Determination of transient state of analyte concentration for continuous glucose monitoring using potential modulation.

Abstract

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

Description

[Technical Field]

[0001] This application relates to U.S. Provisional Patent Application No. 63 / 061,135, titled "Continuous Analyte Monitoring Sensor Calibration and Measurements by a Connection Function," filed on August 4, 2020; U.S. Provisional Patent Application No. 63 / 061,152, titled "Non-Steady-State Determination of Analyte Concentration for Continuous Glucose Monitoring by Potential Modulation," filed on August 4, 2020; U.S. Provisional Patent Application No. 63 / 061,157, titled "Extracting Parameters for Analyte Concentration Determination," filed on August 4, 2020; and "BIOSENSOR WITH MEMBRANE STRUCTURE FOR STEADY-STATE AND Claiming interest in U.S. Provisional Patent Application No. 63 / 061,167, entitled “NON-STEADY-STATE CONDITIONS FOR DETERMINING ANALYTE CONCENTRATIONS,” the disclosures thereof, each of which, for all purposes, are incorporated herein by reference in their entirety.

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

[0003] For example, continuous analyte detection in in vivo or in vitro samples, such as CGM, has become an everyday detection operation in the field of medical devices, more specifically in diabetes care. For example, in the case of a biosensor that measures an analyte in a whole blood sample using individual detections such as pricking a finger with a needle to obtain a blood sample, the temperature of the sample and the hematocrit of the blood sample can be the main causes of errors. However, in the case of sensors deployed in a non-whole blood environment having a relatively constant temperature, such as sensors used in continuous in vivo detection operations, there may be other causes of sensor errors.

[0004] Therefore, an improved apparatus and method for determining glucose values using a CGM sensor are desired.

Summary of the Invention

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

[0006] In some embodiments, a continuous glucose monitoring (CGM) device includes a wearable portion having a sensor configured to generate an electrical current signal from interstitial fluid, a processor, a memory coupled to the processor, and a transmitter circuit coupled to the processor. The memory includes a connection function based on a primary current signal generated by the application of a constant voltage potential applied to a reference sensor and a plurality of probing potential modulation current signals generated by the application of a probing potential modulation sequence applied during the measurement of the primary current signal. The memory includes computer program code stored in the memory, which, when executed by the processor, causes the CGM device to use the sensor and memory of the wearable portion to measure and store the primary current signal, measure and store a plurality of probing potential modulation current signals associated with the primary current signal, determine an initial glucose concentration based on the conversion function and the measured probing potential modulation current signals, determine a connection function value based on the primary current signal and the plurality of probing potential modulation current signals, and 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) measurements is provided. The method includes providing a CGM device including a sensor, a memory, and a processor, applying a constant voltage potential to the sensor, measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in the memory, applying a probing potential modulation sequence to the sensor, measuring a probing potential modulation current signal resulting from the probing potential modulation sequence and storing the measured probing potential modulation current signal in the memory, determining a conversion function value based on the measured probing potential modulation current signal, determining an initial glucose concentration based on the conversion function value, determining a connection function value based on the primary current signal and the plurality of probing potential modulation current signals, and determining a final glucose concentration based on the initial glucose concentration and the connection function value.

[0008] Further aspects, features, and advantages of this disclosure may be readily apparent from the following detailed description and illustration of several exemplary embodiments and implementations, including the best mode intended for carrying out the invention. This disclosure may enable other different embodiments without departing from the scope of the invention, and some of its details may be modified in various ways. For example, while the following description relates to continuous glucose monitoring, the devices, systems, and methods described below may be readily adapted for monitoring other analytes in other continuous analytes monitoring systems, such as cholesterol, lactic acid, uric acid, alcohol, and similar substances. [Brief explanation of the drawing]

[0009] The drawings described below are for illustrative purposes only and are not necessarily drawn to scale. Therefore, the drawings and descriptions should be considered illustrative and not limiting. The drawings are not intended to limit the scope of the invention in any way. [Figure 1] Figure 1A illustrates a graph of applied voltage E0 versus time for a continuous glucose monitoring (CGM) sensor according to one or more embodiments of the present disclosure. Figure 1B illustrates a graph of the current profile of a probing potential modulation (PPM) sequence for the CGM sensor in Figure 1A according to one or more embodiments of the present disclosure. [Figure 2A] Graphs of steady-state conditions associated with electrodes and their near-boundary environments according to one or more embodiments of this disclosure are illustrated. [Figure 2B] A graph illustrating an example of a probing potential modulation (PPM) sequence according to one or more embodiments of the present disclosure is provided. [Figure 2C] The graphs illustrating the transient state conditions associated with the electrode and its near-boundary environment during the E2 and E3 potential steps, according to one or more embodiments of this disclosure, are illustrated. [Figure 2D] The IV curves and graphs of individual potential steps of a PPM sequence implemented according to one or more embodiments of this disclosure are illustrated. [Figure 2E] A graph illustrating the return from a non-steady state (NSS) condition to a steady state (SS) condition after a PPM cycle, according to one or more embodiments of this disclosure, is provided. [Figure 2F] The present disclosure illustrates a typical output current in a current implementation of a PPM sequence and a graph of current labeling at each potential step, according to one or more embodiments of this disclosure. [Figure 3A] This disclosure illustrates graphs of the temporal current profiles of primary data points in linearity tests with four levels of acetaminophen using PPM and non-PPM (NPPM) methods according to one or more embodiments of this disclosure. [Figure 3B] This disclosure illustrates graphs of the primary current response to glucose under non-PPM applied voltages in linearity tests with four levels of acetaminophen using the PPM method according to one or more embodiments of this disclosure. [Figure 3C] The graphs illustrating the primary current response under PPM applied voltage to glucose in the same test, according to one or more embodiments of the present disclosure, are illustrated. [Figure 3D] The graph illustrates the i43 current response lines under a PPM applied voltage for linearity at four levels of acetaminophen having a PPM current i43 response to glucose in the same test, according to one or more embodiments of the present disclosure. [Figure 4A] Illustrative graphs of the initial current profiles of SS current i10 and NSS current i43 in a linearity test using the PPM method according to one or more embodiments of this disclosure are provided. [Figure 4B] Illustratively illustrating graphs of individual normalized SS currents i10 and normalized NSS currents i43 from seven different sensors during the first 60 minutes, as well as the average current of these two groups, according to one or more embodiments of the present disclosure. [Figure 4C] This specification illustrates an in vitro linearity test of i43 current versus reference glucose using 10 different sensors, according to one or more embodiments provided herein. [Figure 5A]High-level block diagrams illustrating exemplary CGM devices according to one or more embodiments of the present disclosure are illustrated below. [Figure 5B] High-level block diagrams of other exemplary CGM devices according to one or more embodiments of the present disclosure are illustrated. [Figure 6] This is a schematic side view of an exemplary glucose sensor according to one or more embodiments of the present disclosure. [Figure 7] This specification illustrates an exemplary method for determining glucose levels during continuous glucose monitoring (CGM) measurement, according to embodiments provided herein. [Figure 8] This specification illustrates another exemplary method for determining glucose levels during CGM measurement, according to embodiments provided herein. [Modes for carrying out 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. The terms “current,” “signal,” and “current signal” are also used interchangeably herein, as are “continuous analyte monitoring” and “continuous analyte detection.” As used herein, PPM refers to a deliberate, periodic change in a constant voltage potential applied to a sensor during continuous analyte detection, such as the application of a probing potential step, pulse, or other potential modulation to the sensor. The use of PPM during continuous analyte detection may be referred to as the PP or PPM method, while continuous analyte detection without PPM may be referred to as the NP or NPPM method.

[0011] A primary data point or primary current refers to a measured value of the current signal generated in response to an analyte at a constant voltage potential applied to the sensor during continuous analyte detection. For example, Figure 1A illustrates a graph of applied voltage E0 versus time for a continuous glucose monitoring (CGM) sensor according to one or more embodiments of the present disclosure. Exemplary times are shown at which primary data point measurements may be taken and subsequent PPMs may be applied. As shown in Figure 1A, in this example, the constant voltage potential E0 applied to the operating electrode of the analyte sensor may be approximately 0.55 volts. Other voltage potentials may be used. Figure 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 regular intervals, such as 3 to 15 minutes, during continuous glucose monitoring at a constant applied voltage and used to calculate the user's glucose value. A primary data point may be, for example, the operating electrode current measured for an analyte sensor during continuous analyte monitoring. Figure 1A shows the time and voltage at which each primary data point is measured, rather than showing the primary data points themselves. For example, square 102 in Figure 1A represents the time / voltage (3 minutes / 0.55 volts) measured for a sensor biased with voltage E0, where the first primary data point (e.g., the first operating electrode current) is a first primary data point. Similarly, square 104 in Figure 1A represents the time / voltage (6 minutes / 0.55 volts) measured for a sensor biased with voltage E0, where the second primary data point (e.g., the second operating electrode current) is a second primary data point.

[0012] PPM current refers to the measured value of the current signal generated in response to the PPM applied to the sensor during continuous analyte detection. PPM is explained in more detail below, in relation to Figure 2B.

[0013] A reference sensor refers to a sensor used to generate primary data points and PPM currents (e.g., primary and PPM currents measured for the purpose of determining predictive calculation formulas, such as connection functions for determining analyte concentrations, which are subsequently stored in a continuous analyte monitoring (CAM) device and used during continuous analyte detection) in response to a reference glucose concentration, such as a reading from a blood glucose meter (BGM).

[0014] Similarly, a reference sensor data point refers to a reference sensor reading at a time that precisely corresponds to the time of the sensor signal during continuous operation. For example, a reference sensor data point may be obtained directly as the concentration of a reference analyte solution prepared and validated by a weighing scale with a reference sensor / instrument, such as a YSI glucose analyzer (YSI Incorporated (Yellow Springs, Ohio)), Contour NEXT One (Ascensia Diabetes Care US, Inc. (Parsippany, New Jersey)), and / or similar, in which case an in vitro test, including a linearity test, is performed by exposing a continuous analyte sensor to the reference solution. In another example, a reference sensor data point may be obtained from a reference sensor reading in a periodic in vivo measurement of the target analyte through venous blood sampling or finger puncture sampling.

[0015] Unified calibration refers to a calibration mode in which a single calibration sensitivity, or one subset of several calibration sensitivities, is always applied to all sensors. Under unified calibration, field finger puncture calibration or calibration using sensor codes can be minimized or may no longer be necessary.

[0016] For sensors deployed in non-whole blood environments with relatively constant temperatures, such as those used in continuous in vivo detection operations, sensor errors can be related to the sensor's short-term and long-term sensitivity, as well as subsequent calibration methods. Several problems / challenges are associated with such continuous detection operations, including (1) long commissioning (warm-up) times, (2) factory or field calibration, and (3) changes in sensitivity during continuous detection operations. These challenges / challenges appear to relate to sensor sensitivity, as expressed by initial decay (commissioning / warm-up time), changes in sensitivity due to the sensor's sensitivity to the environment during production, and the environment / conditions in which the sensor is subsequently deployed.

[0017] According to one or more embodiments of the present disclosure, the apparatus and method are operable to explore the initial start state of continuous sensor operation with respect to a sample analyte, and thereafter explore the sensor state at any point during the continuous sensing operation of the sensor.

[0018] A method is provided for formulating parameters for a predictive calculation formula (e.g., a connection function) that can be used to accurately determine the analyte concentration continuously from an analyte sensor. Furthermore, a method and apparatus for determining the analyte concentration is provided using a PPM self-sufficient signal (e.g., an operating electrode current resulting from the application of PPM). Such a method and apparatus may enable the determination of the analyte concentration while (1) overcoming the effects of different background interference signals, (2) equalizing or eliminating the effects of different sensor sensitivities, (3) reducing the warm-up time at the start of a (long-term) continuous monitoring process, and / or (4) correcting changes in sensor sensitivity over the continuous monitoring process. These and other embodiments are described below with reference to Figures 1A to 8.

[0019] Typically, in the case of continuous glucose monitoring (CGM) biosensors operating at a constant applied voltage, the current from the mediating substance is measured continuously as a result of enzymatic oxidation of the target analyte, glucose. In practice, despite being called continuous, the current is typically measured or detected every 3 to 15 minutes, or at other regular time intervals. When a CGM sensor is first inserted / implanted by the 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 various reasons. Therefore, it is necessary to detect the operating state of the sensor during its initial period and after the commissioning period to identify any changes in its sensitivity.

[0020] CGM sensor operation begins with an applied voltage E0 after the CGM sensor is inserted / implanted subcutaneously into the user. This applied voltage E0 is typically located at a point on the redox plane of the mediating substance. In the case of a natural oxygen mediating substance with the enzyme glucose oxidase, the redox plane of hydrogen peroxide H2O2 (the oxidation product of the enzymatic reaction) is in the range of approximately 0.5 to 0.8 volts relative to 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 falls within the plane.

[0021] Embodiments described herein utilize PPM as a periodic perturbation of a constant voltage potential applied to the operating 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 operating electrode current is typically sampled every 3 to 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 the continuous sensing operation. In some embodiments, a periodic cycle of probing potential modulation may be used after each primary current measurement, resulting in a group of self-sufficient currents that accompany each primary data point with information regarding the sensor / electrode status and / or conditions.

[0022] PPM may include one or more steps at potentials different from the constant voltage potentials typically used during continuous analyte monitoring. For example, PPM may include a first potential step above or below the constant voltage potential, a first potential step above or below the constant voltage potential followed by a first potential step returning to the constant voltage potential, a series of potential steps above and / or below the 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. An example of a PPM sequence is shown in Figure 2B.

[0023] As described, conventional biosensors used in continuous analyte detection 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, and not to changes in applied potential. That is, transient currents associated with the application of different potentials are avoided or minimized. While this approach simplifies continuous detection operation, the current signal in the data stream resulting from the application of a constant potential to the sensor provides minimal information about the sensor status / condition. In other words, the sensor current signal resulting from the application of a constant potential to the sensor provides little information related to the challenges associated with long-term continuous monitoring of sensors, such as lot-to-lot sensitivity variations, long warm-up times due to initial signal decay, changes in sensor sensitivity over long-term monitoring processes, influences from various background interference signals, and so on.

[0024] Subcutaneously implanted continuous glucose monitoring (CGM) sensors require timely calibration against a reference glucose value. Traditionally, this calibration process involves obtaining readings from a blood glucose meter (BGM) from finger-prick glucose measurements or capillary glucose measurements, and inputting these BGM values ​​into the CGM device to set calibration points for the CGM sensor during the next operating period. Typically, this calibration process involves performing finger-prick glucose measurements daily, or at least once per day, because the sensitivity of the CGM sensor can vary from day to day. This is an inconvenient but necessary step to ensure the accuracy of the CGM sensor system.

[0025] 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 formulating parameters of predictive calculation formulas (e.g., connection functions) that can be used to accurately determine the analyte concentration continuously from the analyte sensor. In some embodiments, an initial glucose value is obtained using a conversion function (e.g., based on an i43 current signal or another PPM current signal), and then a final glucose value is obtained from the initial glucose value using a connection function (e.g., based on a primary current signal and a PPM current signal). Furthermore, methods and systems are provided for determining the analyte concentration using a probing potential modulation (PPM) self-sufficient 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 the warm-up time at the start of a (long-term) continuous monitoring process, (4) compensating for changes in sensor sensitivity over a continuous monitoring process, and / or (5) eliminating the need for field calibration. These and other embodiments are described below with reference to Figures 1A to 8.

[0026] According to one or more embodiments of this disclosure, apparatus and methods are operable to use current sampled from transient conditions during a PPM cycle to determine analyte concentration in continuous analyte monitoring operation. During the PPM cycle, potential modulation is provided to a constant applied voltage to the sensor. Primary data derived from steady-state conditions and / or PPM currents derived from transient conditions may be used as indicators of analyte concentration, and the associated PPM currents and PPM parameters may be used to provide information about the sensor and electrode conditions for error compensation. As described below, continuous monitoring sensors operating using the PPM method actually operate under alternating steady-state (SS) and transient (NSS) conditions. Thus, in some embodiments, there are two concepts described herein. Firstly, the use of current under transient conditions such as i43 (described below) represents a different method for determining analyte concentration in continuous analyte monitoring operation. Secondly, a method of alternating between steady-state (SS) and transient (NSS) conditions for continuous analyte monitoring is another aspect of potential modulation, also disclosed for analyte concentration determination.

[0027] Steady-state conditions: Conventional biosensors used for continuous analyte detection operate under steady-state conditions, which stabilize after a settling time with the continuous monitoring sensor having a constant potential applied to the working electrode (WE). Under these conditions, the current is drawn from a constant flow of incident analyte molecules under steady-state diffusion conditions created by the outer membrane. These conditions are shown in Figure 2A. Under these conditions, theoretically, the boundary structure defined by the enzyme layer and the outer membrane creates a boundary environment along line C 媒介物質 This elicits a constant flux of the measurable species or reduced mediating substance, largely defined by the boundary conditions. If the analyte concentration remains unchanged, the current is proportional to the concentration gradient of the measurable species at the electrode surface, which further depends on the analyte concentration gradient, as defined by the boundary conditions.

[0028] Boundary environment: The boundary conditions in Figure 2A are, theoretically, as follows: the analyte concentration C 外側 is interpreted to be at some value in equilibrium with the membrane concentration C 膜 at the outer interface of the membrane. The low concentration C 膜 inside the membrane indicates that the membrane is designed to reduce the influx of analyte molecules so that the biosensor operates under steady-state conditions. The relationship between C 外側 and C 膜 is generally governed by the equilibrium constant K 外側 = C 膜 / C 外側 < 1. Furthermore, it is governed by a diffusion coefficient D 外側 lower than D 膜 . Together, the permeability P 膜 of the membrane to the analyte = D 膜 * C 膜 defines the analyte throughput. As analyte molecules move towards the enzyme-coated electrode, those molecules are rapidly attenuated to zero by the enzyme. On the other hand, this enzyme converts the analyte molecules into measurable species that can be oxidized at the electrode, such as H2O2 with oxygen as a mediator for glucose oxidase. Once generated, this measurable species will diffuse towards the electrode as well as towards the membrane.

[0029] Under a certain applied voltage that completely oxidizes the measurable species, a certain flux of the measurable species will be drawn towards the electrode. Soon, a steady state is established where the current is proportional to the concentration gradient (dC 媒介物質 / dx) of the measurable species at the electrode surface. Under diffusion-limited conditions (meaning that the oxidation / consumption rate of the measurable species is maximum and is limited only by the diffusion of the measurable species), the concentration gradient C 媒介物質 is predicted to be a straight line defined as zero at the electrode surface, and a point at the membrane interface defined by the equilibrium conditions reached by multiple processes (e.g., the analyte flow rate into the enzyme, the consumption and conversion of the analyte by the enzyme, and the diffusion of the measurable species). The concentration C 媒介物質 to the membrane is gently defined by diffusion. This steady-state condition changes dynamically when the outer analyte concentration changes.

[0030] Under operating conditions dominated by PPM cycles, primary data points are effectively sampled and recorded under steady-state conditions because the boundary environment reverts to a steady state after each transient potential modulation cycle.

[0031] Potential Modulation and Transient Conditions: The effect of potential modulation on the transient behavior of a biosensor is described below with reference to Figures 2B to 2F. Figure 2B illustrates a graph of an example of a probing potential modulation (PPM) sequence according to one or more embodiments of the present disclosure. In Figure 2B, the exemplary PPM sequence has six voltage-potential steps 1 to 6. Other numbers, values, or types of voltage-potential changes may be used. Figure 2C illustrates a graph of transient conditions associated with the electrode and its near-boundary environment between potential steps 2 and 3 in Figure 2B (potential steps E2 and E3 in Figure 2D) according to one or more embodiments of the present disclosure. Figure 2D illustrates the IV curve and graphs of individual potential steps of PPM implemented according to one or more embodiments of the present disclosure. Figure 2E illustrates a graph of the return from transient (NSS) conditions to steady-state (SS) conditions after a PPM cycle according to one or more embodiments of the present disclosure. Figure 2F illustrates a typical output current and labeled graph of the current at each potential step in an exemplary embodiment of a PPM sequence according to one or more embodiments of the present disclosure.

[0032] Referring to Figures 2B and 2D, when the applied potential is modulated away from a constant voltage, such as when the potential steps are 0.55V to 0.6V (step 1 in Figure 2B and E0 to E1 in Figure 2D), but are still within the oxidation flat region of the mediating material (diffusion-limited region on the V axis), there will be several finite currents generated with small decay. This is exp(E 印加 -E 0’ This is an induced current process resulting from an asymmetric flat region dominated by E, where E 印加 This is the applied voltage, and E 0’This is the formal potential of the redox species that represent its electrochemical properties. This finite current, with slight decay, is sometimes referred to as a flat-region degradation current, which has slightly different oxidation states in the flat region. The current-to-voltage relationship of the mediating material is schematically shown in Figure 2D. Examples of such output currents are labeled i11, i12, and i13 in Figure 2F, where i10 is the primary current under steady-state conditions.

[0033] When the applied potential is reversed to a lower voltage, or specifically, reversed from E1 to E2 and then to E3 in Figure 2D (steps 2 and 3 in Figure 2B), two things can happen: (1) the measurable species are no longer completely oxidized at the electrode surface due to the lower potential, and (2) there is a partial reduction of the oxidation form of the measurable species or mediating substance, accompanied by the generation of a negative current. The combined effect of these two events leads to the accumulation of excess measurable species at and near the electrode surface. Consequently, the concentration profile is disturbed from a linear state where it reaches zero at the electrode surface. This state is called a transient state and is shown in Figure 2C, in this case C 媒介物質 The current is not zero at the electrode surface. The output current for such effects is shown as negative and is labeled as i21, i22, i23, and i31, i32, i33 in Figure 2F for steps 2 and 3 in Figure 2B. The negative current suggests that the potential step is partially reduced from high to low. Disturbances in the steady-state conditions affect the film (C 膜 and C 外側 The process occurs near the electrode surface only if it is short, while the internal and external boundary environments of the electrode remain substantially unchanged.

[0034] Alternating NSS and SS conditions: When the potential reverses again in step 4 of Figure 2B (from E3 to E2, as shown in Figure 2D), some of the accumulated measurable species are consumed, in which case oxidation is at a higher rate set by the higher potential E2. Even if E2 is not in the flat region of redox species, this step results in a sudden consumption of measurable species, causing a jump in the current output from the transient concentration and thus providing a strong indication of concentration. Step 5 of Figure 2B (E2 to E1 in Figure 2D) further completes the transient oxidation of excess species, positioning the sensor back at the operating potential on the flat region. Step 6 of Figure 2B undergoes a negative flat region degradation step, returning to the original potential which leads to the restart of the steady state before the next potential modulation cycle. Such a state is shown in Figure 2E and is theoretically the same as in Figure 2A. Thus, as the PPM cycle is repeated, the steady and transient conditions alternate, providing a signal for analyte concentration determination.

[0035] The PPM method described above provides primary data as an indicator of analyte concentration (although PPM currents such as i43 can provide similar information), while the associated PPM currents and PPM parameters are parameters that provide information regarding sensor and electrode condition compensation. All examples of PPM sequences and output current profiles have a high-to-low potential step before reversing to high and returning, and therefore have alternating steady-state and transient conditions.

[0036] One drawback of operating under steady-state conditions for continuous monitoring is that other chemical species that can pass through the film and be oxidized at the electrode surface also affect the overall current at each sampling time. These oxidizing species are not the target analytes, but rather interfering species that affect the overall signal. Therefore, the main concern in continuous analyte detection is the background effect in the sensor's output current. Here, an example is provided to illustrate this background signal effect.

[0037] In Figure 3A, currents are shown from sensors operating by the PPM method and sensors having conventional operation at a constant applied voltage, according to embodiments provided herein. These sensors were tested in vitro with four sets of five glucose solutions, namely 0.2 mg / dL, 0.6 mg / dL, 1.2 mg / dL, and 1.8 mg / dL, where the glucose solution represents four different levels of acetaminophen that constitute the background signal. The 0.2 mg / dL acetaminophen concentration was considered equivalent to a normal level of interference background signal, and 0.6 mg / dL was considered a high level. The 1.2 and 1.8 mg / dL acetaminophen concentrations were considered extremely high levels. The five glucose concentrations were 50, 100, 200, 300, and 450 mg / dL for linearity testing with different background acetaminophen levels.

[0038] The responses of primary data points with respect to glucose concentration from non-PPM (abbreviated as NPPM or NP) and PPM (abbreviated as PP) biased methods are shown in Figures 3B and 3C, respectively. As shown, the effect of different background levels of acetaminophen, as indicated by the intercepts, is virtually the same for both the NPPM and PPM methods. Primary data points from NPPM sensor operation under steady-state conditions show intercept dependence on the level of added acetaminophen, but this result for PPM primary data points with different intercept levels indirectly indicates that primary data points from the PPM method are also from steady-state conditions and are the same as those from the NPPM method.

[0039] In contrast, as shown in Figure 3D, when a transient current such as i43 (the last sampling current from the fourth potential modulation step as shown in Figure 2F) is used to indicate glucose concentration, the intercepts of the four lines at four different levels of acetaminophen are virtually identical. The linearity signal from the NSS current i43 merges the four lines into a single line, spanning a range of nine times the background signal concentration (acetaminophen range of 0.2–0.6, ~1.2, ~1.8 mg / dL). This result of merging the four lines can alternatively be achieved by using a steady-state (SS) current i10 using the PPM method and a predictive calculation formula determined by regression using input from the PPM parameters. Furthermore, in continuous monitoring of analyte concentration by biosensor, alternating steady-state and transient conditions creates a repeating / continuous operating pattern of the analyte signal quantified in each NSS-SS cycle. Thus, interference-free conditions are continuously maintained, providing a better signal basis for analyte concentration determination.

[0040] The advantage of determining analyte concentration using transient signals / parameters is evident in its ability to eliminate background effects on the analyte signal arising from varying levels of oxidizing species in the sample. Therefore, transient determination of analyte concentration represents a different and unique approach to continuous analyte monitoring. Uninterference-free signals from NSS conditions allow for more resources (parameter items) to be allocated to regression for further accuracy.

[0041] Another advantage of the NSS signal for analyte concentration determination is that it substantially reduces the initial decay in the current of continuous monitoring sensors, as shown in Figures 4A and 4B. Figure 4A compares the steady-state current i10 and transient current i43 from a single sensor in an in vitro linearity test. To compare the effect of initial decay, the currents of the i10 and i43 current series for the first 60 minutes are normalized by the first sampled current. Figure 4B shows the normalized currents from SS(N-i10) and NSS(N-i43) currents, as well as the average (Avg-i10, Avg-i43) of these two current groups from seven different CGM sensors. As shown in the figure, the initial decay of the i43 current is much smaller than that of the i10 current. That is, the NSS current is less affected by initial decay than the SS current. On average, in the in vitro test, the SS current decays by 30% in the first 30 minutes, while the NSS current decays by 10%. This small initial decay will result in a short warm-up time for the continuous monitoring sensor.

[0042] Assuming uncertainty regarding a one-to-one correlation between in vitro and in vivo sensitivity, a method for connecting in vitro glucose to in vivo glucose is disclosed herein, by applying a unified "conversion function" to a wide range of sensor response data, and then applying a "connection function" or unified calibration to reduce glucose errors to a narrow range of variation. This unified conversion function is based on the raw or "initial" glucose value G 生 Calculate f(signal), where the signal is a measured current signal (or a parameter derived from one or more measured signals), and f can be a linear or nonlinear function. If the transformation function f is nonlinear, the sensitivity or response gradient does not apply (as described below).

[0043] In its simplest form, the unified conversion function may 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 could be the glucose signal (e.g., Iw-Ib, i43, or another PPM current signal), the gradient, and the reference glucose G 基準 A linear relationship like the following is possible between them: Signal = Gradient * G 基準 As a result, the following was found: G 基準 = Signal / Gradient Here, the gradient is the compound gradient (gradient 複合 This represents the unified composite gradient, which is described below. The above relationship then shows that in CGM, initial or raw glucose G 生 This can be used to perform calculations as follows: G 生 = Signal / Gradient 複合

[0044] As described above, PPM current signals are less sensitive to interference effects and may exhibit lower warm-up sensitivity. Therefore, in some embodiments provided herein, a unified composite gradient can be determined from a PPM current signal, such as i43 or another preferred PPM current signal. For example, Figure 4C shows i43 current versus reference glucose in an in vitro linearity test using 10 different sensors according to embodiments provided herein. Each sensor has 3 to 6 linearity tests at 50, 100, 200, 300, and 450 mg / dL glucose over a 15-day long-term test. From this data, a transformation function can be developed, for example, using linear regression. Fitting linear regression to the data in Figure 4C yields i43 = 0.0801 * Gref + 12.713. Based on this, the transformation function G_raw = (i43 - 12) / 0.0805 is obtained using the relationship i43 = 0.0805 * Gref + 12. Other relationships may be used. Note that an equivalent form of Iw-Ib in the primary data (i10) may be used. However, background subtraction is not used in this example because i43 is relatively unaffected by interference from other interfering species.

[0045] In some embodiments, instead of using a linear transformation function, a nonlinear transformation function such as a polynomial may be used (for example, to better fit various sensor responses).

[0046] In the example above, the unified composite gradient is 0.0805. This composite gradient is pre-selected from the perspective of the center of the data population, as shown in Figure 4C, but may also be related to the subdivision of the entire response population for each sensor manufacturing specification. 生 A unified composite gradient for calculating the % bias values ​​will be more scattered when there is no one-to-one corresponding gradient for calculating glucose for each sensor, and when there are no individual gradients for subsequent responses during 15 days of monitoring. However, the connection function is individual error (% bias = 100% * ΔG / G = 100% * (G 生 -G 基準 ) / G 基準 When applied to obtain a narrow range of glucose variation, a single transformation makes the in vitro connection to in vivo a simple problem without calibration. This connection function is ΔG / G 生 Based on the value, it is derived from the PPM parameter. G 生 By narrowing the error range from the data in this way, the connection function is referred to as a connection function that makes an in vitro to in vivo connection without calibration, meaning that all sensor responses are accommodated within a narrow error range.

[0047] A connection function is said to be a broad connection from in vitro glucose to in vivo glucose if it provides predicted in vivo glucose values ​​within a narrow range of error without calibration. In this context, the aim is not to establish a one-to-one correspondence between in vitro sensitivity and in vivo sensitivity. Conversely, the connection function will provide glucose values ​​from a sensor within its sensitivity range, as long as the sensor responds to glucose. That response can be linear or nonlinear.

[0048] By utilizing the extensive information about the CGM sensor from the PPM current, this function can be derived from the PPM current and related parameters. Each response data point in a periodic cycle is transformed by a composite transformation function to obtain the glucose value G 生 When converted, the error, or the % bias ΔG / G associated with that error, 生 =( G 生 -G 基準 ) / G 基準 G exists. 接続 =G 基準 By setting G 接続 =G 生 / (1+ΔG / G 生 )=G 生 / (1 + connection function), where the connection function = ΔG / G 生 = f(PPM parameter). One way to derive the connection function is the relative error ΔG / G 生 This is achieved by setting this as the target input parameter from multivariate regression and PPM parameters.

[0049] Additional PPM parameters may include normalized PPM currents ni11=i11 / i10, ni12=i12 / i10, ..., ni63=i63 / i10, relative differences d11=(i11-i12) / i10, d12=(i12-i13) / i10, d21=(i21-i22) / i10, d22=(i22-i23) / i10, ..., d61=(i61-i62) / i10, and d62=(i62-i63) / i10, average currents for each PPM potential step av1=(i11+i12+i13) / 3, av2=(i21+i22+i23) / 3, ..., and their ratios av12=av1 / av2, etc.

[0050] In summary, in some embodiments, i43 receives the raw current signal from the raw or initial glucose value G 生 It can be used as part of a conversion function to convert to G. For example, G 生 This can be calculated as follows: G 生 =(i43-12.0) / 0.0805 G 生Other relationships between i43 (or other PPM current signals) may be used.

[0051] G 生 Once this is known, the connection function is the compensated or final glucose signal or concentration, G 複合 It can be used to calculate the following. For example, the connection function takes the SS signal (i10) and the NSS signal (PPM signal) as input parameters, and the relative error ΔG / G 生 The target of multivariate regression can be used and derived from in vitro data. An exemplary connection function CF is provided below. It will be understood that other numbers and / or types of terms may be used. CF=24.53135+0.510036*ni53-9.90634*R53+7.22965*z43-5.602442*y51+0.049372*GR1+0.143765*GR3-4.875524*R6 1R53-19.98925*R65R52-8.59255*R51R32+0.348577*R54R41-0.497589*R54R42-0.08465*GR61R53+0.013702*GR63R52- 0.0270023*GR64R41-0.115267*GR51R52+0.018377*GR51R43-0.019587*GR54R43…-0.0339635*Gy61y65-0.123701*Gy6 1y52+0.129388*Gy61y42+0.079116*Gy63y42+0.054673*Gy63y31-0.03599*Gy65y32-0.001983*Gy51y43-0.0494*Gy31y 32+59.1546*R61z32+18.9493*R65z53-22.5024*R65z54+78.2594*R65z42+7.022692*R53z41+10.90881*R53z42-8.280 324*R41z42+0.070284*GR65z53+0.077797*GR51z42…-0.022664*Gz61y52+0.048962*Gz63y54+0.015388*Gz63y43-0.02 5835*Gz64y32-0.002533*Gz51y43+0.004559*Gz53y32+0.00254*Gz54y43-0.000884*Gz41y43-1.17164*d61-0.006599 *Gd32+0.005669*Gd41+6.849786*d11d31-0.939887*d21d51-0.072769*d31d42+0.162176*d32d61-3.714043*d42d51….

[0052] The input parameters of the connection function CF may be of the following types, for example:

[0053] Probing currents: These are the probing potential-modulated currents i11, i12, i13, ..., i61, i62, i63, where the first digit (x) in the ixy format indicates the potential step, and the second digit (y) indicates which current measurement was performed after the application of the potential step (e.g., the first, second, or third measurement).

[0054] R parameters: These ratios are calculated by dividing the termination 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.

[0055] X-type parameters: The general format for parameters of this type is given by dividing the termination ppm current of a later potential step by the termination ppm current of the previous potential step. For example, parameter x61 is i 6 3 / i 1 Determined by 3, i63 is the final ppm current of step 6 in the three recorded currents for each step, and i13 is the final ppm current of 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.

[0056] Y-type parameters: The general format for parameters of this type is given by dividing the final ppm current of a later potential step by the initial ppm current of the previous potential step. For example, parameter y61 is i 6 3 / i1 1This is determined by i63, where i63 is the final ppm current of step 6 in the three recorded currents for each step, and i11 is the initial ppm current of step 1. 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.

[0057] Z-type parameters: The general format for this type of parameter is given by dividing the first ppm current of a later potential step by the final ppm current of the previous potential step. For example, parameter z61 is determined by i61 / i13, where i61 is the first ppm current of step 6 out of three recorded currents per step, and i13 is the final ppm current of step 1. 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.

[0058] Additional terms include normalized currents: ni11=i11 / i10, ni12=i12 / i10, ...; relative differences: d11=(i11-i12) / i10, d12=(i12-i13) / i10, ...; average currents for each PPM potential step: av1=(i11+i12+i13) / 3, av2=(i21+i22+i23) / 3, ...; and average current ratios: av12=av1 / av2, av23=av2 / av3, .... Other terms include GR1=G 生 *R1, Gz61=G 生 *z61, Gy52=G 生*Includes y52, ..., R63R51=R63 / R51, R64R43=R64 / R43, ..., z64z42=z64 / z42, z65z43=z65 / z43, ..., d11d31=d11 / d31, d12d32=d12 / d32, ..., Gz61y52=G*z61 / y52....

[0059] Other types of parameters may also be used, such as ppm current difference or relative difference, or ratios of intermediate ppm currents, which carry equivalent or similar information.

[0060] Therefore, the NSS current i43 can be used to indicate the raw glucose analyte concentration, and the connection function can be used with the raw glucose analyte concentration from i43 to connect from in vitro to in vivo glucose. 生 A conversion function to G 複合 The results of compensation using the connection function are summarized in Table 1, which shows that both the SS and NSS signals converge equally to a narrow error range of the final analyte concentration. These results indicate that i43 can be used as the analyte indicated by the signal, and that the connection function can converge a wide range of scattered responses to a narrow range of glucose values. [Table 1]

[0061] In one embodiment, the connection function is G 接続 =G 生 Provided by / (1 + connection function), where the connection function = f(PPM parameter) is derived by multivariate regression, and as a result, the gradient 複合 Errors derived from composite transformation functions such as are reduced / minimized to generate glucose values ​​within a narrow range of error variation. In another embodiment, the connection function is G 基準 By setting this as a regression target with multivariate regression from PPM input parameters, it becomes simply a predictive calculation formula.

[0062] 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 to 5 minutes) to allow sufficient time for the constant voltage application to the working electrode to resume for steady-state conditions before the next primary data point is recorded. In some embodiments, the PPM cycle may be about 1 to 90 seconds, or at most 50% of a regular 180-second primary data cycle.

[0063] In one or more embodiments, a PPM cycle may be about 10 to 40 seconds and / or may include two or more modulated potential steps around the redox flat region of the mediating substance. In some embodiments, the PPM sequence may be about 10 to 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 of the primary data cycle allows the steady-state conditions to be restarted with a constant applied voltage. With respect to the potential steps of the PPM cycle, the continuous time is transient, and as a result, the boundary conditions of the measurable species created by these potential steps are transient. Thus, each potential step may be about 1 to 15 seconds in some embodiments, about 3 to 10 seconds in other embodiments, and about 4 to 6 seconds in yet another embodiment.

[0064] In some embodiments, probing potential modulation can be stepped into the potential domain under non-diffusion-limited redox conditions, or into the dynamic domain of the mediating material (meaning the output current depends on the applied voltage, with higher applied voltages producing higher output currents from the electrodes). For example, E2 and E3 in Figure 2D (steps 2 and 3 in Figure 2B) are two potential steps in the dynamic domain of the mediating material, generating transient output currents from the electrodes. When the potential steps are reversed, applied voltages of the same magnitude, E2 and E1, are restarted to probe transient output currents from the electrodes.

[0065] Different embodiments may be used in conjunction with transient conditions. For example, transient conditions may also be probed in one step, which proceeds directly to a target potential E2 and returns to a starting potential E1, followed by a second probing potential step, which proceeds directly to a different potential E3 in the dynamical region with different transient conditions, and then returns directly to the starting potential E1. The intention is to modulate the applied potential to create an alternation of steady and transient conditions for the measurable species at the electrode surface, thereby allowing the signal from the transient state to be used to determine the analyte concentration.

[0066] Figure 5A illustrates a high-level block diagram of an exemplary CGM device 500 according to embodiments provided herein. Although not shown in Figure 5A, it should be understood that various electronic components and / or circuits are configured to be coupled to a power source, not limited to a battery. The CGM device 500 includes a bias circuit 502, which may be configured to be coupled to a CGM sensor 504. The bias circuit 502 may be configured to apply a bias voltage, such as a continuous DC bias, to the analyte-containing fluid through the CGM sensor 504. In this exemplary embodiment, the analyte-containing fluid may be human interstitial fluid, and the bias voltage may be applied to one or more electrodes 505 of the CGM sensor 504 (e.g., an operating electrode, a background electrode, etc.).

[0067] The bias circuit 502 may also be configured to apply a PPM sequence to the CGM sensor 504, as shown in Figure 2B or another PPM sequence. For example, the PPM sequence may be applied during the initial and / or intermediate phases, or for each primary data point. The PPM sequence may be applied, for example, before, after, or before and after the measurement of a primary data point.

[0068] In some embodiments, the CGM sensor 504 may include two electrodes, and a bias voltage and probing potential modulation may be applied between the pair of electrodes. In such cases, the current may be measured through the CGM sensor 504. In other embodiments, the CGM sensor 504 may include three electrodes, such as an operating electrode, a counter electrode, and a reference electrode. In such cases, a bias voltage and probing potential modulation may be applied between the operating electrode and the reference electrode, and the current may be measured, for example, through the operating electrode. The CGM sensor 504 contains a chemical that reacts with the glucose-containing solution in a reduction-oxidation reaction, affecting the concentration of the charge carrier and the time-dependent impedance of the CGM sensor 504. Exemplary chemicals include glucose oxidase, glucose dehydrogenase, or similar. In some embodiments, mediators such as ferricyanide or ferrocene may be used.

[0069] The continuous bias voltage generated and / or applied by the bias circuit 502 may be in the range of approximately 0.1 to 1 volt relative to the reference electrode. Other bias voltages may be used. Exemplary PPM values ​​have been previously described.

[0070] PPM current, as well as non-PPM (NPPM) current passing through the CGM sensor 504 in the analyte-containing fluid, which responds to PPM and a constant bias voltage, is measured from the CGM sensor 504 (I 測定 The current can be carried to circuit 506 (also referred to as the current sensing circuit). The current sensing circuit 506 may be configured to detect and / or record a current sensing signal having a magnitude indicating the magnitude of the current transmitted from the CGM sensor 504 (for example, using a suitable current-voltage converter (CVC)). In some embodiments, the current sensing circuit 506 may include a resistor having a known nominal value and a known nominal precision (for example, in some embodiments, 0.1% to 5%, or even less than 0.1%) through which the current transmitted from the CGM sensor 504 passes. The voltage generated across the resistor in the current sensing circuit 506 represents the magnitude of the current and is transmitted as a current sensing signal (or raw glucose signal). Raw ) can be called.

[0071] In some embodiments, the sample circuit 508 may be coupled to the current measurement circuit 506 and configured to sample the current measurement signal. The sample circuit 508 may generate digitized time-domain sample data representing the current measurement signal (e.g., a digitized glucose signal). For example, the sample circuit 508 may be any suitable A / D converter circuit configured to receive the current measurement signal, which is an analog signal, and convert it into a digital signal having a desired number of bits as its output. The number of bits output by the sample circuit 508 may be 16 bits in some embodiments, but more or fewer bits may be used in other embodiments. In some embodiments, the sample circuit 508 may sample the current measurement signal at a sampling rate in the range of about 10 samples per second to 1000 samples per second. Faster or slower sampling rates may be used. For example, downsampling may be performed using sampling rates such as about 10 kHz to 100 kHz to further reduce the signal-to-noise ratio. Any suitable sampling circuit may be used.

[0072] Referring further to Figure 5A, the processor 510 may be coupled to the sample circuit 508 and further coupled to the memory 512. In some embodiments, the processor 510 and the sample circuit 508 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 510 and the sample circuit 508 may be via the memory 512. In this arrangement, the sample circuit 508 writes digital data to the memory 512, and the processor 510 reads digital data from the memory 512.

[0073] Memory 512 may store one or more predictive calculation formulas 514, such as one or more connection functions, for use in determining glucose values ​​based on primary data points (NPPM currents) and PPM currents (from current measurement circuits 506 and / or sample circuits 508). For example, in some embodiments, two or more predictive calculation formulas may be stored in memory 512 for use with different segments (time periods) of CGM acquired data. In some embodiments, memory 512 may include a predictive calculation formula (e.g., a connection function) based on a primary current signal generated by applying a constant voltage potential applied to a reference sensor, and multiple PPM current signals generated by applying a PPM sequence applied during primary current signal measurement.

[0074] Additionally or alternatively, memory 512 may store a calibration index calculated based on the PPM current for use during in-situ calibration, as described above.

[0075] Memory 512 may also store multiple instructions within it. In various embodiments, the processor 510 may be, but is not limited to, a computing resource such as a microprocessor, microcontroller, embedded microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA) configured to operate as a microcontroller, or similar.

[0076] In some embodiments, a plurality of instructions stored in memory 512 may include instructions that, when executed by the processor 510, cause the processor 510 to (a) cause the CGM device 500 to measure current signals (e.g., primary current signals and PPM current signals) from interstitial fluid (via bias circuit 502, CGM sensor 504, current measurement circuit 506, and / or sample circuit 508); (b) store the current signals in memory 512; (c) calculate predictive formula (e.g., conversion function and / or connection function) parameters such as the ratio (and / or other relationship) of currents from different pulses, voltage steps, or other voltage changes in the PPM sequence; (d) use the calculated predictive formula (e.g., conversion function and / or connection function) parameters to calculate glucose values ​​(e.g., concentration) using the predictive formula (e.g., conversion function and / or connection function); and / or (e) communicate the glucose values ​​to the user.

[0077] Memory 512 may be any preferred type of memory, including, but is not limited to, one or more of volatile memory and / or non-volatile memory. Volatile memory may include, but is not limited to, static random-access memory (SRAM) or dynamic random-access memory (DRAM). Non-volatile memory may include, but is not limited to, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., EEPROM of the type in either a NOR or NAND configuration, and / or in either a stacked or planar arrangement, and / or in any arrangement of single-level cell (SLC), multi-level cell (MLC), or a combination of SLC / MLC), resistive memory, filamentary memory, metal oxide memory, phase-change memory (e.g., chalcogenide memory), or magnetic memory. Memory 512 may be packaged, for example, as a single chip or as multiple chips. In some embodiments, memory 512 may be embedded in an integrated circuit, such as an application-specific integrated circuit (ASIC), together with one or more other circuits.

[0078] As described above, memory 512 may have a plurality of instructions stored in it that, when executed by processor 510, cause processor 510 to perform various operations specified by one or more of the stored instructions. Memory 512 may further have portions reserved for one or more "scratchpad" storage areas that can be used for read or write operations by processor 510 in response to the execution of one or more of the plurality of instructions.

[0079] In the embodiment shown in Figure 5A, the bias circuit 502, CGM sensor 504, current measurement circuit 506, sample circuit 508, processor 510, and memory 512 including the predictive calculation formula 514 may be disposed within the wearable sensor portion 516 of the CGM device 500. In some embodiments, the wearable sensor portion 516 may include a display 517 for displaying information such as glucose concentration information (e.g., without using external equipment). The display 517 may be any preferred type of human-aware display, including but not limited to liquid crystal displays (LCDs), light-emitting diode (LED) displays, or organic light-emitting diode (OLED) displays.

[0080] Referring further to Figure 5A, the CGM device 500 may further include a portable user device portion 518. A processor 520 and a display 522 may be disposed within the portable user device portion 518. The display 522 may be coupled to the processor 520. The processor 520 may control text or images displayed by the display 522. The wearable sensor portion 516 and the portable user device portion 518 may be communicatively coupled. In some embodiments, the communicative coupling of the wearable sensor portion 516 and the portable user device portion 518 may be by wireless communication via transmitter and / or receiver circuits, such as a transmit / receive circuit TxRx524a of the wearable sensor portion 516 and a transmit / receive circuit TxRx524b of the portable user device 518. Such wireless communication may be by any preferred means, including but not limited to standards-based communication protocols such as the Bluetooth® communication protocol. In various embodiments, wireless communication between the wearable sensor portion 516 and the portable user device portion 518 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 516 and the portable user device portion 518 may be connected by one or more wires.

[0081] The display 522 may be any suitable type of human-aware display, including but not limited to liquid crystal displays (LCDs), light-emitting diode (LED) displays, or organic light-emitting diode (OLED) displays.

[0082] Referring here to Figure 5B, an exemplary CGM device 550 is illustrated, which is similar to the embodiment illustrated in Figure 5A but has a different division of components. In the CGM device 550, the wearable sensor portion 516 includes a bias circuit 502 coupled to the CGM sensor 504 and a current measuring circuit 506 coupled to the CGM sensor 504. The portable user device portion 518 of the CGM device 550 includes a sample circuit 508 coupled to the processor 520 and a display 522 coupled to the processor 520. The processor 520 is further coupled to a memory 512, which may have a predictive calculation formula 514 stored in that memory. In some embodiments, the processor 520 in the CGM device 550 may also perform functions described further, for example, those performed by the processor 510 of the CGM device 500 in Figure 5A. The wearable sensor portion 516 of the CGM device 550 is smaller, lighter, and therefore less invasive than the CGM device 500 in Figure 5A, because it does not contain the sample circuit 508, processor 510, memory 512, etc. Other component configurations may be used. For example, in a modification of the CGM device 550 in Figure 5B, the sample circuit 508 may still remain on the wearable sensor portion 516 (so that the portable user device 518 receives the digitized glucose signal from the wearable sensor portion 516).

[0083] Figure 6 is a schematic side view of an exemplary glucose sensor 504 according to embodiments provided herein. In some embodiments, the glucose sensor 504 may include a working electrode 602, a reference electrode 604, a counter electrode 606, and a background electrode 608. 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 the charge carrier and the time-dependent impedance of the CGM sensor 504). 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 602 include glucose oxidase, glucose dehydrogenase, or similar. The enzyme component may be immobilized on the electrode surface by a crosslinking agent, such as glutaraldehyde. An outer film layer may be applied over the enzyme layer to protect the overall internal components, including the electrode and the enzyme layer. In some embodiments, mediators such as ferricyanide or ferrocene may be used. Other chemical catalysts and / or mediators may be used.

[0084] In some embodiments, the reference electrode 604 may be formed from Ag / AgCl. The counter electrode 606 and / or background electrode 608 may be formed from a suitable conductor such as platinum, gold, palladium, or similar. Other materials may be used for the reference electrode, counter electrode, and / or background electrode. In some embodiments, the background electrode 608 may be identical to the working electrode 602 but without a chemical catalyst and / or mediating substance. The counter electrode 606 may be separated from the other electrodes by a separation layer 610 (e.g., polyimide or another suitable material).

[0085] Figure 7 illustrates an exemplary method 700 for determining glucose values ​​during continuous glucose monitoring (CGM) measurement according to embodiments provided herein. In some embodiments, in block 702, method 700 includes providing a CGM device (e.g., CGM device 500) including a sensor, memory, and a processor. In block 704, method 700 includes applying a constant voltage potential (e.g., about 0.55 volts, or another preferred voltage) to the sensor. In block 706, method 700 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in memory. In block 708, method 700 includes applying a probing potential modulation sequence to the sensor (e.g., as shown in Figure 2B or another preferred PPM sequence). In block 710, 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. Method 700 further involves determining the conversion function value in block 712 based on the measured probing potential modulated current signal (e.g., i43 or another PPM current signal), and in block 714, determining the conversion function value (e.g., G 生 In block 716, the initial glucose concentration is determined based on the following: the connection function value is determined based on the primary current signal and multiple probing potential modulated current signals; and in block 718, the final glucose concentration (e.g., G) is determined based on the initial glucose concentration and the connection function value. 複合 This includes determining ) and .

[0086] Figure 8 illustrates another exemplary method 800 for determining glucose values ​​during continuous glucose monitoring (CGM) measurement according to embodiments provided herein. In some embodiments, in block 802, method 800 includes providing a CGM device including a sensor, memory, and a processor. In block 804, method 800 includes applying a constant voltage potential to the sensor. In block 806, method 800 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in memory. In block 808, method 800 includes applying a probing potential modulation sequence to the sensor. In block 810, method 800 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. In block 812, method 800 includes determining an initial glucose concentration based on a conversion function and the measured probing potential modulation current signal. In block 814, method 800 includes determining a connection function value based on a primary current signal and a plurality of probing potential-modulated current signals. In block 816, method 800 includes determining a final glucose concentration based on an initial glucose concentration and a connection function value.

[0087] Some embodiments or parts thereof may be provided as computer program products or software that include a machine-readable medium internally storing non-temporary instructions, which may be used to program a computer system, controller, or other electronic device according to one or more embodiments.

[0088] This disclosure is susceptible to various modifications and alternative forms, but specific embodiments of methods and apparatus are shown in the drawings as examples and described in detail herein. However, it should be understood that the specific methods and apparatus disclosed herein are not intended to limit the disclosure or the claims.

Claims

1. A method for determining glucose levels during continuous glucose monitoring (CGM), The CGM device includes sensors, memory, and a processor, Applying a constant voltage potential to the aforementioned sensor, The primary current signal generated from the constant voltage potential is measured, and the measured primary current signal is stored in the memory. Applying a probing potential modulation sequence to the sensor, wherein applying the probing potential modulation sequence includes sequentially applying a plurality of voltage potential steps. Measuring multiple probing potential modulated current signals generated from each of the multiple voltage potential steps in the probing potential modulation sequence, and storing the multiple measured probing potential modulated current signals in the memory, and here measuring the multiple probing potential modulated current signals is, Measure the first probing potential modulation (PPM) current at the start of each of the plurality of voltage potential steps, and This includes measuring a termination probing potential modulation (PPM) current at the end of each of the plurality of voltage potential steps, so that a plurality of first PPM currents and a plurality of termination PPM currents are measured for the plurality of voltage potential steps. The initial glucose concentration is determined based on the conversion function and one of the multiple measured probing potential-modulated current signals. The connection function is used to determine the connection function value based on the measured primary current signal and the plurality of measured probing potential-modulated current signals, wherein the connection function is derived from a plurality of input parameters, each of which input parameters includes at least the ratio of each of the plurality of termination PPM currents to one of the plurality of first PPM currents, each ratio calculated by dividing the termination PPM current by the first PPM current within one of the plurality of voltage potential steps, and A method comprising determining the final glucose concentration based on the initial glucose concentration and the connection function value.

2. The plurality of voltage potential steps in the probing potential modulation sequence include a first voltage potential step greater than the constant voltage potential and a second voltage potential step less than the constant voltage potential. The method according to claim 1, wherein applying the probing potential modulation sequence further includes applying a third voltage potential step smaller than the second voltage potential step, and a fourth voltage potential step larger than the third voltage potential step.

3. The method according to claim 2, wherein determining the initial glucose concentration based on the conversion function and one of a plurality of measured probing potential modulated current signals includes determining the initial glucose concentration based on the conversion function and the termination PPM current measured during the fourth voltage potential step.

4. The method according to claim 2, wherein the termination PPM current measured during the fourth voltage potential step is the final probing potential modulated current signal measured during the fourth voltage potential step.

5. The method according to claim 1, wherein the primary current signal and the probing potential modulated current signal are operating electrode current signals.

6. The method according to claim 1, wherein the primary current signal is measured during a primary data cycle, and the primary data cycle occurs in a time range of every 3 to 15 minutes.

7. The method according to claim 6, wherein the application of the probing potential modulation sequence occurs within the time range of half of the primary data cycle.

8. A continuous glucose monitoring (CGM) device, This is the wearable part, A sensor configured to generate an electrical signal from interstitial fluid, Processor and The memory coupled to the aforementioned processor, The wearable portion includes a transmitter circuit coupled to the processor, The memory of the CGM device includes a connection function based on the measurement of a primary current signal generated by applying a constant voltage potential to a reference sensor, and the measurement of a plurality of probing potential modulated current signals generated by applying a probing potential modulation sequence applied between primary current signal measurements. The application of the probing potential modulation sequence includes applying a plurality of voltage potential steps in sequence, and The memory includes computer program code stored in the memory, and when the computer program code is executed by the processor, the CGM device, Using the sensor and memory of the wearable portion, the primary current signal is measured and stored. Measure and store the plurality of probing potential modulated current signals associated with the primary current signal, The plurality of probing potential modulation current signals include a first probing potential modulation (PPM) current measured at the start of each of the plurality of voltage potential steps, and a termination probing potential modulation (PPM) current measured at the end of each of the plurality of voltage potential steps, and as a result, a plurality of first PPM currents and a plurality of termination PPM currents are measured for the plurality of voltage potential steps. The initial glucose concentration is determined based on the conversion function and one of the multiple measured probing potential-modulated current signals. The connection function is used to determine the value of the connection function based on the primary current signal and the plurality of measured probing potential-modulated current signals, wherein the connection function is derived from a plurality of input parameters, each of which input parameters includes at least the ratio of each of the plurality of termination PPM currents to one of the plurality of first PPM currents, each ratio being calculated by dividing the termination PPM current by the first PPM current within one of the plurality of voltage potential steps, and A continuous glucose monitoring (CGM) device that determines the final glucose concentration based on the initial glucose concentration and the connection function value.

9. The wearable portion is configured to apply the probing potential modulation sequence, The CGM device according to claim 8, wherein the plurality of voltage potential steps in the probing potential modulation sequence include a first voltage potential step greater than the constant voltage potential, a second voltage potential step less than the constant voltage potential, a third voltage potential step less than the second voltage potential step, and a fourth voltage potential step greater than the third voltage potential step.

10. The CGM device according to claim 9, wherein when the computer program code is executed by the processor, the CGM device determines the initial glucose concentration based on the termination PPM current measured during the fourth voltage potential step.

11. The CGM device according to claim 10, wherein the termination PPM current measured during the fourth voltage potential step is the final probing potential modulated current signal measured during the fourth voltage potential step.

12. The CGM device according to claim 8, wherein the primary current signal and the probing potential modulated current signal are operating electrode current signals.

13. The wearable part is A current detection circuit is coupled to the aforementioned sensor and configured to measure the current signal generated by the aforementioned sensor. The CGM device according to claim 8, further comprising a sampling circuit coupled to the current sensing circuit and configured to generate a digitized current signal from the measured current signal.

14. The CGM device according to claim 8, further comprising a portable user device, wherein the portable user device includes a receiver circuit and a display, and the transmitter circuit of the wearable portion is configured to transmit glucose values ​​to the receiver circuit of the portable user device for presentation to the user of the CGM device.

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