Methods of determining glucose values and continuous glucose monitoring devices

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 a ratio of 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] Cross Reference to Related Applications

[0002] 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

[0003] The present disclosure relates generally to continuous sensor monitoring of analytes in bodily fluids, and more specifically, to continuous glucose monitoring (CGM). BACKGROUND

[0004] 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.

[0005] Accordingly, there is a need for improved apparatuses and methods for determining glucose values using CGM sensors. SUMMARY

[0006] 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 a ratio of the measured probe potential modulation current signal; determining a linking 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 linking function value.

[0007] In some embodiments, a continuous glucose monitoring (CGM) device includes a wearable portion having a sensor configured to generate a current signal from interstitial fluid, a processor, a memory coupled to the processor, and a transmitter 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 also 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 a ratio of 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.

[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 invention. 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 invention. 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 purposes of illustration only and are not necessarily drawn to scale. Thus, the drawings and specification are to be regarded as illustrative in nature and not as restrictive. The drawings are not intended to limit the scope of the invention in any way.

[0010] 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.

[0011] Figure 1B A graph of current curves for a probe potential modulation (PPM) sequence for a CGM sensor of Figure 1A

[0012] Figure 2 ​A graph showing comparison of glucose response signals in sequential order by primary current i10 and ratio parameter R4 of data according to one or more embodiments of the present disclosure.

[0013] Figure 3A A graph showing steady state conditions occurring at the electrode and its nearby boundary environment according to one or more embodiments of the present disclosure.

[0014] Figure 3B A graph showing an example of PPM sequence applied to a sensor collecting data according to one or more embodiments of the present disclosure.

[0015] Figure 3C A graph showing non-steady state conditions occurring at the electrode and its nearby boundary environment during E2 and E3 potential steps according to one or more embodiments of the present disclosure.

[0016] Figure 3D A graph showing I-V curves and individual potential steps of a PPM sequence implemented according to one or more embodiments of the present disclosure.

[0017] Figure 3E A graph showing typical output current from Figure 3B a PPM sequence shown and current markers in each potential step according to one or more embodiments of the present disclosure.

[0018] Figure 4A A graph showing comparison of i10 response to glucose from type 1 and type 2 sensors according to one or more embodiments of the present disclosure.

[0019] Figure 4B A graph showing comparison of R1 response to glucose from type 1 and type 2 sensors according to one or more embodiments of the present disclosure.

[0020] Figure 4C A graph showing comparison of R4 response to glucose from type 1 and type 2 sensors according to one or more embodiments of the present disclosure.

[0021] Figure 4D A graph showing comparison of y45 response to glucose from type 1 and type 2 sensors according to one or more embodiments of the present disclosure.

[0022] Figure 5A A graph showing comparison of initial response of i10 and R4 ratio from a single sensor according to one or more embodiments of the present disclosure.

[0023] Figure 5B A graph showing comparison of average initial normalized response of i10 and R4 from seven sensors according to one or more embodiments of the present disclosure.

[0024] Figure 6A A graph showing time current curves of primary data points in a linear test of four levels of acetaminophen using PPM and no PPM (NPPM) methods, in accordance with one or more embodiments of the present disclosure.

[0025] Figure 6B A graph showing current i10 response to glucose in a linear test of four levels of acetaminophen using NPPM methods, in accordance with one or more embodiments of the present disclosure.

[0026] Figure 6C A graph showing PPM current response to glucose in the same test, in accordance with one or more embodiments of the present disclosure.

[0027] Figure 6D A graph showing R4 response to glucose in the same test, in accordance with one or more embodiments of the present disclosure.

[0028] Figure 6E A graph showing R1 response to glucose in the same test, in accordance with one or more embodiments of the present disclosure.

[0029] Figures 7A-7D A graph showing CGM sensor response and its reference correlation from a set of seven sensors in a linear test; specifically, Figure 7A A graph showing G Ref A graph of R1 ratio; Figure 7B A graph showing G Ref A graph of R4 ratio; Figure 7C A graph showing G Ref A graph of y45 ratio; and Figure 7D A graph showing i10 versus G Ref in accordance with one or more embodiments of the present disclosure.

[0030] Figure 8 A table showing summary of G 原始 and G comp from i10, R4, y45, and R1 with in vitro data sets, in accordance with one or more embodiments of the present disclosure.

[0031] Figure 9A A high level block diagram of an example CGM device, in accordance with one or more embodiments of the present disclosure.

[0032] Figure 9B A high level block diagram of another example CGM device, in accordance with one or more embodiments of the present disclosure.

[0033] Figure 10is a side view schematic of an example glucose sensor according to one or more embodiments of the present disclosure.

[0034] Figure 11 An example method of determining glucose values during continuous glucose monitoring measurements is demonstrated according to embodiments provided herein. DETAILED DESCRIPTION

[0035] 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.

[0036] Primary data points or primary currents refer to measured values of a current signal generated in response to an analyte under 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 according to one or more embodiments of the present disclosure is demonstrated. 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 a 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 under 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 are 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 Primary data points are not shown, but the time and voltage at which each primary data point is measured is shown. For example, Figure 1A The circle 102 in 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 1AThe circle 104 in the figure represents time / voltage (6 minutes / 0.55 volts), at which a second primary data point (e.g., the second working electrode current) is measured for a sensor biased at voltage E0.

[0037] PPM current refers to the measured value of the current signal generated in response to the PPM applied to the sensor during continuous analyte sensing. The following text combines... Figure 3B and Figure 3E PPM is described in more detail. Figure 3B An example PPM cycle containing six voltage potential steps is shown. Figure 3E It shows the Figure 3B An example current response of a PPM cycle. The current generated during a PPM cycle is called the PPM current, which can be calculated according to... Figure 3E The description is used for sampling and labeling (e.g., i11 is the first current sampled during the first voltage step, i12 is the second current sampled during the first voltage step, i13 is the third current sampled during the first voltage step, i21 is the first current sampled during the second voltage step, and so on). Other numbers and / or types of voltage potential steps can be used.

[0038] A reference sensor is a sensor used to generate primary data points and PPM currents in response to a reference glucose concentration, such as that represented by a blood glucose meter (BGM) reading (e.g., primary currents and PPM currents measured for the purpose of determining a predictive equation (containing a connection function) for determining analyte concentrations that is subsequently stored in a continuous analyte monitoring (CAM) device and used during continuous analyte sensing).

[0039] For sensors deployed in non-whole blood environments with relatively constant temperatures, such as those used in continuous in vivo sensing operations, sensor error can be related to both short-term and long-term sensor sensitivity, as well as subsequent calibration methods. Several challenges / problems exist associated with such continuous sensing operations: (1) long break-in (warm-up) times; (2) factory or field calibration; and (3) sensitivity variations during continuous sensing operations. These problems / problems appear to be related to sensor sensitivity as represented by initial decay (break-in / warm-up time), sensitivity variations due to sensor environmental sensitivity during sensor production, and the environmental / conditions of subsequent sensor deployment.

[0040] According to one or more embodiments of this disclosure, the apparatus and method are operable to detect initial start conditions for continuous sensor operation for a sample analyte, and to detect sensor conditions at any point thereafter during continuous sensing operation of the sensor.

[0041] Methods of developing parameters for a predictive equation (e.g., a conversion function and / or a connection function) that can be used to determine analyte concentrations continuously and accurately from an analyte sensor are provided. In addition, methods and devices for determining analyte concentrations using PPM self-provided signals (e.g., working electrode current generated by the application of PPM) are provided. Such methods and devices can allow analyte concentration determinations to be made 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, taken in conjunction with the accompanying drawings, in which: Figures 1A-10 These and other embodiments are described.

[0042] For continuous glucose monitoring (CGM) biosensors that are typically operated at a constant applied voltage, current from the mediator is measured continuously 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, it is desirable to sense the operating conditions of the sensor during its initial period and after break-in time to identify any changes in its sensitivity.

[0043] After a CGM sensor is inserted / implanted subcutaneously into a user, the operation of the sensor begins from the applied voltage E0. The applied voltage E0is typically at a point on the redox plateau of the mediator. For a native mediator of oxygen with glucose oxidase, in a medium of about 100-150 mM chloride concentration, the oxidation plateau of hydrogen peroxide H2O2, the 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.

[0044] 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 determination. These current measurements represent the primary current and / or primary data points used for analyte determination during the continuous sensing operation. In some embodiments, following each primary current measurement, a periodic cycle of a probing potential modulation (PPM) can be employed such that a set of self- supplied currents accompanies each primary data point with information about the sensor / electrode state and / or condition.

[0045] 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 sinusoidal wave, a triangular wave, or any other potential modulation. An example of a PPM sequence is shown in Figure 3B

[0046] As described, a conventional biosensor for continuous analyte sensing operates by applying a constant potential to the working electrode (WE) of the sensor. Under 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 the analyte concentration and not changes in the applied potential. That is, there is no non-steady state current associated with applying different potentials. 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 time due to initial signal decay, changes in sensor sensitivity over the course of long-term monitoring, effects of changing background interference signals, etc.

[0047] ​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 operational 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.

[0048] Embodiments described herein include systems and methods for applying PPMs on top of otherwise constant voltages applied to an analyte sensor. Methods are provided for developing parameters for prediction equations, such as conversion functions and / or correction functions, which can be used to determine analyte concentrations continuously and accurately from an analyte sensor.

[0049] Extracted parameters: In accordance with one or more embodiments of the present disclosure, devices and methods are operable to use extracted parameters to determine analyte concentrations, such as the ratios R1, R4, and y45 from the currents under non-steady state (NSS) conditions and steady state (SS) degeneracy, described below. Using extracted parameters to determine analyte concentrations represents a different and unique approach for determining analyte concentrations. Analyte indicative parameters are extracted from the non-steady state and steady state degenerate currents during continuous sensor operation between repeated alternations of steady state and non-steady state conditions. Using ratio parameters as analyte indicative parameters has the advantages of being independent of the electrode size of the sensor, short pre-heat times, and relatively free of background signals. Additionally, ratio parameters can also provide a wide range connection from in vitro to in vivo analyte to provide a narrow range output analyte concentration from a wide range of sensor responses using a connection function.

[0050] The PPM methods described above provide potential modulation of applied voltages that are otherwise constant. Primary data points obtained from steady state conditions are used as an indicator of analyte concentration, while the related PPM currents and PPM parameters are used to provide information about sensor and electrode conditions. Examples of PPM sequences and output current curves have step changes in potential from high to low, then back to high, and thus are alternations of steady state and non-steady state conditions.

[0051] In some embodiments, the extracted parameters, or more specifically, the ratio parameters, are used as input parameters in error compensation regressions. Some parameters extracted from the non-steady state and steady state degenerate currents, such as R1 (= i13 / i11), R4 (= i43 / i41), and y45 (= i43 / i51), described below, are strongly related to analyte concentrations. As extracted parameters, such as ratios of PPM currents, these parameters are unitless.Figure 2 A plot of one of the ratio parameters R4 versus the primary current i10 is shown in a series of linear tests. It can be seen that there are different responses corresponding to glucose concentration levels. To better understand the characteristics of these ratio parameters, the sensor membrane and electrode boundary conditions are described below.

[0052] Steady state condition: Conventional biosensors used in continuous analyte monitoring operate in a steady state condition, which is established when the continuous monitoring sensor stabilizes after a stabilization time with a constant potential applied to the working electrode (WE). In this condition, the current is drawn from a constant flow of entering analyte molecules under steady state diffusion conditions generated by the outer membrane. This condition is depicted in Figure 3A Under this condition, a boundary environment is theoretically created as defined by the boundary conditions of 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 C 外 does not change, the current is proportional to the concentration gradient of the measurable substance at the electrode surface C med , which further depends on the analyte concentration gradient as defined by the boundary conditions.

[0053] Boundary environment: Figure 3A The boundary conditions of the boundary environment can be theoretically explained as follows: The analyte concentration C 外 at the outer interface of the membrane is in equilibrium with the membrane concentration C 膜 at the inner interface of the membrane. The lower concentration C 膜 inside the membrane indicates that the membrane is designed to reduce the inflow of analyte molecules so that the biosensor operates in a steady state condition. The relationship between C 外 and C 膜 is approximately defined by the equilibrium constant K 外 = C 膜 / C 外 <1 controls. It is further controlled by the lower diffusion coefficient D 外 compared to D 膜 . The membrane permeability P 膜 of the analyte = D 膜 *C 膜 together define the flux of the analyte. As the analyte molecules move towards the electrode covered with the 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 as a target cell versus glucose oxidase as a mediator. Once generated, the measurable substance will diffuse towards the electrode as well as towards the membrane. Under a constant applied voltage for the complete oxidation of the measurable substance, there will be a constant flow of the measurable substance drawn towards the electrode. Soon, a steady state is established, where the current is proportional to the concentration gradient of the measurable substance at the electrode surface (dC medproportional. Under diffusion-limited conditions (meaning that the rate of oxidation / consumption of the measurable substance is at a maximum, limited only by the diffusion of the measurable substance), C med The concentration gradient is projected as a straight line, defined as zero at the electrode surface and as a point at the membrane interface, defined by the equilibrium conditions reached by a number of processes (e.g., analyte flux into the enzyme, enzyme consumption and conversion of the analyte, and diffusion of the measurable substance). The concentration C med is defined roughly by diffusion. This steady-state condition is dynamic with changes in the external analyte concentration.

[0054] Under operating conditions controlled by a probe potential modulation (PPM) cycle, the primary data points are actually sampled and recorded under steady-state conditions, as the boundary environment is restored to steady-state conditions after each non-steady-state potential modulation cycle, as Figure 3B depicted (which shows a PPM sequence or cycle with six steps, although fewer, more, or different potential modulation steps can be employed).

[0055] Potential modulation and non-steady-state conditions: If the applied potential is modulated away from a constant voltage, such as a potential step from 0.55 V to 0.6 V ( Figure 3B Step 1 in Figure 3D E0 to E1 in Figure 3D ), but still within the oxidation plateau of the mediator (diffusion-limited region in the V-axis), some finite current with small decay will result. This is still a Faradaic process due to the asymmetric plateau controlled by exp(E app – E 0’ ). E app is the applied voltage, and E 0’ is the formal potential of the redox species. This finite current with small decay can be referred to as a plateau degeneracy, meaning a slightly different oxidation state on the plateau. The current-voltage relationship of the mediator is approximately described in Figure 3D . Examples of such output currents are shown in Figure 3E , where the PPM currents are labeled as i11, i12, and i13, while i10 is the primary current under steady-state conditions. For example, i11 is the first current sampled during the first potential step.

[0056] If the applied potential is reversed to a lower voltage, or specifically from E1 to E2 and further to Figure 3D E3 in Figure 3BIn 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 the measurable substance accumulating at and near the electrode surface. 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 3C As shown, where C med It is not zero at the electrode surface. For Figure 3B For steps 2 and 3, the output current of this type of effect is shown as negative, and... Figure 3E These are labeled i21, i22, i23 and i31, i32, i33. The negative current indicates 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 potential modulation process is short, and the boundary environment (C) between the inside and outside of the membrane... 膜 and C 外 () Remain unchanged.

[0057] The alternation of NSS and SS conditions: when the potential reverses again from E3 to E2 in step 4, as... Figure 3B and 3D As shown, a portion of the accumulated measurable substance is consumed, with oxidation occurring at a higher rate set by the higher potential E2. Even though E2 is not in the plateau region of redox substances, this step provides a sudden consumption of the measurable substance and a jump in current output from the non-steady-state concentration, thus providing a strong indication of concentration. Figure 3B The step 5 from E2 to E1 further completes the unsteady oxidation of the excess material, so as to reposition the sensor at the operating potential in the plateau region. Figure 3B The step 6 adopts a negative plateau period degenerate step to return to the original potential, which leads to the restoration of steady-state conditions before the next potential modulation cycle. Such conditions are theoretically similar to... Figure 3A The conditions are the same. Therefore, when the PPM cycle is repeated, steady-state and non-steady-state conditions alternate, providing a signal for analyte concentration determination.

[0058] Extracted ratio parameters: As an example, further consideration of parameters R1 (=i13 / i11), R4 (=i43 / i41), and y45 (=i43 / i51) provides the following insights: Parameter R1 is derived from the potential step 1 ( Figure 3B The potential step is extracted from the PPM current in the potential step 4, which is referred to as plateau degeneracy because the current originates from the quasi-plateau region. Parameter R4 is derived from the potential step 4 ( Figure 3B Extracted from the PPM current, the potential step is under unsteady-state conditions. According to... Figures 3A to 3CThe potential step is the accumulation of excess measurable species during a short time period when the electrode is at E3 potential (E3 = 0.1 V vs. Ag / AgCl) Figure 3D The potential step is the accumulation of excess measurable species during a short time period when the electrode is at E3 potential (E3 = 0.1 V vs. Ag / AgCl)

[0059] Independent electrode size of the sensor: When R1, R4 and y45 are used to indicate the analyte concentration, they have the advantage of being independent of the electrode size. Figure 4A A comparison of the steady state i10 current of two sensor types is shown, where the electrode area of sensor 1 is twice that of sensor 2. In linear tests of glucose solutions at 50, 100, 200, 300 and 450 mg / dL, the sensitivity of the response current of sensor 1 is twice that of sensor 2. This is expected. On the other hand, if current ratios such as R1, R4 and y45 are used as the indication parameters, the response of the different ratio parameters is actually independent of the electrode size. The small differences can just be due to different brands / batches of sensors. These comparison plots for R1, R4 and y45 are shown in Figures Figure 4B , 4C and 4D, respectively. The correlation of these ratio parameters with the analyte concentration is better represented with a non-linear relationship, such as a 2nd order polynomial.

[0060] Short initial warm-up time: Another advantage of using ratio parameters to indicate the analyte concentration is that the sensor does not have an initial decay behavior in practice during the continuous monitoring operation. Figure 5A The current i10 of the primary data points in the first linear test of the long term study is directly compared with the R4 ratio from the same sensor. Not only is the correspondence of the different R4 values with the glucose levels surprising, the initial small decay of R4 is even more surprising. This advantage can be better understood by comparing the normalized initial response from the steady state current (such as the primary data points i10) and the ratio parameter R4 extracted from the non-steady state current. Figure 5BThe average of the normalized i10 and R4 values of the initial responses of the seven sensors were compared. It can be seen that while the i10 current dropped 35% from the first reading after initial submersion into the solution (50 and 100 mg / dL) it took about 60 minutes to stabilize, the R4 ratio dropped on average only 5% from its first reading. This means that by using R4, the sensor warm-up time can be very fast, approximately 10-15 minutes, and does not even have to rely on a correction method / algorithm (as described below).

[0061] Independence from background signal: Another advantage of using the ratio parameters for analyte concentration determination is that they are relatively unaffected by background of different oxidizable species. One of the disadvantages of continuous monitoring steady state operating conditions is that other chemical species that are able to cross the membrane and be oxidized at the electrode surface also contribute to the total current at each current sampling time. These oxidizable species are not the target analyte and are thus interfering species that contribute to the overall signal. Thus, one of the major concerns for continuous analyte sensing is the background effect in the output current of the sensor. This can be seen in Figure 6A , in which one CGM sensor was operated in PPM method and the other CGM sensor was operated in constant applied voltage (NPPM or np). The CGM sensors were tested with glucose solutions having 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 was considered to be equivalent to a normal level of interfering background signal, while 0.6 mg / dL was considered to be a high level. The 1.2 and 1.8 mg / dL acetaminophen concentrations were considered to be extremely high levels. One linear run was performed for each background acetaminophen level at five glucose concentration levels of 50, 100, 200, 300, and 450 mg / dL.

[0062] The responses of the primary data points from the NPPM method (without PPM cycling) and the PPM method (with PPM cycling) are shown in Figure 6B and 6C The difference in the response slopes is due to the two different sensors operated in NPPM and PPM modes. The effect of different background levels of acetaminophen is practically the same as indicated by the intercepts of the NPPM and PPM methods, where the intercepts increased by about 75%, 150%, and 250% when the interfering level of acetaminophen increased from 0.2 mg / dL to 0.6, 1.2, and 1.8 mg / dL. While the primary data points from the NPPM sensor operation show the dependence of the intercept on the added acetaminophen level under steady state conditions, this result shows that the primary data points from the PPM method also come from steady state conditions, the same as the NPPM method.

[0063] On the other hand, when a ratio parameter such as R4 is used to indicate glucose concentration, the response is relatively independent of the different levels of background acetaminophen, as shown by Figure 6D and 6E Since the response is relatively independent of the background signal, the ratio parameter as an indicator of analyte concentration allows more regression resources (parameter terms) to be used to further improve the accuracy of analyte concentration determination.

[0064] Response curves and wide range correlation: Figure 7A - C, respectively, show G Ref The three plots of expected ratio parameters, and the raw i10 signal from primary data points under steady state conditions, where the same data set came from a group of 7 sensors. G Ref is the gravimetric glucose concentration, which was determined using a YSI glucose analyzer (from YSI Inc. of Yellow Springs, OH) to within ± 2% of face value. A 2nd order polynomial equation in each ratio plot was used as the reference correlation for each of the three ratio parameters from the average of the ratios from each glucose concentration. The independent and dependent variables of the three plots were reversed so that the ratio parameter could be directly input into the polynomial equation to obtain glucose concentration, rather than trying to solve a quadratic equation to obtain glucose concentration. The ratio response ranged over approximately 3X in a range from low to high, which is the same as the linear response of the i10 current shown in Figure 7D .

[0065] The ratio parameter as an analyte indicator parameter can also provide a wide range correlation of glucose from in vitro to in vivo in a wide range of responses in the same manner as the i10 current. That is, a single conversion function can be used to convert the ratio R4 values to G 原始 values, which are subsequently reduced in error ΔG / G 原始 by a correlation function as further described below. Other methods of using the R4 ratio (or other PPM ratio parameter) to determine analyte concentration can also be employed. The results of the compensation of each parameter's correlation function are summarized in the table 800 shown in Figure 8 . The results show that the ratio parameter is able to converge a wide range of sensor responses to a narrow band of glucose values by a correlation function.

[0066] Use of conversion and correlation functions

[0067] 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 a correlation of glucose from in vitro to in vivo by applying a uniform "conversion function" to the data of a wide range of sensor responses, followed by a "correlation function" to reduce the glucose error to a narrow band. The uniform conversion function calculates a 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.

[0068] In its simplest form, the unified conversion function can be a linear relationship between the measured current signal and the reference glucose level obtained from in-vitro test data. For example, the unified conversion function can be a linear relationship between the glucose signal (e.g., Iw-Ib, R1, R4, y45, or another PPM current signal or parameter), the slope, and the reference glucose G ref :

[0069] Signal = Slope * G ref

[0070] such that

[0071] G ref = Signal / Slope

[0072] where the slope represents the composite slope (slope 复合 ), also referred to as the unified composite slope. The above relationship can then be used to calculate the initial or raw glucose G 原始 during CGM:

[0073] G 原始 = Signal / Slope 复合

[0074] As described above, the PPM current signal parameters such as R1, R4, and y45 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 PPM current signal parameters such as R1, R4, and y45, or another suitable PPM current signal parameter. 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). For example, Figure 7A , 7B and 7C illustrate polynomial fits of R1, R4, and y45 to reference glucose G ref . These polynomial fits can be used as a linking function to determine the initial or raw glucose value from R1, R4, or y45:

[0075] For R1: G 原始 = 4351.9 * (R1) 2 - 4134.4 * (R1) + 1031.9

[0076] For R4: G 原始= 5068 * (R4) 2 - 2213.3 * (R4) + 290.05

[0077] For y45: G 原始 = 6266.8 * (y45) 2 - 1325.2 * (y45) + 117.49

[0078] Other relationships can be used. It should be noted that an equivalent form of Iw - Ib for primary data (i10) can be used. However, since R1, R4, and y45 are relatively independent of the effects of interference from other interfering substances, no background subtraction is used. In some embodiments, multiple conversion functions can be used.

[0079] 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 in vitro to in vivo connection simple without calibration. This connection function is derived from PPM parameters based on AG / G 原始 values. In this way of narrowing the error band from the initial or raw glucose G 原始 , the connection function is said to establish the connection from in vitro to in vivo without calibration, which means that all responses of the sensor to the narrow error band are accommodated.

[0080] When the connection function provides predicted in vivo glucose values to a narrow error band without calibration, the connection function is said to be a wide range connection from in vitro glucose to in vivo glucose. In this context, a one-to-one correspondence between in vitro sensitivity and in vivo sensitivity is not sought. Rather, the connection function will provide glucose values from the sensor within a range of sensitivities as long as the sensor is responsive to glucose. The response can be linear or non-linear.

[0081] With the rich information from PPM currents about the CGM sensor, this function is derived from PPM currents and related parameters. When each response data point in a periodic cycle is converted to a glucose value G 原始 by a composite conversion function, 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f(1 + connection function), where connection function = ΔG / G 原始 = f(PPM parameters). One way to derive the connection function is by fitting the relative error ΔG / G 原始 to the target of a multivariate regression and input parameters from the PPM parameters.

[0082] In summary, in some embodiments, R1, R4, or y45 PPM parameters can be used as part of a conversion function to convert raw current signal information to raw or initial glucose values G 原始 . Once G 原始 is known, a connection function can then be employed to calculate a compensated or final glucose signal or concentration G comp . For example, the connection function can be derived from in vitro data using the SS signal (i10) and the NSS signal (PPM signal) as input parameters, fitting the relative error ΔG / G 原始 to the target of a multivariate regression. An example connection function CF is provided below. It will be appreciated that other quantities and / or types of terms can be used.

[0083] CF = 30.02672 + 3.593884 * n123 - 11.74152 * R3 - 0.915224 * z54 + 0.026557 * GR41 - 0.061011 * GR43 + 0.17876 * Gy43 + 0.355556 * R62R54 - 1.910667 * R54R42 - 0.367626 * R54R43 - 0.010501 * GR43R31 - 4.92585 * z61z63 - 48.9909 * z63z32 - 22.97277 * z64z42 - 2.566353 * z64z43 + 69.93413 * z65z52 - 75.5636 * z65z32 - 16.28583 * z52z32... + 0.017588 * Gy51y42 + 0.020281 * Gy51y32 - 1.92665 * R62z51 - 0.348193 * R62z53 - 0.901927 * R62z31 + 75.69296 * R64z52 - 222.675 * R65z52 - 29.05662 * R65z53 - 142.145 * R65z32 + 15.47396 * R51z53 + 74.8836 * R51z32 + 23.1061 * R42z32 + 0.0018396 * GR52z41 + 0.100615 * GR31z32 - 8.89841 * R61y52 + 1.873765 * R61y42 + 2.459974 * R61y43... + 4.911592 * z41y31 - 1.04261 * z31y32 - 0.014889 * Gz61y42 + 0.007133 * Gz63y65 + 0.019989 * Gz64y51 + 0.004536 * Gz64y43 - 0.01605 * Gz65y54 + 0.00011 * Gz52y32 + 0.004775 * Gz53y54 - 0.531827 * d32 - 0.026387 * Gd11 - 0.010296 * Gd21 + 0.003426 * Gd32 - 6.350168 * d21d31 + 8.39652 * d22d31 - 0.0329025 * Gd11d31 - 0.039527 * av1 - 2.342127 * av1i10 + 0.550159 * av3i10 - 4.87669 * av14 - 0.139865 * av16 + 14.59835 * av25 - 9.31e-5 * Gav3 - 0.000143 * Gav4 + 0.001157 * Gav16 - 0.022394 * Gav25 - 0.000888 * Gav26 - 0.928135 * R30 + 2.307865 * R50 - 4.501269 * z60 - 7.491846 * w65w51 - 3.56458 * w65w53 + 7.147535 * w43w32.

[0084] For example, the input parameters of the connection function CF can be of the following types.

[0085] Probe currents: probe potential step-modulated currents i11, i12, i13,..., i61, i62, i63, where the first number (x) in the format ixy 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.

[0086] 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.

[0087] X-type parameters: The general format of 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, the parameter x61 is determined by i 6 3 / i 1 63, where i63 is the end PPM current of step 6 of the three currents recorded per 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.

[0088] Y-type parameters: The general format of 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, the parameter y61 is determined by i 6 3 / i 11 is determined, where i63 is the end PPM current of step 6 of the recorded three currents of 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.

[0089] Z-type parameter: The general format of this type of parameter is given by the first PPM current of the following potential step divided by the end PPM current of the previous potential step. For example, parameter z61 is determined by i 6 1 / i 1 3, where i61 is the first PPM current of step 6 of the recorded three currents of 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.

[0090] Additional terms include normalized currents: nill = i11 / i10, n112 = i12 / i10...; relative differences: dll = (i11-i12) / i10, d112 = (i12-i13) / i10...; average currents of each PPM potential step: avl = (i11+i12+i13) / 3, av2 = (i21+i22+i23) / 3,...; and average current ratios avl2 = avl / av2, av23 = av2 / av3.... Other miscellaneous terms include GR1 = G 原始 *R1, Gz61 = G 原始 *z61, Gy52 = G 原始y52..., R63R51 = R63 / R51, R64R43 = R64 / R43..., z64z42 = z64 / z42, z65z43 = z65 / z43..., d11d31 = d11 / d31, d12d32 = d12 / d32..., Gz61y52 = G * z61 / y52... and so on.

[0091] 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.

[0092] Thus, the extracted parameters R1, R4 and y45 can be used to indicate the original glucose analyte concentration, and a connection function can be used with the original glucose analyte concentration to connect in vitro glucose to in vivo glucose. Figure 8 The results of the compensation of the conversion function to G 原始 and the connection function to G comp are summarized in Table 2. The results show that R1, R4 and y45 can be used as analyte indicating signals, and that a wide range of responses can be converged to a narrow glucose value band by a connection function.

[0093] 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.

[0094] In one or more embodiments, the PPM cycle can be about 10-40 seconds, and / or contain more than one modulation potential step near 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.

[0095] In some embodiments, the probe potential modulation (PPM) 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 3D E2 and E3 of Example 2 and 3 of Figure 3B are two potential steps in the kinetic region of mediator that generates 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.

[0096] Different embodiments can be employed that accompany non-steady state conditions. For example, the non-steady state conditions can also be probed directly to the target potential E2 by one step and return to the starting potential E1, followed by a second probe potential step directly to a different potential E3 in the kinetic region with different non-steady state conditions, and then directly return 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.

[0097] Example CGM system

[0098] Figure 9A A high-level block diagram of an example CGM device 900 in accordance with embodiments provided herein is shown. Although not shown in Figure 9A various electronic components and / or circuitry is configured to be coupled to a power source, such as but not limited to a battery. The CGM device 900 includes a biasing circuit 902 that can be configured to be coupled to a CGM sensor 904. The biasing circuit 902 can be configured to apply a bias voltage, such as a continuous DC bias, to an analyte-containing fluid through the CGM sensor 904. 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 905 (e.g., a working electrode, a background electrode, etc.) of the CGM sensor 904.

[0099] The biasing circuit 902 can also be configured to apply a PPM sequence, such as that shown in FIG. 1C, or another PPM sequence, to the CGM sensor 904. 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.

[0100] In some embodiments, the CGM sensor 904 can include two electrodes, and a bias voltage and a probe potential modulation (PPM) can be applied across the pair of electrodes. In such cases, a current through the CGM sensor 904 can be measured. In other embodiments, the CGM sensor 904 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. The CGM sensor 904 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 the CGM sensor 904. Example chemicals include glucose oxidase, glucose dehydrogenase, and the like. In some embodiments, a mediator such as ferricyanide or ferrocene can be employed.

[0101] For example, a continuous bias voltage generated and / or applied by the bias circuit 902 can be in the range of about 0.1 to 1 volt with respect to the reference electrode. Other bias voltages can be used. Example PPM values were described previously.

[0102] In response to the PPM and the constant bias voltage, a PPM current and a non-PPM (NPPM) current through the CGM sensor 904 in an analyte-containing fluid can be communicated from the CGM sensor 904 to the current measurement (I 测量 ) circuit 906 (also referred to as current sense circuitry). The current measurement circuit 906 can be configured to sense and / or record a current measurement signal having a magnitude indicative of the amount of current communicated from the CGM sensor 904 (e.g., using a suitable current-to-voltage converter (CVC)). In some embodiments, the current measurement circuit 906 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 the CGM sensor 904 passes. A voltage developed across the resistor of the current measurement circuit 906 represents the magnitude of the current and can be referred to as the current measurement signal.

[0103] In some embodiments, a sampling circuit 908 can be coupled to the current measurement circuit 906 and can be configured to sample the current measurement signal. The sampling circuit 908 can then produce digitized time-domain sampled data representative of the current measurement signal (e.g., a digitized glucose signal). For example, the sampling circuit 908 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 908 can be sixteen, although more or fewer bits can be used in other embodiments. In some embodiments, the sampling circuit 908 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.

[0104] Still referring to Figure 9A The processor 910 can be coupled to the sampling circuit 908 and to the memory 912. In some embodiments, the processor 910 and the sampling circuit 908 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 910 and the sampling circuit 908 can be accomplished through the memory 912. In this embodiment, the sampling circuit 908 writes digital data to the memory 912 and the processor 910 reads the digital data from the memory 912.

[0105] The memory 912 can have stored therein one or more prediction equations 914 for determining glucose values based on the primary data points (NPPM currents) and PPM currents (from the current measurement circuit 906 and / or the sampling circuit 908). In some embodiments, these prediction equations can include one or more of the conversion functions and / or connection functions as described above. For example, in some embodiments, two or more prediction equations can be stored in the memory 912, each equation for a different segment (time period) of data collected by the CGM. In some embodiments, the memory 912 can include a prediction equation 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 PPM sequences between primary current signal measurements.

[0106] The memory 912 can also have stored therein a plurality of instructions. In various embodiments, the processor 910 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.

[0107] In some embodiments, the plurality of instructions stored in the memory 912 can include instructions that, when executed by the processor 910, cause the processor 910 to: (a) cause the CGM device 900 (via the biasing circuit 902, the CGM sensor 904, the current measurement circuit 906, and / or the sampling circuit 908) to measure current signals (e.g., primary current signals and PPM current signals) from interstitial fluid; (b) store the current signals in the memory 912; (c) calculate predictive equation 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 parameters to calculate glucose values (e.g., concentrations) using a predictive equation; and / or (e) communicate the glucose values to a user.

[0108] The memory 912 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. The memory 912 may, for example, be packaged as a single chip or multiple chips. In some embodiments, the memory 912 can be embedded in an integrated circuit such as, for example, an application specific integrated circuit (ASIC), along with one or more other circuits.

[0109] As described above, the memory 912 can have stored therein a plurality of instructions that, when executed by the processor 910, cause the processor 910 to perform various actions specified by one or more of the stored plurality of instructions. The memory 912 can further have portions reserved as one or more “scratchpad” storage areas that can be used by the processor 910 to perform read operations or write operations in response to execution of one or more of the plurality of instructions.

[0110] In Figure 9A embodiments, the biasing circuit 902, the CGM sensor 904, the current measurement circuit 906, the sampling circuit 908, the processor 910, and the memory 912 containing the prediction equation 914 can be disposed within a wearable sensor portion 916 of the CGM device 900. In some embodiments, the wearable sensor portion 916 can include a display 917 for displaying information, such as glucose concentration information, for example, without the use of an external device. The display 917 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.

[0111] Still referring to Figure 9A , the CGM device 900 can further include a portable user device portion 918. A processor 920 and a display 922 can be disposed within the portable user device portion 918. The display 922 can be coupled to the processor 920. The processor 920 can control text or images displayed by the display 922. The wearable sensor portion 916 and the portable user device portion 918 can be communicatively coupled. In some embodiments, for example, the communicative coupling of the wearable sensor portion 916 and the portable user device portion 918 can be via wireless communication through transmitter circuitry and / or receiver circuitry, such as a transmit / receive circuit TxRx 924a in the wearable sensor portion 916 and a transmit / receive circuit TxRx 924b in the portable user device 918. 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 916 and the portable user device portion 918 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 916 and the portable user device portion 918 can be connected through one or more wires.

[0112] The display 922 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.

[0113] Referring now to Figure 9B , an example CGM device 950 is shown, which is similar to the CGM device 900 of Figure 9A ​The illustrated embodiment, however, has a different partitioning of components. In CGM device 950, the wearable sensor portion 916 includes bias circuitry 902 coupled to CGM sensor 904, and current measurement circuitry 906 coupled to CGM sensor 904. The portable user device portion 918 of CGM device 950 includes sampling circuitry 908 coupled to processor 920, and display 922 coupled to processor 920. Processor 920 is further coupled to memory 912, which can include a predictive equation 914 stored therein. In some embodiments, processor 920 in CGM device 950 can also perform the previously described functions performed by processor 910 of CGM device 900, for example. Figure 9A The wearable sensor portion 916 of CGM device 950 can be smaller and lighter than the CGM device 900 of Figure 9A because sampling circuitry 908, processor 910, memory 912, etc. are not included therein. Other configurations of components can be employed. For example, as a variation of CGM device 950, sampling circuitry 908 can be retained on wearable sensor portion 916 (such that portable user device 918 receives digitized glucose signals from wearable sensor portion 916). Figure 9B

[0114] Figure 10 is a side view schematic of an example glucose sensor 904 in accordance with embodiments provided herein. In some embodiments, glucose sensor 904 can include a working electrode 1002, a reference electrode 1004, a counter electrode 1006, and a background electrode 1008. The working electrode can include a conductive layer coated with a chemical (that affects the concentration of charge carriers and the time-dependent impedance of CGM sensor 904) that reacts with glucose-containing solutions in a reduction-oxidation reaction. 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 working electrode 1002 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.

[0115] ​In some embodiments, the reference electrode 1004 can be formed of Ag / AgCl. The counter electrode 1006 and / or the background electrode 1008 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 1008 can be the same as the working electrode 1002, but without the chemical catalyst and mediator. The counter electrode 1006 can be isolated from the other electrodes by an isolation layer 1010 (e.g., polyimide or another suitable material).

[0116] Figure 11 An example method 1100 of determining glucose values during continuous glucose monitoring measurements is shown in accordance with embodiments provided herein. The method 1100 includes, in block 1102, providing a CGM device (e.g., the CGM device 900 or 950 of Figure 9A and 9B ). The method 1100 also includes, in block 1104, applying a constant voltage potential to the sensor (e.g., E0in Figure 1A ). In block 1106, the method 1100 includes measuring a primary current signal resulting from the constant voltage potential and storing the measured primary current signal in the memory. In block 1108, the method 1100 includes applying a probe potential modulation sequence to the sensor (e.g., the PPM sequence of Figure 3B ). In block 1110, the method 1100 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 1100 further includes, in block 1112, determining an initial glucose concentration based on a conversion function and a ratio of the measured probe potential modulation current signal, in block 1114, determining a connection function value based on the primary current signal and the plurality of probe potential modulation current signals, and in block 1116, determining a final glucose concentration based on the initial glucose concentration and the connection function value. The final glucose concentration can be communicated to a user (e.g., through the display 917 or 922 of the Figure 9A or 9B).

[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 disclosure is susceptible to various modifications and alternative forms, specific aspects thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that the specific aspects disclosed herein are not intended to limit the disclosure or the claims to the particular forms disclosed.

Claims

1. A method for determining glucose levels during continuous glucose monitoring measurements, the method comprising: A wearable continuous glucose monitoring device is provided, the wearable continuous glucose monitoring device including a sensor implanted subcutaneously in a user to generate an electrical signal from interstitial fluid, the wearable continuous glucose monitoring device further including: processor; A working electrode for converting an analyte into a measurable substance; The sensor coupled to the processor; A bias circuit that periodically establishes an unsteady condition by applying a sequence of probe potential modulated voltage signals to the working electrode; and Memory, the memory being coupled to the processor; Execute the primary data loop, including: Steady-state conditions are established by continuously applying a constant voltage potential to the sensor via the bias circuit; In this process, the constant voltage potential is applied to completely oxidize the measurable substance; The primary current signal generated by the constant voltage potential is periodically measured, and The primary current signal is stored in the memory; After each primary current signal measurement, a probe potential modulation loop is executed, including: Unsteady conditions are periodically established by perturbing the constant voltage potential with a sequence of probe potential modulated voltage signals that are different from the constant voltage potential via the bias circuit. The sequence of the probe potential modulated voltage signal includes: A first voltage potential that is greater than the constant voltage potential at the first moment; The second voltage potential, which is less than the constant voltage potential, occurs at a second time after the first time. A third voltage potential that is less than the second voltage potential at a third time after the second time; and A fourth voltage potential that is greater than the third voltage potential at a fourth time after the third time. The second voltage potential of the sequence of applied probe potential modulated voltage signals reduces the oxidation of the measurable substance. For each probe potential modulated voltage signal generated from the sequence of the probe potential modulated voltage signals, multiple probe potential modulated current signals are measured, and The plurality of probe potential modulated current signals are stored in a memory. Wherein, the time of the probe potential modulation cycle does not exceed half the time of the primary data cycle; Multiple ratio parameters are calculated based on the multiple probe potential modulated current signals. The initial glucose concentration is calculated by applying at least one of the plurality of ratio parameters to the conversion function; The connection function value is determined based on the primary current signal and the plurality of ratio parameters; Calculate the final glucose concentration based on the initial glucose concentration and the connection function value; and The final glucose concentration is displayed to the user.

2. The method of claim 1, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of the probe potential modulated current signal measured during the first voltage potential.

3. The method of claim 1, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of the probe potential modulation current signal measured during the fourth voltage potential.

4. The method according to claim 1, wherein the sequence of the probe potential modulated voltage signal further includes a fifth voltage potential greater than the fourth voltage potential at a fifth time after the fourth time.

5. The method of claim 4, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of the probe potential modulation current signal during the fourth voltage potential and the fifth voltage potential.

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

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

8. The method of claim 1, wherein the plurality of ratio parameters are independent of electrode size.

9. A wearable continuous glucose monitoring device, comprising: A working electrode for converting an analyte into a measurable substance; A sensor configured to be implanted subcutaneously into a user to generate an electrical signal from interstitial fluid; A bias circuit that periodically establishes unsteady conditions by applying a sequence of probe potential modulated voltage signals to the working electrode; processor; A transmitter circuit system coupled to the processor; as well as Memory, the memory being coupled to the processor; The memory contains connection functions and computer program code stored therein, which, when executed by the processor, enables the continuous glucose monitoring device to: Steady-state conditions are established by continuously applying a constant voltage potential to the sensor via the bias circuit; In this process, the constant voltage potential is applied to completely oxidize the measurable substance; During the primary data cycle, the primary current signal generated by the constant voltage potential is measured periodically; The primary current signal is stored in the memory; After measuring the primary current signal, an unsteady condition is periodically established by perturbing the constant voltage potential with a sequence of probe potential modulated voltage signals different from the constant voltage potential during the probe potential modulation cycle via the bias circuit. The sequence of the probe potential modulated voltage signal includes: A first voltage potential that is greater than the constant voltage potential at the first moment; The second voltage potential, which is less than the constant voltage potential, occurs at a second time after the first time. A third voltage potential that is less than the second voltage potential at a third time after the second time; and A fourth voltage potential that is greater than the third voltage potential at a fourth time after the third time. The second voltage potential of the sequence of applied probe potential modulated voltage signals reduces the oxidation of the measurable substance. For each probe potential modulation voltage signal generated in the sequence of probe potential modulation voltage signals, multiple probe potential modulation current signals are measured; The plurality of probe potential modulated current signals are stored in the memory. Wherein, the first duration of the probe potential modulation cycle does not exceed half the second duration of the primary data cycle; Multiple ratio parameters are calculated based on the multiple probe potential modulated current signals. The initial glucose concentration is calculated by applying at least one of the plurality of ratio parameters to the conversion function; The connection function value is determined based on the primary current signal and the plurality of ratio parameters; Calculate the final glucose concentration based on the initial glucose concentration and the connection function value; and The final glucose concentration is displayed to the user.

10. The wearable continuous glucose monitoring device of claim 9, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of the probe potential modulated current signal measured during the first voltage potential.

11. The wearable continuous glucose monitoring device of claim 9, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of the probe potential modulated current signal measured during the fourth voltage potential.

12. The wearable continuous glucose monitoring device according to claim 9, wherein the sequence of the probe potential modulated voltage signal further includes a fifth voltage potential greater than the fourth voltage potential at a fifth time after the fourth time.

13. The wearable continuous glucose monitoring device of claim 12, wherein the initial glucose concentration is calculated based on the conversion function and the ratio of the probe potential modulated current signal measured during the fourth voltage potential and the fifth voltage potential.

14. The wearable continuous glucose monitoring device according to claim 9, wherein the primary current signal and the probe potential modulated current signal are working electrode current signals.

15. The wearable continuous glucose monitoring device of claim 9, wherein the primary data cycle measures the primary current signal every 3 minutes and every 15 minutes.

16. The wearable continuous glucose monitoring device of claim 9, wherein the plurality of ratio parameters are independent of electrode size.

17. The wearable continuous glucose monitoring device according to claim 9, further comprising: A current sensing circuit system coupled to the sensor and configured to measure the current signal generated by the sensor; as well as A sampling circuit system coupled to the current sensing circuit system and configured to generate a digitized current signal from the measured current signal.

18. The wearable continuous glucose monitoring device of claim 9, wherein the transmitter circuitry is configured to transmit glucose values ​​to a portable user device for presentation to a user of the wearable continuous glucose monitoring device.

19. The wearable continuous glucose monitoring device of claim 9, wherein the second voltage potential and the third voltage potential cause the measurable substance to accumulate near the electrode surface.

20. The wearable continuous glucose monitoring device of claim 19, wherein the fourth voltage potential causes at least a portion of the accumulation of the measurable substance to be consumed.

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