Non-steady state determination of analyte concentration for continuous glucose monitoring by potential modulation

Abstract

A method of 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 probe potential modulation sequence to the sensor; measuring a probe potential modulation current signal resulting from 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 conversion function and the measured probe potential modulation current signal; 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. Other aspects are disclosed.

Description

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 061,135, filed August 4, 2020, and titled “CONTINUOUS ANALYTE MONITORING SENSOR CALIBRATION AND MEASUREMENTS BY A CONNECTION FUNCTION,” U.S. Provisional Patent Application No. 63 / 061,152, filed August 4, 2020, and titled “NON-STEADY-STATE DETERMINATION OF ANALYTE CONCENTRATION FOR CONTINUOUS GLUCOSE MONITORING BY POTENTIAL MODULATION,” U.S. Provisional Patent Application No. 63 / 061,157, filed August 4, 2020, and titled “EXTRACTING PARAMETERS FOR ANALYTE CONCENTRATION DETERMINATION,” and U.S. Provisional Patent Application No. 63 / 061,167, filed August 4, 2020, and titled “BIOSENSOR WITH MEMBRANE STRUCTURE FOR STEADY-STATE AND NON-STEADY-STATE CONDITIONS FOR DETERMINING ANALYTE CONCENTRATIONS,” each of which is hereby incorporated by reference in its entirety for all purposes. TECHNICAL FIELD

[0002] The present application relates generally to continuous sensor monitoring of analytes in bodily fluids, and more specifically, to continuous glucose monitoring (CGM). BACKGROUND

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

[0004] Accordingly, there is a need for improved apparatuses and methods for determining glucose values using CGM sensors. SUMMARY

[0005] In some embodiments, a method of 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 the memory; applying a probe potential modulation sequence to the sensor; measuring a probe potential modulation current signal resulting from 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 conversion function and the measured probe potential modulation current signal; 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.

[0006] In some embodiments, a continuous glucose monitoring (CGM) device includes a wearable portion having a sensor configured to generate a current signal from interstitial fluid, a processor, a memory coupled to the processor, and transmitter circuitry coupled to the processor. The memory includes a connection function based on a primary current signal generated by applying a constant voltage potential applied to a reference sensor and a plurality of probe potential modulation current signals generated by applying a probe potential modulation sequence applied between primary current signal measurements. The memory includes computer program code stored therein, which when executed by the processor, causes the CGM device to measure and store a primary current signal using the sensor of the wearable portion and the memory, measure and store a plurality of probe potential modulation current signals associated with the primary current signal, determine an initial glucose concentration based on a conversion function and the measured probe potential modulation current signals, determine a connection function value based on the primary current signal and a plurality of the probe 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 of determining glucose values 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 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 probe potential modulation current signals generated by the probe potential modulation sequence and storing the measured probe potential modulation current signals in the memory, determining a conversion function value based on the measured probe potential modulation current signals, determining an initial glucose concentration based on the conversion function value, 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 the present disclosure can become apparent from the detailed description and drawings of multiple example embodiments and implementations, including the best mode contemplated for carrying out the present disclosure. The present disclosure can also be capable of other and different embodiments, and its several details can be modified in various respects, all without departing from the scope of the present disclosure. For example, although the description below relates to continuous glucose monitoring, the devices, systems, and methods described below can be readily adapted for monitoring other analytes, such as cholesterol, lactate, uric acid, alcohol, etc., in other continuous analyte monitoring systems. BRIEF DESCRIPTION OF DRAWINGS

[0009] The drawings described below are for illustrative purposes and are not necessarily drawn to scale. Accordingly, the drawings and the specification are to be regarded as illustrative in nature, and not as restrictive. The drawings are not intended to limit the scope of the application in any way.

[0010] Figure 1A A plot of applied voltage E0versus time for a continuous glucose monitoring (CGM) sensor is shown in accordance with one or more embodiments of the present disclosure.

[0011] Figure 1B A plot of current curves for a probe potential modulation (PPM) sequence for a CGM sensor is shown in accordance with one or more embodiments of the present disclosure. Figure 1A

[0012] Figure 2A A plot of steady state conditions occurring at the electrode and its nearby boundary environment is shown in accordance with one or more embodiments of the present disclosure.

[0013] Figure 2B A plot of an example of a probe potential modulation (PPM) sequence is shown in accordance with one or more embodiments of the present disclosure.

[0014] Figure 2C A plot of non-steady state conditions occurring at the electrode and its nearby boundary environment during E2and E3potential steps is shown in accordance with one or more embodiments of the present disclosure.

[0015] Figure 2D A plot of I-V curves for a PPM sequence and individual potential steps implemented in accordance with one or more embodiments of the present disclosure.

[0016] Figure 2E A plot of return from non-steady state (NSS) conditions to steady state (SS) conditions after a PPM cycle is shown in accordance with one or more embodiments of the present disclosure.

[0017] Figure 2F A plot of typical output current in the current implementation of a PPM sequence and labeling of the current in each potential step is shown in accordance with one or more embodiments of the present disclosure.

[0018] Figure 3A A plot of time current curves for primary data points in a linear test of four levels of acetaminophen using a PPM method and a non-PPM (NPPM) method is shown in accordance with one or more embodiments of the present disclosure.

[0019] Figure 3B ​A plot showing the primary current response under PPM applied voltage at four levels of acetaminophen in a linear test using the PPM method according to one or more embodiments of the present disclosure.

[0020] Figure 3C A plot showing the primary current response under PPM applied voltage at glucose in the same test according to one or more embodiments of the present disclosure.

[0021] Figure 3D A plot showing the i43 current response line under PPM applied voltage at four levels of acetaminophen versus the PPM current i43 response to glucose in a linear test using the PPM method according to one or more embodiments of the present disclosure.

[0022] Figure 4A A plot showing the initial current curves for SS current i10 and NSS current i43 in a linear test using the PPM method according to one or more embodiments of the present disclosure.

[0023] Figure 4B A plot showing the individual normalized SS current i10 and normalized NSS current i43 from 7 different sensors and the average current for both sets over the first 60 minutes according to one or more embodiments of the present disclosure.

[0024] Figure 4C A plot showing the i43 current versus reference glucose for an in vitro linear test using 10 different sensors according to one or more embodiments provided herein.

[0025] Figure 5A A high-level block diagram of an example CGM device according to one or more embodiments of the present disclosure is shown.

[0026] Figure 5B A high-level block diagram of another example CGM device according to one or more embodiments of the present disclosure is shown.

[0027] Figure 6 A side view schematic of an example glucose sensor according to one or more embodiments of the present disclosure.

[0028] Figure 7 An example method of determining glucose values during continuous glucose monitoring (CGM) measurements according to embodiments provided herein is shown.

[0029] Figure 8 Another example method of determining glucose values during CGM measurements according to embodiments provided herein is shown. DETAILED DESCRIPTION

[0030] Embodiments described herein include systems and methods for applying probe potential modulation (PPM) on top of otherwise constant voltage applied to an analyte sensor. The terms "voltage," "potential," and "voltage potential" are used interchangeably herein. "Current," "signal," and "current signal" are also used interchangeably herein, as are "continuous analyte monitoring" and "continuous analyte sensing." As used herein, PPM refers to intentionally varying an otherwise constant voltage potential applied to a sensor periodically during continuous analyte sensing, such as applying a probe potential step, pulse, or other potential modulation to the sensor. The use of PPM during continuous analyte sensing can be referred to as a PP or PPM method, while performing continuous analyte sensing without PPM can be referred to as an NP or NPPM method.

[0031] Primary data points or primary currents refer to measurements of current signals generated in response to analyte at a constant voltage potential applied to a sensor during continuous analyte sensing. For example, Figure 1A A graph of applied voltage E0versus time for a continuous glucose monitoring (CGM) sensor is shown in accordance with one or more embodiments of the present disclosure. Example times at which measurements of primary data points can be taken and at which subsequent PPMs can be applied are shown. As Figure 1A As shown, in this example, the constant voltage potential E0applied to the working electrode of an analyte sensor can be about 0.55 volts. Other voltage potentials can be used. Figure 1A An example of a typical cycle of primary data points obtained at a constant applied voltage is shown. Primary data points are data points measured or sampled at a constant applied voltage and at regular intervals (such as 3-15 minutes) during continuous glucose monitoring and used to calculate glucose values for a user. For example, a primary data point can be a working electrode current measured for an analyte sensor during continuous analyte monitoring. Figure 1A The primary data points are not shown, but the times and voltages at which each primary data point is measured are shown. For example, Figure 1A The square 102 in FIG. 1 represents the time / voltage (3 minutes / 0.55 volts) at which a first primary data point (e.g., a first working electrode current) is measured for a sensor biased at voltage E0. Likewise, Figure 1A The square 104 in FIG. 1 represents the time / voltage (6 minutes / 0.55 volts) at which a second primary data point (e.g., a second working electrode current) is measured for a sensor biased at voltage E0.

[0032] PPM currents refer to measurements of current signals generated in response to PPM applied to a sensor during continuous analyte sensing. PPM currents are described in more detail below in connection with Figure 2B PPM is described in more detail.

[0033] Reference sensor refers to a sensor that is used to generate primary data points and PPM currents in response to a reference glucose concentration, e.g., as represented by a blood glucose meter (BGM) reading (e.g., primary and PPM currents measured for the purpose of determining a prediction equation, such as a linking function that is subsequently stored in a continuous analyte monitoring (CAM) device and used to determine analyte concentration during continuous analyte sensing).

[0034] Likewise, reference sensor data points refer to reference sensor readings at times that closely correspond in time to signals from sensors in continuous operation. For example, reference sensor data points can be obtained directly as concentrations of reference analyte solutions prepared gravimetrically and verified by reference sensors / instruments such as YSI glucose analyzers (from YSI Incorporated of Yellow Springs, Ohio), Contour NEXT One (from Ascensia Diabetes Care US, Inc. of Parsippany, New Jersey), and / or similar instruments, where in vitro studies of linearity are performed by exposing continuous analyte sensors to reference solutions. In another example, reference sensor data points can be obtained from reference sensor readings at periodic in vivo measurements of the target analyte by venous blood draws or finger stick sampling.

[0035] Uniform calibration refers to a calibration mode in which only one calibration sensitivity or one of several subsets of calibration sensitivities is applied to all sensors at all times. Under uniform calibration, in situ finger stick calibration or calibration with sensor codes can be minimized or no longer needed.

[0036] For sensors deployed in non-whole blood environments with relatively constant temperature, such as sensors used in continuous in vivo sensing operations, sensor errors can be related to short- and long-term sensitivities of the sensor and subsequent calibration methods. There are several challenges / issues associated with such continuous sensing operations: (1) long break-in (warm-up) time; (2) factory or in-field calibration; and (3) changes in sensitivity during continuous sensing operations. These issues / challenges appear to be related to sensor sensitivity represented in initial decay (break-in / warm-up time), changes in sensitivity due to sensor sensitivity to the environment while sensor production is ongoing, and the environment / conditions in which the sensor is subsequently deployed.

[0037] According to one or more embodiments of the present disclosure, devices and methods are operable to detect initial starting conditions for continuous sensor operation of a sample analyte and to detect sensor conditions at any point thereafter during continuous sensing operation of the sensor.

[0038] Methods are provided for developing parameters for a predictive equation (e.g., a connection function) that can be used to accurately determine analyte concentration from an analyte sensor in continuous. Further, methods and devices are provided for utilizing a PPM self-provided signal (e.g., a working electrode current generated from the application of a PPM) to determine analyte concentration. Such methods and devices can allow for 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 beginning of a (long-term) continuous monitoring session, and / or (4) correcting for sensor sensitivity changes during a continuous monitoring session. Reference is made to the following detailed description of specific embodiments of the application, illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout. Figure 1A-8 These and other embodiments are described.

[0039] For continuous glucose monitoring (CGM) biosensors that are typically operated at a constant applied voltage, current from the mediator is continuously measured due to enzyme oxidation of the target analyte glucose. In practice, the current is typically measured or sensed every 3 to 15 minutes, or another regular interval, although referred to as continuous. When a CGM sensor is first inserted / implanted into a user, there is an initial break-in time, which can last from 30 minutes to several hours. Once the CGM sensor is broken in, its sensitivity can still change for various reasons. Therefore, there is a need to sense the operating conditions of the sensor during its initial period and after break-in time to identify any changes in its sensitivity.

[0040] After a CGM sensor is inserted / implanted subcutaneously into a user, the operation of the sensor starts from the applied voltage E0. The applied voltage E0 is typically at a point on the redox plateau of the mediator. For a native mediator of oxygen with glucose oxidase enzyme, in a medium of about 100-150 mM chloride concentration, the oxidation plateau of hydrogen peroxide H2O2 (oxidation product of the enzyme reaction) is in the range of about 0.5 to 0.8 volts relative to an Ag / AgCl reference electrode. The operating potential of a glucose sensor can be set at 0.55-0.7 volts, which is within the plateau region.

[0041] The embodiments described herein employ PPM as a periodic perturbation of the otherwise constant voltage potential applied to the working electrode of a subcutaneous biosensor in a continuous sensing operation (e.g., for monitoring a biological sample analyte, such as glucose). During a continuous sensing operation, such as continuous glucose monitoring, the sensor working electrode current is typically sampled every 3-15 minutes (or at some other frequency) for glucose value determinations. These current measurements represent primary current and / or primary data points for analyte determinations during the continuous sensing operation. In some embodiments, a periodic cycle of probe potential modulation can be employed following each primary current measurement, such that a set of self- provided currents accompanies each primary data point with information about the sensor / electrode state and / or condition.

[0042] The PPM can include one or more potential steps that are different from the constant voltage potential typically used during continuous analyte monitoring. For example, the PPM can include a first potential step that is higher or lower than the constant voltage potential, a first potential step that is higher or lower than the constant voltage potential and then returns to the constant voltage potential, a series of potential steps that are higher and / or lower than the constant voltage potential, a voltage step, a voltage pulse, a pulse of the same or different duration, a square wave, a sine wave, a triangle wave, or any other potential modulation. An example of a PPM sequence is shown in Figure 2B

[0043] As described, conventional biosensors for continuous analyte sensing operate by applying a constant potential to the working electrode (WE) of the sensor. In this condition, the current from the WE is periodically recorded (e.g., every 3-15 minutes or at some other time interval). In this way, the current generated by the biosensor is solely due to changes in analyte concentration, and not changes in the applied potential. That is, non-steady state currents associated with applying different potentials are avoided or minimized. While this approach simplifies the continuous sensing operation, the current signal in the data stream from applying a constant potential to the sensor provides minimal information about the sensor state / condition. That is, the sensor current signal from applying a constant potential to the sensor provides little information about issues associated with long-term continuous monitoring of the sensor, such as batch-to-batch variability in sensitivity, long warm-up times due to initial signal decay, changes in sensor sensitivity over the course of long-term monitoring, effects of changing background interference signals, etc.

[0044] ​Such continuous glucose monitoring (CGM) sensors implanted subcutaneously require calibration against reference glucose values in a timely manner. Conventionally, the calibration process involves taking a blood glucose meter (BGM) reading, or capillary glucose value, from a finger stick glucose measurement, and entering the BGM value into the CGM device to set a calibration point for the CGM sensor for the next operating period. Typically, this calibration process occurs once a day, or at least one finger stick glucose measurement is taken each day, as the sensitivity of the CGM sensor can be changing each day. This is an inconvenient but necessary step to ensure the accuracy of the CGM sensor system.

[0045] Embodiments described herein include systems and methods for applying PPM on top of otherwise constant voltage applied to an analyte sensor. Methods are provided for parameterizing a predictive equation (e.g., a connection function) that can be used to continuously and accurately determine analyte concentration from an analyte sensor. In some embodiments, a conversion function (e.g., based on an i43 current signal or another PPM current signal) is employed to obtain an initial glucose value, and then a connection function (e.g., based on a primary current signal and a PPM current signal) is employed to obtain a final glucose value from the initial glucose value. Further, methods and systems are provided for determining analyte concentration by using probe potential modulation (PPM) self-referencing. 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 beginning of a (long-term) continuous monitoring session, (4) correcting for sensor sensitivity changes during a continuous monitoring session, and / or (5) eliminating the need for in-field calibration. Reference is made to the following figures and detailed description. Figure 1A-8 These and other embodiments are described.

[0046] According to one or more embodiments of the present disclosure, devices and methods are operable to determine analyte concentration in a continuous analyte monitoring operation using current sampled from non-steady state conditions during a PPM cycle. During the PPM cycle, a potential modulation is provided to an otherwise constant applied voltage of a sensor. Primary data derived from steady state conditions and / or PPM current derived from non-steady state conditions can be used as an indicator of analyte concentration, and the related PPM current and PPM parameters can be used to provide information about sensor and electrode conditions for error compensation. As will be described below, a continuous monitoring sensor operating using a PPM method is in fact operating under alternating steady state (SS) conditions and non-steady state (NSS) conditions. Thus, in some embodiments, two concepts are described herein. First, the use of current under non-steady state conditions, as represented by i43 (described below), represents a different method for determining analyte concentration in a continuous analyte monitoring operation. Second, a method alternating between steady state (SS) conditions and non-steady state (NSS) conditions for continuous analyte monitoring is another aspect of the potential modulation disclosed for analyte concentration determination.

[0047] Steady state condition: Conventional biosensors used in continuous analyte sensing operate under steady state conditions that are established when the continuous monitoring sensor stabilizes after a stabilization time with a constant potential applied to the working electrode (WE). Under this condition, the current is drawn from a constant flow of entering analyte molecules under steady state diffusion conditions produced by the outer membrane. This condition is depicted in Figure 2A Under this condition, a boundary environment is theoretically created by the boundary structure as defined by the enzyme layer and the outer membrane to draw a constant flow of measurable substance or reduced mediator, approximately defined by the straight line C med When the analyte concentration does not change, 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.

[0048] Boundary environment: Figure 2A The boundary conditions of 外 the membrane concentration C 膜 at the outer interface of the membrane is in equilibrium at some value C 膜 The lower concentration C 外 in the interior of 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 膜 and C 外 is approximately defined by the equilibrium constant K 膜 = C 外 <1 control. It is further controlled by the lower diffusion coefficient D 外 compared to D 膜 The membrane permeability P膜 = D 膜 * C 膜 Together define the flux of analyte. As analyte molecules move toward the electrode covered with enzyme, they are rapidly attenuated to zero by the enzyme. At the same time, the enzyme converts the analyte molecules into a measurable substance that can be oxidized at the electrode, such as H2O2, where oxygen acts as a mediator relative to glucose oxidase. Once generated, the measurable substance will diffuse toward the electrode and toward the membrane.

[0049] At a constant applied voltage that fully oxidizes the measurable substance, there will be a constant flux of the measurable substance drawn toward the electrode. Soon, a steady state is established in which the current is proportional to the concentration gradient of the measurable substance at the electrode surface (dC med / dx). Under diffusion-limited conditions (meaning that the rate of oxidation / consumption of the measurable substance is at a maximum, limited only by diffusion of the measurable substance), the concentration gradient C med is projected as a straight line, defined as zero at the electrode surface and projected as a point at the membrane interface, defined by the equilibrium conditions reached by multiple processes (e.g., analyte flux into the enzyme, consumption and conversion of the analyte by the enzyme, and diffusion of the measurable substance). The concentration C med into the membrane is roughly defined by diffusion. This steady state condition is dynamically changed with changes in the external analyte concentration.

[0050] Under operating conditions controlled by PPM cycling, the primary data points are actually sampled and recorded under steady state conditions, as the boundary environment returns to steady state conditions after each non-steady state potential modulation cycle.

[0051] Potential modulation and non-steady state conditions: Reference is made below to Figure 2B-2F The effect of potential modulation on the non-steady state behavior of a biosensor is described. Figure 2B A graph showing an example of a probe potential modulation (PPM) sequence according to one or more embodiments of the present disclosure is shown. In Figure 2B , the example PPM sequence has six voltage potential steps 1-6. Other numbers, values, or types of voltage potential changes can be used. Figure 2C A graph showing the non-steady state conditions that occur at the electrode and its nearby boundary environment during potential steps 2 and 3 Figure 2B of the PPM sequence of Figure 2D potential steps E2 and E3) according to one or more embodiments of the present disclosure is shown. Figure 2D A graph showing the I-V curve and individual potential steps of a PPM sequence implemented according to one or more embodiments of the present disclosure is shown. Figure 2E A graph showing the return from non-steady state (NSS) conditions to steady state (SS) conditions after a PPM cycle according to one or more embodiments of the present disclosure is shown. Figure 2FA graph showing a typical output current and the current markings at each potential step in an example implementation of a PPM sequence according to one or more embodiments of the present disclosure.

[0052] refer to Figure 2B and 2D If the applied potential is modulated away from a constant voltage, such as a potential step from 0.55V to 0.6V ( Figure 2B Step 1 and Figure 2D Within the E0 to E1 range, but still within the oxidation plateau of the mediator (the diffusion-restricted region on the V-axis), a finite current with slight decay will be generated. This is due to the fact that exp(E... app –E 0 The asymmetric plateau period controlled by ') is still a Faraday process, where E app It is the applied voltage, and E 0 ' is the redox potential representing the electrochemical properties of a substance. This finite current with a small decay can be called the plateau degenerate current, at which a slightly different oxidation state is present. The current-voltage relationship of the dielectric is approximately described as follows: Figure 2D Examples of this type of output current are in... Figure 2F They are shown and labeled as i11, i12 and i13, while i10 is the primary current under steady-state conditions.

[0053] If the applied potential is reversed to a lower voltage, or specifically from E1 to E2 and further to... Figure 2D E3 in Figure 2B In the step transitions 2 and 3, 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 2C As shown, where C med It is not zero at the electrode surface. For Figure 2B For steps 2 and 3, the output current of this type of effect is shown as negative, and... Figure 2F 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 It remains essentially unchanged.

[0054] Alternating between NSS and SS conditions: when the potential isFigure 2B When the step 4 reverses again (from E3 to E2, as...) Figure 2D 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 if 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 2B Step 5 in Figure 2D The E2 to E1 phases further complete the unsteady oxidation of the excess material to reposition the sensor at the operating potential in the plateau region. Figure 2B The step 6 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 2E In theory, the conditions are similar to Figure 2A The conditions are the same. Therefore, when the PPM cycle is repeated, steady-state and non-steady-state conditions alternate, thus providing a signal for analyte concentration determination.

[0055] 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 current and PPM parameters are parameters that provide information about sensor and electrode condition compensation. Examples of PPM sequences and output current curves both exhibit a potential step from high to low and then back to high, and thus represent alternation between steady-state and unsteady-state conditions.

[0056] One drawback of operating under steady-state conditions for continuous monitoring is that other oxidizable chemicals that can penetrate the membrane and are present at the electrode surface also contribute to the total current at each sampling time. These oxidizable substances are not the target analytes and are therefore interfering factors that contribute to the overall signal. Therefore, a primary concern in continuous analyte sensing is the background effect in the sensor's output current. Examples of this background signal effect are provided here.

[0057] exist Figure 3AIn particular, according to the embodiments provided herein, current from sensors operating with PPM method and sensors operating conventionally with constant applied voltage are shown. These sensors were tested in vitro in four sets of five glucose solutions with glucose solutions at four different levels of acetaminophen representing background signal: 0.2 mg / dL, 0.6 mg / dL, 1.2 mg / dL, and 1.8 mg / dL. The 0.2 mg / dL acetaminophen concentration is considered to be equivalent to normal levels of interfering background signal, while 0.6 mg / dL is considered to be a high level. The 1.2 and 1.8 mg / dL acetaminophen concentrations are considered to be extremely high levels. For the linear study with different background acetaminophen, the five glucose concentrations were 50, 100, 200, 300, and 450 mg / dL, respectively.

[0058] Figure 3B and 3C The glucose concentration response for primary data points from the no PPM (for brevity, NPPM or NP) and PPM (for brevity, PP) bias methods are shown, respectively. As shown, the effect of different background levels of acetaminophen on the NPPM and PPM methods, as indicated by the intercepts, is practically the same. While the primary data points from NPPM sensor operation show dependence of the intercept on the added acetaminophen level under steady-state conditions, this result for PPM primary data points with different intercept levels indirectly shows that the primary data points from the PPM method also come from steady-state conditions, the same as the NPPM method.

[0059] On the other hand, when using non-steady-state current, such as i43 (the last sampled current from the fourth potential modulation step as shown in Figure 2F , to indicate glucose concentration, the intercepts of the four lines at the four different acetaminophen levels are practically the same, as shown in Figure 3D . The linear signal of the NSS current i43 collapses from four lines spanning a 9-fold background signal concentration range (in the range of 0.2 to 0.6 to 1.2 to 1.8 mg / dL acetaminophen) into one line. Alternatively, this result of collapsing the four lines can be achieved by employing the steady-state (SS) current i10 with the PPM method and using a prediction equation determined from regression of inputs from PPM parameters. Moreover, in continuous monitoring of analyte concentration by a biosensor, the alternation of steady-state and non-steady-state conditions produces a repeating / continuous mode of operation of the analyte signal to be quantified in each NSS-SS cycle. Thus, the interference-free condition can be sustained continuously, providing a better signal basis for analyte concentration determination.

[0060] The advantages of non-steady state signal / parameter determination of analyte concentration are evident in eliminating the background effects on analyte signal from different levels of oxidizable species in the sample. Thus, the method of non-steady state determination of analyte concentration represents a different and unique approach to continuous analyte monitoring. The interference-free signal from NSS conditions will put more resources (parameter terms) in the regression to further improve accuracy.

[0061] Another advantage of NSS signals for analyte concentration determination is that the initial decay of the current of the continuous monitoring sensor is significantly reduced, as shown in Figure 4A and 4B Figure 4A The steady state current i10and non-steady state current i43from a single sensor from in vitro linear tests were compared. To compare the effect of initial decay, the i10and i43current series were normalized by the current at the first sample for the first 60 minutes. Figure 4B The normalized currents from SS (i10) and NSS (i43) currents, as well as the average of these two sets of currents from seven different CGM sensors (Avg-i10, Avg-i43) are shown. As shown, the initial decay of the i43current is much smaller than that of the i10current. That is, the NSS current is less susceptible to initial decay than the SS current. On average, the SS current dropped 30% in the first 30 minutes in the in vitro test, while the NSS current dropped only 10%. This small initial decay will translate into a short warm-up time for the continuous monitoring sensor.

[0062] In view of the uncertainty in establishing a one-to-one correlation between in vitro and in vivo sensitivity, a method is disclosed herein for establishing the link from in vitro to in vivo glucose by applying a unified "conversion function" to the data of a wide range of sensor responses, followed by applying a "linking function" or a unified calibration method to reduce the glucose error to a narrow band. The unified conversion function calculates the raw or "initial" glucose value G 原始 = f (signal), where signal is the measured current signal (or a parameter derived from one or more measured current signals), and f can be a linear or non-linear function. When the conversion function f is non-linear, then the sensitivity or response slope (as described below) is not applied.

[0063] In its simplest form, the unified conversion function can be a linear relationship between the measured current signal and the reference glucose level obtained from in vitro test data. For example, the unified conversion function can be a linear relationship between the glucose signal (e.g., Iw-Ib, i43or another PPM current signal), slope, and reference glucose G ref

[0064] signal = slope * G ref ​​

[0065] such that

[0066] G ref = signal / slope

[0067] where the slope represents the composite slope (slope 复合 ), also referred to as the unified composite slope, as described below. The above relationship can then be used to calculate the initial or raw glucose G 原始 :

[0068] G 原始 = signal / slope 复合

[0069] As described above, the PPM current signal can be less sensitive to interference effects and exhibit lower warm-up sensitivity. To this end, in some embodiments provided herein, the unified composite slope can be determined from the PPM current signal, such as i43 or another suitable PPM current signal. For example, Figure 4C shows i43 current versus reference glucose for in vitro linearity testing using 10 different sensors according to embodiments provided herein. Each sensor was subjected to 3-6 linearity tests at 50, 100, 200, 300, 450 mg / dL glucose over a 15-day long-term study. From this data, a conversion function can be developed using linear regression, for example. The data in Figure 4C was subjected to a linear regression fit to yield i43 = 0.0801 * Gref + 12.713. Based on this, a relationship of i43 = 0.0805 * Gref + 12 was employed to yield the conversion function G_raw = (i43 - 12) / 0.0805. Other relationships can be used. It should be noted that an equivalent form of Iw - Ib for the primary data (i10) can be used. However, since i43 is relatively immune to interference effects from other interferents, background subtraction was not used in this example.

[0070] In some embodiments, a non-linear conversion function, such as a polynomial, can be employed instead of a linear conversion function (e.g., to better fit the varying response of the sensor).

[0071] In the above example, the unified composite slope in this example is.0805. This composite slope is pre-selected from the perspective of the center of the population of data, as shown in Figure 4C but it can also be related to a subdivision of the overall response population according to manufacturing specifications of the sensor. The G 原始The unity composite slope makes the bias % values more spread out because there is no one-to-one corresponding slope to calculate glucose for each sensor and no individual slope for subsequent responses during the 15-day monitoring period. However, if a connection function is applied to the individual errors (bias % = 100%*AG / G = 100%*(G 原始 – G ref ) / G ref ) to obtain a narrow band of glucose, a single conversion makes the connection from in vitro to in vivo simple without the need for calibration. This connection function is derived from the PPM parameters based on the AG / G 原始 values. By such a way of narrowing the error band from G 原始 , the connection function is referred to as a connection function that establishes the connection from in vitro to in vivo without calibration, which means adapting all responses of the sensor to a narrow error band.

[0072] When the connection function provides predicted in vivo glucose values to a narrow error band without calibration, the connection function is referred to as a wide-range connection from in vitro glucose to in vivo glucose. In this context, it is not sought to establish a one-to-one correspondence of in vitro sensitivity and in vivo sensitivity. Rather, the connection function will provide glucose values from the sensor within the sensitivity range as long as the sensor is responsive to glucose. The response can be linear or non-linear.

[0073] With the rich information about the CGM sensor from the PPM current, this function is derived from the PPM current and related parameters. When each response data point in the periodic cycle is converted to a glucose value G 原始 , there is an error or bias % AG / G 原始 = (G 原始 – G ref ) / G ref associated with it. By setting G 连接 = G ref , then G 连接 = G 原始 / (1 + AG / G 原始 ) = G 原始 / (1 + connection function), where connection function = AG / G 原始 = f(PPM parameters). One way to derive the connection function is by setting the relative error AG / G 原始 as the target of a multivariate regression and the input parameters from the PPM parameters.

[0074] Additional PPM parameters can include normalized PPM currents nill = i11 / i10, n112 = i12 / i10,..., n163 = 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 current for each PPM potential step av1 = (i11 + i12 + i13) / 3, av2 = (i21 + i22 + i23) / 3,..., and ratios thereof av12 = av1 / av2, etc.

[0075] In summary, in some embodiments, the i43 current can be used as part of a conversion function to convert the raw current signal to a raw or initial glucose value G 原始 . For example, G 原始 may be calculated as:

[0076] G 原始 = (i43 - 12.0) / 0.0805

[0077] Other relationships between G 原始 and i43 (or other PPM current signals) can be used.

[0078] Once G 原始 is known, a linking function can be employed to calculate a compensated or final glucose signal or concentration G comp . For example, a SS signal (i10) and a NSS signal (PPM signal) can be used as input parameters, and a relative error AG / G 原始 can be used as a target for a multivariate regression to derive a linking function from in vitro data. An example linking function CF is provided below. It should be understood that other quantities and / or types of terms can be used.

[0079] CF = 24.53135 + 0.510036 * n153 - 9.90634 * R53 + 7.22965 * z43 - 5.602442 * y51 + 0.049372 * GR1 + 0.143765 * GR3 - 4.875524 * R61R53 - 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 * Gy61y52 + 0.129388 * Gy61y42 + 0.079116 * Gy63y42 + 0.054673 * Gy63y31 - 0.03599 * Gy65y32 - 0.001983 * Gy51y43 - 0.0494 * Gy31y32 + 59.1546 * R61z32 + 18.9493 * R65z53 - 22.5024 * R65z54 + 78.2594 * R65z42 + 7.022692 * R53z41 + 10.90881 * R53z42 - 8.280324 * R41z42 + 0.070284 * GR65z53 + 0.077797 * GR51z42... - 0.022664 * Gz61y52 + 0.048962 * Gz63y54 + 0.015388 * Gz63y43 - 0.025835 * 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

[0080] For example, the input parameters of the connection function CF can be of the following types.

[0081] Probe currents: probe potential modulation currents i11, i12, i13,..., i61, i62, i63, where the first number (x) in the format ix y indicates the potential step, and the second number (y) indicates the current measurement (e.g., first, second, or third measurement) taken after the potential step is applied.

[0082] R parameters: These ratios are calculated by dividing the end ppm current of one potential step by the first ppm current of the previous potential step. For example, R1 = i13 / i11, R2 = i23 / i21, R3 = i33 / i31, R4 = i43 / i41, R5 = i53 / i51, and R6 = i63 / i61.

[0083] X parameters: The general format for this type of parameter is given by the end ppm current of the later potential step divided by the end ppm current of the previous potential step. For example, parameter x61 is determined by i63 / i13, where i63 is the end ppm current of step 6 of the recorded three currents for each step and i13 is the end 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.

[0084] Y parameters: The general format for this type of parameter is given by the end ppm current of the later potential step divided by the first ppm current of the previous potential step. For example, parameter y61 is determined by i63 / i11, where i63 is the end ppm current of step 6 of the recorded three 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.

[0085] Z-type parameters: The general format for this type of parameter is given by the first ppm current of the last potential step divided by the end 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 of the three recorded currents for each step, and i13 is the end 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.

[0086] Additional terms include normalized currents: nii i / iio, nii2 i / iio...; relative differences: di i = (i i - i2) / iio, di2 = (i2 - i3) / iio...; average currents for each PPM potential step: av i = (i i + i2 + i3) / 3, av2 = (i2i + i23) / 3,...; and average current ratios av12 = av i / av2, av23 = av2 / av3... Other miscellaneous terms include GRi = G 原始 *Ri, G z61 = G 原始 *z61, Gy52 = G 原始 *y52..., R63 R51 = R63 / R51, R64 R43 = R64 / R43..., z64 z42 = z64 / z42, z65 z43 = z65 / z43..., di i d31 = di i / d31, di2 d32 = di2 / d32..., G z61 y52 = G*z61 / y52...etc.

[0087] Other types of parameters can also be used, such as ppm current differences or relative differences or ratios of intermediate ppm currents that carry equivalent or similar information.

[0088] Thus, the NSS current i43 can be used to indicate the raw glucose analyte concentration, and a connection function can be used with the raw glucose analyte concentration from i43 to connect in vitro glucose to in vivo glucose. The conversion functions to G 原始 and the connection function to G compThe results of the compensation show that both the SS signal and the NSS signal converge equivalently to a narrow error band of the final analyte concentration. The results show that i43 can be used as an analyte indicating signal and a wide range of responses can be converged to a narrow glucose value band by a linking function.

[0089] Table 1: G of i10, i43 for in vitro data sets 原始 and G comp Summary

[0090]

[0091] In one embodiment, the linking function is provided by G 连接 = G 原始 / (1 + linking function), where the linking function = f(PPM parameters) is derived from a multiple regression such that the error from the composite conversion function, such as slope 复合 , is reduced / minimized to produce a glucose value within a narrow error band. In another embodiment, the linking function is simply a prediction equation by setting G Ref as the regression target, utilizing a multiple regression of PPM input parameters.

[0092] 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 the constant voltage to be applied to the working electrode to recover steady state conditions before the next primary data point is recorded. In some embodiments, the PPM cycle can be about 1 to 90 seconds, or no more than 50% of the regular 180 second primary data cycle.

[0093] In one or more embodiments, the PPM cycle can be about 10-40 seconds, and / or contain more than one modulation potential step around the oxidation-reduction plateau of the mediator. In some embodiments, the PPM sequence can be about 10-20% of the regular primary data point cycle. For example, when the regular primary data point cycle is 180 seconds (3 minutes), a PPM cycle of 36 seconds is 20% of the primary data point cycle. The remaining time of the primary data cycle allows for steady state conditions to recover under the constant applied voltage. For the potential steps in the PPM cycle, the duration is of a transient nature such that the boundary conditions of the measurable species resulting from these potential steps are non-steady state. Thus, in some embodiments, each potential step can be about 1-15 seconds, in other embodiments about 3-10 seconds, and in yet other embodiments about 4-6 seconds.

[0094] In some embodiments, the probing potential modulation can step into the potential region of non-diffusion-limited redox conditions, or into the kinetic region of mediator (meaning that 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 2D E2 and E3 of FIG. 2 and FIG. 3 of the prior art are two potential steps in the kinetic region of mediator generating non-steady state output current from the electrode. At the potential step reversal, the same magnitude of applied voltage E2 and E1 is restored to probe the non-steady state output current from the electrode. Figure 2B

[0095] Different embodiments with non-steady state conditions can be employed. For example, the non-steady state conditions can also be probed directly to the target potential E2 and back to the starting potential E1 in one step, followed by a second probing potential step directly to a different potential E3 in the kinetic region with different non-steady state conditions, and then directly back to the starting potential E1. The purpose is to modulate the applied potential to generate an alternation of steady state and non-steady state conditions for the measurable species at the electrode surface, whereby the signal from the non-steady state can be used to determine the analyte concentration.

[0096] Figure 5A A high-level block diagram of an example CGM device 500 in accordance with embodiments provided herein is shown. Although not shown in FIG. 5, it should be understood that various electronic components and / or circuitry are configured to be coupled to a power source, such as but not limited to a battery. The CGM device 500 includes a biasing circuit 502 that can be configured to be coupled to a CGM sensor 504. The biasing circuit 502 can be configured to apply a bias voltage, such as a continuous DC bias, to an analyte-containing fluid through the CGM sensor 504. In this example embodiment, the analyte-containing fluid can be human interstitial fluid, and the bias voltage can be applied to one or more electrodes 505 (e.g., a working electrode, a background electrode, etc.) of the CGM sensor 504. Figure 5A The biasing circuit 502 can also be configured to apply a PPM sequence, such as the PPM sequence shown in FIG. 6, or another PPM sequence, to the CGM sensor 504. For example, the PPM sequence can be applied initially and / or at intermediate time periods, or to each primary data point. For example, the PPM sequence can be applied before, after, or before and after measuring a primary data point.

[0097] Figure 2B The biasing circuit 502 can also be configured to apply a PPM sequence, such as the PPM sequence shown in FIG. 6, or another PPM sequence, to the CGM sensor 504. For example, the PPM sequence can be applied initially and / or at intermediate time periods, or to each primary data point. For example, the PPM sequence can be applied before, after, or before and after measuring a primary data point.

[0098] ​​In some embodiments, CGM sensor 504 can include two electrodes, and a bias voltage and a probe potential modulation can be applied across the pair of electrodes. In such cases, a current through CGM sensor 504 can be measured. In other embodiments, CGM sensor 504 can include three electrodes, such as a working electrode, a counter electrode, and a reference electrode. In such cases, for example, a bias voltage and a probe potential modulation can be applied between the working electrode and the reference electrode, and a current through the working electrode can be measured. CGM sensor 504 includes a chemical that reacts with a glucose-containing solution in a reduction-oxidation reaction, which affects the concentration of charge carriers and the time-dependent impedance of CGM sensor 504. Example chemicals include glucose oxidase, glucose dehydrogenase, and the like. In some embodiments, a mediator such as ferricyanide or ferrocene can be employed.

[0099] For example, a continuous bias voltage generated and / or applied by bias circuit 502 can be in the range of about 0.1 to 1 volt with respect to a reference electrode. Other bias voltages can be used. Example PPM values were described previously.

[0100] In response to the PPM and the constant bias voltage, a PPM current and a non-PPM (NPPM) current through CGM sensor 504 in an analyte-containing fluid can be communicated from CGM sensor 504 to current measurement (I 测量 ) circuit 506 (also referred to as current sense circuitry). Current measurement circuit 506 can be configured to sense and / or record a current measurement signal having a magnitude indicative of the amount of current communicated from CGM sensor 504 (e.g., using a suitable current-to-voltage converter (CVC)). In some embodiments, current measurement circuit 506 can 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 communicated from CGM sensor 504 passes. The voltage developed across the resistor of current measurement circuit 506 represents the magnitude of the current, and can be referred to as a current measurement signal (or raw glucose signal signal 原始 ).

[0101] In some embodiments, a sampling circuit 508 can be coupled to the current measurement circuit 506 and can be configured to sample the current measurement signal. The sampling circuit 508 can produce digitized time domain sampled data representative of the current measurement signal (e.g., a digitized glucose signal). For example, the sampling circuit 508 can be any suitable A / D converter circuit configured to receive the current measurement signal as an analog signal and convert it to a digital signal having a desired number of bits as an output. In some embodiments, the number of bits output by the sampling circuit 508 can be sixteen, although more or fewer bits can be used in other embodiments. In some embodiments, the sampling circuit 508 can 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 can be used. For example, a sampling rate of about 10 kHz to 100 kHz can be used and down-sampled to further reduce the signal-to-noise ratio. Any suitable sampling circuitry can be employed.

[0102] Still referring to Figure 5A The processor 510 can be coupled to the sampling circuit 508 and can be further coupled to the memory 512. In some embodiments, the processor 510 and the sampling circuit 508 are configured to communicate directly with each other over a wired pathway (e.g., over a serial or parallel connection). In other embodiments, the coupling of the processor 510 and the sampling circuit 508 can be achieved through the memory 512. In this embodiment, the sampling circuit 508 writes digital data to the memory 512 and the processor 510 reads the digital data from the memory 512.

[0103] The memory 512 can have stored therein one or more prediction equations 514, such as one or more connection functions, for determining glucose values based on primary data points (NPPM current) and PPM currents (from the current measurement circuit 506 and / or the sampling circuit 508). For example, in some embodiments, two or more prediction equations can be stored in the memory 512, each equation for a different segment (time period) of data collected by the CGM. In some embodiments, the memory 512 can contain a prediction equation (e.g., a connection function) based on a primary current signal generated by applying a constant voltage potential to a reference sensor, and a plurality of PPM current signals generated by applying a PPM sequence between primary current signal measurements.

[0104] Additionally or alternatively, the memory 512 can have stored therein a calibration index calculated based on the PPM currents for use during in-field calibration as previously described.

[0105] The memory 512 can also have stored therein a plurality of instructions. In various embodiments, the processor 510 can 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 perform as a microcontroller, or the like.

[0106] In some embodiments, the plurality of instructions stored in the memory 512 can include instructions that, when executed by the processor 510, cause the processor 510 to: (a) cause the CGM device 500 (via the biasing circuit 502, the CGM sensor 504, the current measurement circuit 506, and / or the sampling circuit 508) to measure current signals (e.g., primary current signals and PPM current signals) from interstitial fluid; (b) store the current signals in the memory 512; (c) calculate predictive equation (e.g., conversion and / or linking function) parameters such as ratios (and / or other relationships) of current from different pulses, voltage steps, or other voltage changes within a PPM sequence; (d) employ the calculated predictive equation (e.g., conversion and / or linking function) parameters to calculate glucose values (e.g., concentrations) using the predictive equation (e.g., conversion and / or linking function); and / or (e) communicate the glucose values to a user.

[0107] The memory 512 can be any suitable type of memory such as, but not limited to, one or more of volatile memory 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., one type of EEPROM, either nor or nand configuration, and / or one of a stacked or planar arrangement, and / or one of a single level cell (SLC), multi-level cell (MLC), or a combination SLC / MLC arrangement), resistive memory, fusible memory, metal oxide memory, phase change memory (e.g., chalcogenide memory), or magnetic memory. For example, the memory 512 can be packaged as a single chip or multiple chips. In some embodiments, the memory 512 can be embedded in an integrated circuit such as, for example, an application specific integrated circuit (ASIC), along with one or more other circuits.

[0108] As described above, the memory 512 can have stored therein a plurality of instructions that, when executed by the processor 510, cause the processor 510 to perform various actions specified by one or more of the stored plurality of instructions. The memory 512 can further have portions reserved for one or more “scratchpad” storage areas that can be used by the processor 510 for read or write operations in response to execution of one or more of the plurality of instructions.

[0109] In Figure 5A embodiments, the biasing circuit 502, the CGM sensor 504, the current measurement circuit 506, the sampling circuit 508, the processor 510, and the memory 512 containing the prediction equation 514 can be disposed within a wearable sensor portion 516 of the CGM device 500. In some embodiments, the wearable sensor portion 516 can include a display 517 for displaying information, such as glucose concentration information, for example, without the use of an external device. The display 517 can be any suitable type of human perceptible display, such as, but not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, or an organic light emitting diode (OLED) display.

[0110] Still referring to Figure 5A , the CGM device 500 can further include a portable user device portion 518. A processor 520 and a display 522 can be disposed within the portable user device portion 518. The display 522 can be coupled to the processor 520. The processor 520 can control text or images displayed by the display 522. The wearable sensor portion 516 and the portable user device portion 518 can be communicatively coupled. In some embodiments, for example, the communicative coupling of the wearable sensor portion 516 and the portable user device portion 518 can be via wireless communication through transmitter circuitry and / or receiver circuitry, such as a transmit / receive circuit TxRx 524a in the wearable sensor portion 516 and a transmit / receive circuit TxRx 524b in the portable user device 518. Such wireless communication can be through any suitable means, including but not limited to, a standard-based communication protocol, such as Bluetooth®, Bluetooth Low Energy (BLE), ZigBee®, Wi-Fi®, or Z-Wave®. In various embodiments, the wireless communication between the wearable sensor portion 516 and the portable user device portion 518 can alternatively be through 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 can be connected through one or more wires. In various embodiments, the wireless communication between the wearable sensor portion 516 and the portable user device portion 518 can alternatively be through 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 can be connected through one or more wires.

[0111] The display 522 can be any suitable type of human perceptible display such as, but not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, or an organic light emitting diode (OLED) display.

[0112] Referring now to Figure 5B , an example CGM device 550 is shown that is similar to the CGM device 500 shown in Figure 5A , but has a different partitioning of components. In the CGM device 550, the wearable sensor portion 516 includes the biasing circuit 502 coupled to the CGM sensor 504, and the current measurement circuit 506 coupled to the CGM sensor 504. The portable user device portion 518 of the CGM device 550 includes the sampling circuit 508 coupled to the processor 520, and the display 522 coupled to the processor 520. The processor 520 is further coupled to the memory 512, which can include the predictive equation 514 stored therein. In some embodiments, the processor 520 in the CGM device 550 can also perform the previously described functions performed by the processor 510 of the CGM device 500, for example. Figure 5A The wearable sensor portion 516 of the CGM device 550 can be smaller and lighter than the CGM device 500 of Figure 5A , and thus less invasive, as the sampling circuit 508, the processor 510, the memory 512, etc. are not included therein. Other component configurations can be employed. For example, as a variation of the CGM device 550 of Figure 5B , the sampling circuit 508 can remain on the wearable sensor portion 516 (such that the portable user device 518 receives digitized glucose signals from the wearable sensor portion 516).

[0113] Figure 6 is a side view schematic of an example glucose sensor 504 according to the embodiments provided herein. In some embodiments, the glucose sensor 504 can include a working electrode 602, a reference electrode 604, a counter electrode 606, and a background electrode 608. The working electrode can include a conductive layer coated with a chemical that reacts with a glucose-containing solution in a reduction-oxidation reaction (the chemical affecting the concentration of charge carriers and the time-dependent impedance of the CGM sensor 504). In some embodiments, the working electrode can be formed of platinum or roughened platinum. Other working electrode materials can be used. Example chemical catalysts (e.g., enzymes) for the working electrode 602 include glucose oxidase, glucose dehydrogenase, etc. The enzyme component can be immobilized onto the electrode surface, for example, by a cross-linking agent such as glutaraldehyde. An outer membrane layer can be applied over the enzyme layer to protect the entire internal assembly including the electrode and enzyme layer. In some embodiments, a mediator such as ferricyanide or ferrocene can be employed. Other chemical catalysts and / or mediators can be used.

[0114] In some embodiments, the reference electrode 604 can be formed of Ag / AgCl. The counter electrode 606 and / or the background electrode 608 can be formed of a suitable conductor such as platinum, gold, palladium, or the like. Other materials can be used for the reference electrode, counter electrode, and / or background electrode. In some embodiments, the background electrode 608 can be the same as the working electrode 602, but without the chemical catalyst and mediator. The counter electrode 606 can be isolated from the other electrodes by an isolation layer 610 (e.g., polyimide or another suitable material).

[0115] Figure 7 An example method 700 of determining glucose values during continuous glucose monitoring (CGM) measurements is shown in accordance with embodiments provided herein. In some embodiments, in block 702, the method 700 includes providing a CGM device (e.g., the CGM device 500) including a sensor, a memory, and a processor. In block 704, the method 700 includes applying a constant voltage potential (e.g., about 0.55 volts or another suitable voltage) to the sensor. In block 706, the method 700 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in the memory. In block 708, the method 700 includes applying a probe potential modulation sequence (e.g., the PPM sequence shown in FIG. 7B or another suitable PPM sequence) to the sensor. In block 710, the method 700 includes measuring a probe potential modulation current signal resulting from the probe potential modulation sequence and storing the measured probe potential modulation current signal in the memory. The method 700 further includes: in block 712, determining a conversion function value based on the measured probe potential modulation current signal (e.g., i43 or another PPM current signal); in block 714, determining an initial glucose concentration based on the conversion function value (e.g., G Figure 2B ) ; in block 716, determining a connection function value based on the primary current signal and the plurality of probe potential modulation current signals; and in block 718, determining a final glucose concentration (e.g., G 原始 ) based on the initial glucose concentration and the connection function value. comp

[0116] Figure 8 ​Another example method 800 of determining glucose values during continuous glucose monitoring (CGM) measurements is shown in accordance with embodiments provided herein. In some embodiments, in block 802, the method 800 includes providing a CGM device including a sensor, a memory, and a processor. In block 804, the method 800 includes applying a constant voltage potential to the sensor. In block 806, the method 800 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in the memory. In block 808, the method 800 includes applying a probe potential modulation sequence to the sensor. In block 810, the method 800 includes measuring a probe potential modulation current signal resulting from the probe potential modulation sequence and storing the measured probe potential modulation current signal in the memory. In block 812, the method 800 includes determining an initial glucose concentration based on a conversion function and the measured probe potential modulation current signal. In block 814, the method 800 includes determining a connection function value based on the primary current signal and the plurality of probe potential modulation current signals. In block 816, the method 800 includes determining a final glucose concentration based on the initial glucose concentration and the connection function value.

[0117] It should be noted that some embodiments or portions thereof can be provided as a computer program product or software that can include a machine-readable medium having non-transitory instructions stored thereon that can be used to program a computer system, controller, or other electronic device to perform a process according to one or more embodiments.

[0118] While the present disclosure is susceptible to various modifications and alternative forms, specific methods and devices have been shown by way of example in the drawings and are described in detail herein. However, it should be understood that the particular specific methods and devices disclosed herein are not intended to limit the disclosure or claims.

Claims

1. A method for determining glucose values ​​during continuous glucose monitoring (CGM) measurements, the method comprising: Provide a CGM device that includes sensors, bias circuitry, memory, and a processor; A constant voltage potential is applied to the sensor through the bias circuit; The primary current signal generated by the constant voltage potential is measured, and the measured primary current signal is stored in the memory; Applying a probe potential modulation sequence to the sensor, wherein applying the probe potential modulation sequence includes sequentially applying a plurality of voltage potentials, each of the plurality of voltage potentials being applied at different time periods during the continuous glucose monitoring; Measure a plurality of probe potential modulated current signals generated by each of the plurality of voltage potentials in the probe potential modulation sequence, and store the measured plurality of probe potential modulated current signals in the memory, wherein measuring the plurality of probe potential modulated current signals includes measuring a first output current at the beginning of the different time periods for applying each of the plurality of voltage potentials, and measuring a second output current at the end of the different time periods for applying each of the plurality of voltage potentials, such that a plurality of first output currents and a plurality of second output currents are measured for the plurality of voltage potentials in the probe potential modulation sequence, wherein the measured plurality of probe potential modulated current signals include the plurality of first output currents and the plurality of second output currents; The initial glucose concentration is determined based on one of the measured multiple probe potential modulation current signals and a conversion function. A connection function value is determined based on the measured primary current signal and the measured plurality of probe potential modulated current signals, wherein the connection function value is determined at least in part based on the ratio of each of the plurality of second output currents to one of the plurality of first output currents, the plurality of first output currents being measured at the beginning of the different time periods in which each of the plurality of second output currents is measured. as well as The final glucose concentration is determined based on the initial glucose concentration and the connection function value.

2. The method according to claim 1, wherein the plurality of voltage potentials in the probe potential modulation sequence includes a first voltage potential greater than the constant voltage potential, a second voltage potential less than the constant voltage potential, a third voltage potential less than the second voltage potential, and a fourth voltage potential greater than the third voltage potential.

3. The method of claim 2, wherein determining the initial glucose concentration based on one of the measured plurality of probe potential modulated current signals and a conversion function comprises determining the initial glucose concentration based on the conversion function and the second output current measured during the fourth voltage potential.

4. The method of claim 2, wherein the second output current measured during the fourth voltage potential is a final probe potential modulated current signal measured during the fourth voltage potential.

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

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

7. The method of claim 1, wherein the probe potential modulation sequence comprises four or more voltage steps.

8. A continuous glucose monitoring device, comprising: Wearable portion, the wearable portion comprising: The sensor is configured to generate an electrical signal from the interstitial fluid; processor; Memory, the memory being coupled to the processor; and A transmitter circuit system coupled to the processor; The memory of the continuous glucose monitoring 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 probe potential modulated current signals generated by applying a probe potential modulation sequence between the primary current signal measurements. Applying the probe potential modulation sequence includes sequentially applying multiple voltage potentials, with each of the multiple voltage potentials applied at different time periods during glucose monitoring. The memory contains computer program code stored therein, which, when executed by the processor, enables the continuous glucose monitoring device to: The primary current signal is measured and stored using the sensors and memory of the wearable portion; Measure and store a plurality of probe potential modulated current signals associated with the primary current signal, wherein the plurality of probe potential modulated current signals include a first output current measured at the beginning of the different time periods for applying each of the plurality of voltage potentials and a second output current measured at the end of the different time periods for applying each of the plurality of voltage potentials, such that a plurality of first output currents and a plurality of second output currents are measured for the plurality of voltage potentials. The initial glucose concentration is determined based on one of the measured multiple probe potential modulation current signals and a conversion function. A connection function value is determined based on the primary current signal and the measured plurality of probe potential modulated current signals, wherein the connection function is determined at least in part based on the ratio of each of the plurality of second output currents to one of the plurality of first output currents, the plurality of first output currents being measured at the beginning of the different time periods in which each of the plurality of second output currents is measured; and The final glucose concentration is determined based on the initial glucose concentration and the connection function value.

9. The continuous glucose monitoring device of claim 8, wherein the wearable portion is configured to apply a probe potential modulation sequence. in, The plurality of voltage potentials in the probe potential modulation sequence include a first voltage potential greater than the constant voltage potential, a second voltage potential less than the constant voltage potential, a third voltage potential less than the second voltage potential, and a fourth voltage potential greater than the third voltage potential.

10. The continuous glucose monitoring device of claim 9, wherein the computer program code, when executed by the processor, causes the continuous glucose monitoring device to determine the initial glucose concentration based on the second output current measured during the fourth voltage potential.

11. The continuous glucose monitoring device according to claim 10, wherein the second output current measured during the fourth voltage potential is a final probe potential modulated current signal measured during the fourth voltage potential.

12. The continuous glucose monitoring device according to claim 8, wherein the primary current signal and the probe potential modulation current signal are working electrode current signals.

13. The continuous glucose monitoring device according to claim 8, wherein the wearable portion further comprises: A current sensing circuit system, coupled to the sensor and configured to measure a current signal generated by the sensor; and A sampling circuit system coupled to the current sensing circuit system and configured to generate a digitized current signal from the measured current signal.

14. The continuous glucose monitoring device of claim 8, further comprising a portable user device including a receiver circuitry and a display, wherein the transmitter circuitry of the wearable portion is configured to transmit glucose values ​​to the receiver circuitry of the portable user device for presentation to a user of the continuous glucose monitoring device.

15. A method for determining glucose values ​​during continuous glucose monitoring (CGM) measurements, the method comprising: Provide a CGM device that includes sensors, memory, and a processor; A constant voltage potential is applied to the sensor during the continuous glucose monitoring, wherein the continuous glucose monitoring includes multiple different time periods; The primary current signal generated by the constant voltage potential is measured, and the measured primary current signal is stored in the memory; Applying a probe potential modulation sequence to the sensor, wherein applying the probe potential modulation sequence further includes: Applying a first voltage potential at a first different time interval, and A second voltage potential is applied during a second different time period immediately following the first different time period; Measuring the probe potential modulation current signal generated by the probe potential modulation sequence, and storing the measured probe potential modulation current signal in the memory, wherein measuring the probe potential modulation current signal further includes: The first probe current was measured at the first different time periods during which the first voltage potential was applied, and The second probe current is measured at the second different time periods used to apply the second voltage potential; The conversion function value is determined based on the measured probe potential modulation current signal; The initial glucose concentration is determined based on the conversion function value; A connection function value is determined based on the primary current signal and the plurality of probe potential modulated current signals, wherein the connection function is determined at least in part based on the ratio of the second probe current to the first probe current; and The final glucose concentration is determined based on the initial glucose concentration and the connection function value.

16. The method according to claim 15, wherein, The first voltage potential is greater than the constant voltage potential, and the second voltage potential is less than the constant voltage potential. The application of the probe potential modulation sequence includes applying a third voltage potential that is less than the second voltage potential and a fourth voltage potential that is greater than the third voltage potential.

17. The method of claim 16, wherein determining the conversion function value based on the measured probe potential modulated current signal comprises determining the conversion function value based on a fourth probe current measured during the fourth voltage potential.

18. The method of claim 17, wherein the fourth probe current measured during the fourth voltage potential is a final probe potential modulated current signal measured during the fourth voltage potential.

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

20. The method of claim 19, wherein applying the probe potential modulation sequence comprises four or more voltage steps.

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