voltage generated and / or applied by the bias circuit 402 relative to the reference electrode can be in the range of about 0.1 to 1 volt. Other bias voltages can be used. Example PPM values ​​have been described previously.

[0099] In response to PPM and a constant bias voltage, the PPM current and non-PPM (NPPM) current passing through the CGM sensor 404 in the analyte-containing fluid can be transmitted from the CGM sensor 404 to a current measurement (I). 测量 Circuit 406 (also referred to as a current sensing circuit system). Current measurement circuit 406 can be configured to sense and / or record a current measurement signal having a magnitude indicating the amount of current transmitted from CGM sensor 404 (e.g., using a suitable current-to-voltage converter (CVC)). In some embodiments, current measurement circuit 406 may include a resistor having a known nominal value and a known nominal accuracy (e.g., 0.1% to 5%, or even less than 0.1% in some embodiments), through which the current transmitted from CGM sensor 404 passes. The voltage generated across the resistor in current measurement circuit 406 represents the magnitude of the current and can be referred to as the current measurement signal.

[0100] In some embodiments, sampling circuitry 408 may be coupled to current measurement circuitry 406 and may be configured to sample the current measurement signal. Sampling circuitry 408 may generate digitized time-domain sampled data (e.g., a digitized glucose signal) representing the current measurement signal. For example, sampling circuitry 408 may be any suitable A / D converter circuit configured to receive the current measurement signal as an analog signal and convert it into a digital signal with a desired number of bits as output. In some embodiments, the number of bits output by sampling circuitry 408 may be sixteen, but more or fewer bits may be used in other embodiments. In some embodiments, sampling circuitry 408 may sample the current measurement signal at a sampling rate ranging from 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 and downsampled to further reduce the signal-to-noise ratio. Any suitable sampling circuitry system may be employed.

[0101] Still referencing Figure 4A The processor 410 may be coupled to the sampling circuit 408 and to the memory 412. In some embodiments, the processor 410 and the sampling 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 coupling between the processor 410 and the sampling circuit 408 may be implemented via the memory 412. In this embodiment, the sampling circuit 408 writes digital data to the memory 412, and the processor 410 reads the digital data from the memory 412.

[0102] Memory 412 may store one or more prediction equations 414 for determining glucose values ​​based on primary data points (NPPM current) and PPM currents (from current measurement circuit 406 and / or sampling circuit 408). In some cases, the prediction equations may include transition functions and / or connection functions. For example, in some embodiments, two or more prediction equations may be stored in memory 412, each equation for a different segment (time period) of data collected by CGM. In some embodiments, memory 412 may contain prediction equations based on a primary current signal generated by applying a constant voltage potential to a reference sensor, and multiple PPM current signals generated by applying a PPM sequence between primary current signal measurements.

[0103] The memory 412 may also store multiple instructions therein. In various embodiments, the processor 410 may be a computing 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 execute as a microcontroller, etc.

[0104] In some embodiments, a plurality of instructions stored in memory 412 may include instructions that, when executed by processor 410, cause processor 410 to: (a) cause CGM device 400 (via bias circuit 402, CGM sensor 404, current measurement circuit 406, and / or sampling circuit 408) to measure current signals (e.g., primary current signals and PPM current signals) from interstitial fluid; (b) store the current signals in memory 412; (c) calculate prediction equation parameters, such as the ratio (and / or other relationships) of currents from different voltage steps or other voltage changes within a PPM sequence; (d) use the calculated prediction equation parameters to calculate glucose values ​​(e.g., concentrations) using the prediction equation; and / or (e) transmit glucose values ​​to a user.

[0105] Memory 412 can be any suitable type of memory, such as, but not limited to, one or more of volatile and / or non-volatile memory. Volatile memory can include, but is not limited to, static random access memory (SRAM) or dynamic random access memory (DRAM). Non-volatile memory can include, but is not limited to, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., EEPROM of one type in either non-configurable or non-configurable, and / or stacked or planar arrangement and / or single-level cell (SLC), multi-level cell (MLC), or combined SLC / MLC arrangement), resistive memory, filamentary memory, metal-oxide memory, phase-change memory (e.g., chalcogenide memory), or magnetic memory. For example, memory 412 can be packaged as a single chip or multiple chips. In some embodiments, memory 412 can be embedded in an integrated circuit, such as, for example, an application-specific integrated circuit (ASIC), along with one or more other circuits.

[0106] As described above, memory 412 may have a plurality of instructions stored therein, which, when executed by processor 410, cause processor 410 to perform various actions specified by one or more of the stored instructions. Memory 412 may further have portions reserved for one or more "scratchpad" storage areas, which can be used by processor 410 in response to the execution of one or more of the instructions for read or write operations.

[0107] exist Figure 4AIn some embodiments, the bias circuit 402, CGM sensor 404, current measurement circuit 406, sampling circuit 408, processor 410, and memory 412 containing prediction equation 414 may be housed within the wearable sensor portion 416 of the CGM device 400. In some embodiments, the wearable sensor portion 416 may include a display 417 for displaying information such as glucose concentration information (e.g., without the use of external devices). The display 417 may be any suitable type of human-perceptible display, such as, but not limited to, liquid crystal display (LCD), light-emitting diode (LED) display, or organic light-emitting diode (OLED) display.

[0108] Still referencing Figure 4A The CGM device 400 may further include a portable user device portion 418. A processor 420 and a display 422 may be housed 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 displayed by the display 422. The wearable sensor portion 416 and the portable user device portion 418 may be communicatively coupled. In some embodiments, for example, the communicative coupling of the wearable sensor portion 416 and the portable user device portion 418 may be performed wirelessly via transmitter circuitry and / or receiver circuitry, such as the transmit / receive circuitry TxRx 424a in the wearable sensor portion 416 and the transmit / receive circuitry TxRx 424b in the portable user device 418. Such wireless communication may be performed in any suitable manner, including but not limited to standard-based communication protocols 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 conducted via near field communication (NFC), radio frequency (RF), infrared (IR), or optical communication. In some embodiments, the wearable sensor portion 416 and the portable user device portion 418 may be connected via one or more wires.

[0109] Display 422 can be any suitable type of human-perceptible display, such as, but not limited to, liquid crystal display (LCD), light-emitting diode (LED) display, or organic light-emitting diode (OLED) display.

[0110] Now for reference Figure 4B An example CGM device 450 is shown, which is similar to Figure 4AThe illustrated embodiments differ in their component divisions. In the CGM device 450, the wearable sensor portion 416 includes a bias circuit 402 coupled to the CGM sensor 404, and a current measurement circuit 406 coupled to the CGM sensor 404. The portable user device portion 418 of the CGM device 450 includes a sampling circuit 408 coupled to the processor 420, and a display 422 coupled to the processor 420. The processor 420 is further coupled to a memory 412, which may contain a prediction equation 414 stored therein. In some embodiments, the processor 420 in the CGM device 450 may also perform, for example, operations by... Figure 4A The processor 410 of the CGM device 400 performs the functions previously described. The wearable sensor portion 416 of the CGM device 450 can be compared to Figure 4A The CGM device 400 is smaller and lighter, and therefore less invasive, because the sampling circuitry 408, processor 410, memory 412, etc., are not included. Other component configurations can be used. For example, as Figure 4B In a variant of the CGM device 450, the sampling circuit 408 can be retained on the wearable sensor section 416 (so that the portable user device 418 receives the digitized glucose signal from the wearable sensor section 416).

[0111] Figure 5 This is a side view of an example 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 substance that reacts with a glucose-containing solution in a reduction-oxidation reaction (the chemical substance affects the concentration of charge carriers and the time-dependent impedance of the CGM sensor 404). In some embodiments, the working electrode may be formed of platinum or a roughened platinum surface. Other working electrode materials may be used. Example chemical catalysts (e.g., enzymes) used for the working electrode 502 include glucose oxidase, glucose dehydrogenase, etc. For example, the enzyme component may be immobilized to the electrode surface by a crosslinking agent such as glutaraldehyde. An outer membrane layer may be applied to the enzyme layer to protect the entire internal assembly containing the electrode and the enzyme layer. In some embodiments, a mediator such as ferricyanide or ferrocene may be used. Other chemical catalysts and / or mediators may be used.

[0112] In some embodiments, the reference electrode 504 may be formed of Ag / AgCl. The counter electrode 506 and / or the background electrode 508 may be formed of a suitable conductor such as platinum, gold, palladium, etc. Other materials may be used for the reference electrode, counter electrode, and / or background electrode. In some embodiments, the background electrode 508 may be the same as the working electrode 502, but without a chemical catalyst and mediator. The counter electrode 506 may be isolated from other electrodes by an insulating layer 510 (e.g., polyimide or another suitable material).

[0113] Figure 7 An example method 700 for determining glucose values ​​during continuous glucose monitoring measurements according to embodiments provided herein is illustrated. Method 700 includes, in block 702, providing a CGM device (e.g., including a sensor, memory, and processor). Figure 4A and 4B The CGM device (400 or 450) wherein the sensor includes an electrode system and a membrane system surrounding the electrode system, and the membrane system includes an analyte-permeable membrane having analyte permeability with a lower analyte solubility than the analyte solubility outside the membrane.

[0114] Method 700 further includes, in block 704, applying a constant voltage potential to the sensor (e.g., Figure 1A In block 706, method 700 includes measuring a primary current signal generated by a constant voltage potential and storing the measured primary current signal in a memory. In block 708, method 700 includes applying a probe potential modulation sequence to a sensor (e.g., E0 in the context of a voltage potential). Figure 1C The method 700 includes measuring a probe potential modulation current signal generated by the probe potential modulation sequence in block 710, and storing the measured probe potential modulation current signal in memory. The method 700 further includes: determining an initial glucose concentration based on a transfer function and multiple measured probe potential modulation current signals in block 712; determining a connection function value based on the primary current signal and multiple probe potential modulation current signals in block 714; and determining a final glucose concentration based on the initial glucose concentration and the connection function value in block 716. The final glucose concentration can be transmitted to the user (e.g., via...). Figure 4A Or a 4B monitor 417 or 422).

[0115] It should be noted that some embodiments or portions thereof may be provided as computer program products or software, which may include a machine-readable medium having non-transitory instructions stored thereon, the machine-readable medium being usable for programming a computer system, controller or other electronic device to perform processes according to one or more embodiments.

[0116] While this disclosure is susceptible to various modifications and alternative forms, its specific methods and apparatus have been shown by way of example in the accompanying drawings and are described in detail herein. However, it should be understood that the specific methods and apparatus disclosed herein are not intended to limit this disclosure or the claims.

Claims

1. A biosensor system configured to establish steady-state conditions and alternate between the steady-state conditions and non-steady-state conditions to determine analyte concentration, the biosensor system comprising: An electrode system having at least one working electrode, the at least one working electrode being covered with an analyte catalytic layer to convert the analyte into a measurable substance; A membrane system comprising the electrode system and an analyte-permeable membrane configured to retain the measurable substance within the analyte-permeable membrane, thereby establishing the steady-state conditions; A bias circuit configured to apply a probe potential modulation sequence to the at least one working electrode to establish the unsteady-state condition within the electrode system; At least one processor; as well as One or more non-transitory computer-readable media containing computer-executable instructions, which, when executed by the at least one processor, cause the at least one processor to perform a method for determining a glucose value during continuous glucose monitoring, the method comprising: The steady-state condition is established by applying a constant voltage potential to the electrode system through the bias circuit. The constant voltage potential is applied to completely oxidize the measurable substance; The primary current signal generated by the constant voltage potential is measured using the at least one working electrode; Information indicating the primary current signal is stored on one or more non-transitory computer-readable media; The steady-state analyte concentration is determined based on the information indicating the primary current signal; The non-steady-state condition is periodically established by applying the probe potential modulation sequence to the electrode system through the bias circuit. The aforementioned unsteady-state conditions involve the accumulation of the measurable substance at or near the surface of at least one working electrode. The application of the probe potential modulation sequence includes applying a first voltage potential larger than the constant voltage potential, applying a second voltage potential smaller than the constant voltage potential, applying a third voltage potential smaller than the second voltage potential, applying a fourth voltage potential larger than the third voltage potential, and applying a fifth voltage potential larger than the fourth voltage potential. The application of the second and third voltage potentials of the probe potential modulation sequence reduces the oxidation of the measurable substance; While the electrode system is under the aforementioned unsteady-state conditions, multiple probe potential modulated current signals generated from the unsteady-state conditions are measured using the at least one working electrode. Multiple ratio parameters are determined from the multiple probe potential modulated current signals. The plurality of ratio parameters include the ratio of the last current signal measured during the fourth voltage potential to the first current signal measured during the fifth voltage potential; The concentration of the unsteady analyte is determined based on the aforementioned ratio parameters; The initial glucose concentration is determined based on the steady-state analyte concentration; The linkage function value is determined based on the steady-state analyte concentration and the unsteady-state analyte concentration; and The final glucose concentration is determined based on the initial glucose concentration and the connection function value.

2. The biosensor system according to claim 1, wherein the dry thickness of the analyte-permeable membrane is in the range of 5 μm to 15 μm.

3. The biosensor system of claim 1, wherein, in response to subcutaneous insertion of the biosensor system into the skin, the stable thickness of the analyte-permeable membrane is in the range of 30 μm to 60 μm.

4. The biosensor system according to claim 1, wherein the dried thickness of the analyte catalyst layer is in the range of 1 μm to 3 μm.

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

10.

6. The biosensor system of claim 1, wherein the duration of the probe potential modulation sequence is 10% to 20% of the primary data point cycle, wherein the primary data point includes at least one of the plurality of probe potential modulation current signals.

7. The biosensor system of claim 6, wherein the primary data points are cycled in the range of 3 to 15 minutes.

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