Calibration method and device for a continuous glucose monitoring system

Abstract

The application relates to a calibration method and device of a continuous blood glucose monitoring system. In the application, the probability value of the instantaneous sensitivity can be determined by using the relationship equation of the instantaneous sensitivity obtained according to the acquired reference blood glucose concentration and the first current signal and the preset sensitivity and probability, so that the calibration can be completed only by using a single or a small amount of reference blood glucose concentration values, the blood sampling frequency of a user can be significantly reduced, the use convenience is improved, the data smoothness of the continuous second current signal or blood glucose concentration in a preset time length is calculated, the probability value of the instantaneous sensitivity is combined, the blood glucose fluctuation state and the sensor sensitivity change can be reflected in real time, the calibration parameters can be dynamically adjusted, and the calibration accuracy can be effectively improved.

Description

Technical Field

[0001] This application belongs to the field of system calibration technology, and in particular relates to a calibration method and device for a continuous blood glucose monitoring system. Background Technology

[0002] A Continuous Glucose Monitoring System (CGMS) uses an implanted glucose sensor to react chemically with glucose in tissue fluid, generating an electrical signal. The system acquires this signal and uses it to calculate the blood glucose concentration in real time, thus achieving real-time monitoring of blood glucose levels.

[0003] However, due to individual differences in sensors, dynamic changes in the human tissue fluid environment, and the time-varying nature of the sensor's reaction with glucose, sensors with similar performance in in vitro tests may exhibit different monitoring performance after implantation, leading to deviations in blood glucose monitoring results. These deviations may pose potential risks to blood glucose management in diabetic patients. Therefore, regular calibration of the system is necessary to improve monitoring accuracy.

[0004] Existing calibration techniques are mainly based on linear regression methods. This involves constructing a linear or nonlinear function using reference blood glucose concentration values ​​measured from fingertip or venous blood and the current signal synchronously acquired by the sensor. The parameters of the function are then solved, and the solved parameter values ​​are updated in the blood glucose concentration calculation formula. However, linear regression-based calibration methods typically require multiple reference blood glucose concentration values, which not only increases the user's workload (e.g., frequent blood sampling) but may also lead to significant changes in the sensor's sensitivity in vivo due to excessively long intervals between measurements of multiple reference blood glucose concentration values, thus reducing calibration accuracy. Furthermore, the hysteresis effect between glucose concentration in tissue fluid and glucose concentration in blood, and the dynamic variation of the hysteresis time with the rate of blood glucose change (e.g., longer hysteresis time when blood glucose changes rapidly and shorter hysteresis time when blood glucose is stable), further reduces calibration accuracy due to this inconsistent hysteresis. Summary of the Invention

[0005] The purpose of this application is to provide a calibration method and apparatus for a continuous blood glucose monitoring system. The calibration method and apparatus for a continuous blood glucose monitoring system can not only significantly reduce the number of blood samples taken by the user, but also effectively improve the accuracy of calibration.

[0006] The technical solution provided in this application is as follows: A calibration method for a continuous glucose monitoring system, the method comprising: The reference blood glucose concentration of the user's blood and the first current signal synchronously collected by the glucose sensor are obtained. The instantaneous sensitivity is obtained based on the reference blood glucose concentration and the first current signal; The probability value of the instantaneous sensitivity is obtained based on the relationship equation between the instantaneous sensitivity and the preset sensitivity and probability. Acquire multiple consecutive detection data from the user within a preset time period, wherein the detection data is a second current signal or blood glucose concentration; The data smoothness is obtained based on multiple sets of detection data; Based on the probability value, the data smoothness, and the preset calibration function, the calibrated coefficients are obtained, and the calibrated coefficients are updated in the blood glucose concentration calculation formula.

[0007] Optionally, obtaining the probability value of the instantaneous sensitivity based on the instantaneous sensitivity and a preset relationship equation between sensitivity and probability includes: Substituting the instantaneous sensitivity into the preset equation relating sensitivity and probability, we obtain the probability value of the instantaneous sensitivity.

[0008] Optionally, the preset equation relating sensitivity and probability is obtained through the following method: Based on the preset cumulative distribution function of sensitivity, the first preset value, and the second preset value, a first relationship equation between sensitivity and probability is obtained, and the first relationship equation is used as the preset relationship equation between sensitivity and probability.

[0009] Optionally, the preset sensitivity cumulative distribution function is obtained by the following method: The reference blood glucose test concentrations of blood samples from multiple subjects were obtained, along with multiple first test current signals synchronously acquired by the glucose sensor. The cumulative sensitivity distribution function is obtained based on the multiple reference blood glucose test concentrations and the multiple first test current signals.

[0010] Optionally, obtaining the sensitivity cumulative distribution function based on the plurality of reference blood glucose test concentrations and the plurality of first test current signals includes: Based on the multiple reference blood glucose test concentrations and the multiple first test current signals, multiple instantaneous test sensitivities are obtained; Based on multiple instantaneous test sensitivities and preset kernel functions, a cumulative sensitivity distribution function is constructed.

[0011] Optionally, the preset equation relating sensitivity and probability is obtained through the following method: The reference blood glucose test concentrations of blood samples from multiple subjects were obtained, along with multiple first test current signals synchronously acquired by the glucose sensor. Based on the multiple reference blood glucose test concentrations and the multiple first test current signals, multiple instantaneous test sensitivities are obtained; Based on multiple instantaneous test sensitivities and preset kernel functions, a cumulative sensitivity distribution function is constructed; Based on the cumulative distribution function of sensitivity, the first preset value, and the second preset value, the first equation relating sensitivity and probability is obtained. Substituting the multiple instantaneous test sensitivities into the first relational equation yields the test probability values ​​corresponding to the multiple instantaneous test sensitivities; The multiple instantaneous test sensitivities and multiple test probability values ​​are fitted to obtain a second relationship equation between sensitivity and probability, and the second relationship equation is used as the preset relationship equation between sensitivity and probability.

[0012] Optionally, obtaining data smoothness based on multiple detection data includes: Based on the multiple detection data, multiple data features are obtained; Data smoothness is obtained based on multiple data features.

[0013] Optionally, obtaining data smoothness based on multiple data features includes: The data smoothness is obtained by assigning weights to multiple data features.

[0014] Optionally, obtaining the calibrated coefficients based on the probability value, the data smoothness, and the preset calibration function includes: Substituting the probability value and the data smoothness into a preset calibration function yields the calibrated coefficients.

[0015] This application also provides a calibration device for a continuous glucose monitoring system, the device comprising: The first acquisition module is used to acquire the user's reference blood glucose concentration and the first current signal synchronously collected by the glucose sensor. The first processing module is used to obtain the instantaneous sensitivity based on the reference blood glucose concentration and the first current signal; The second processing module is used to obtain the probability value of the instantaneous sensitivity based on the instantaneous sensitivity and the preset relationship equation; The second acquisition module is used to acquire multiple consecutive detection data of the user within a preset time period, wherein the detection data is a second current signal or blood glucose concentration; The third processing module is used to obtain the data smoothness based on the multiple detection data; The calibration module is used to obtain calibrated coefficients based on the probability value, the data smoothness, and the preset calibration function, and to update the calibrated coefficients to the blood glucose concentration calculation formula.

[0016] Compared with existing technologies, the calibration method and apparatus for a continuous blood glucose monitoring system provided in this application acquires a reference blood glucose concentration of the user's blood and a first current signal synchronously collected by a glucose sensor. Based on the reference blood glucose concentration and the first current signal, an instantaneous sensitivity is obtained. Based on the instantaneous sensitivity and a preset relational equation, a probability value of the instantaneous sensitivity is obtained. Multiple continuous detection data points of the monitored subject within a preset time period are acquired, where the detection data is a second current signal or blood glucose concentration. Based on the multiple detection data points, data smoothness is obtained. Based on the probability value, data smoothness, and a preset calibration function, a calibrated coefficient is obtained, and the calibration is then applied. The coefficients after calibration are updated to the blood glucose concentration calculation formula. In this application, by using the instantaneous sensitivity obtained from the acquired reference blood glucose concentration and the first current signal, and the preset relationship equation between sensitivity and probability, the probability value of instantaneous sensitivity can be determined. This allows calibration to be completed with only a single or a small number of reference blood glucose concentration values, significantly reducing the number of blood samples taken by the user and improving ease of use. By using the continuous second current signal or blood glucose concentration within a preset time to calculate the smoothness of the data, combined with the probability value of instantaneous sensitivity, the blood glucose fluctuation state and sensor sensitivity changes can be reflected in real time, thereby dynamically adjusting the calibration parameters and effectively improving the accuracy of calibration. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a calibration method for a continuous blood glucose monitoring system disclosed in an embodiment of this application; Figure 2 The sensitivity probability relationship curve disclosed in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of a calibration device for a continuous blood glucose monitoring system disclosed in an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] It should be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to the other component.

[0021] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "a plurality of" or "several" means two or more, unless otherwise explicitly specified.

[0023] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which this application can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size should still fall within the scope of the technical content disclosed in this application, provided that they do not affect the effects and purposes that this application can produce.

[0024] like Figure 1 As shown, this embodiment provides a calibration method for a continuous glucose monitoring system, the method including: S11. Obtain the user's reference blood glucose concentration and the first current signal synchronously acquired by the glucose sensor; In this embodiment, the reference blood glucose concentration of the user's blood can be the blood glucose concentration of the user's fingertip blood or venous blood. The first current signal can be the current signal generated by the electrochemical reaction between the electrode of the glucose sensor and the blood glucose in the body fluid, which is collected by the glucose sensor implanted in the user's body (such as subcutaneous tissue). Preferably, the reference blood glucose concentration of the user's fingertip blood detected by the blood glucose meter is obtained. Specifically, the user's fingertip can be punctured with a lancet, and the fingertip blood can be drawn with a test strip. The blood glucose meter can then be used to detect the fingertip blood on the test strip.

[0025] S12. Obtain the instantaneous sensitivity based on the reference blood glucose concentration and the first current signal; In this embodiment, the first current signal is a digital signal, and the reference blood glucose concentration can be divided by the first current signal to obtain the calculated instantaneous sensitivity.

[0026] S13. Based on the relationship equation between instantaneous sensitivity and preset sensitivity and probability, obtain the probability value of instantaneous sensitivity; In this embodiment, the preset equation relating sensitivity and probability is a pre-set equation relating sensitivity and probability.

[0027] S14. Acquire multiple consecutive detection data from the user within a preset time period, wherein the detection data is a second current signal or blood glucose concentration; In this embodiment, the preset duration is a pre-set duration, which can be the acquisition of multiple continuous second current signals collected by the glucose sensor implanted in the user's body within the preset duration, or the acquisition of the blood glucose concentration calculated by the blood glucose concentration calculation formula, i.e., the output value of the continuous blood glucose monitoring system.

[0028] S15. Obtain the data smoothness based on multiple detection data; In this embodiment, the data smoothness can be either current smoothness or blood glucose smoothness. That is, the current smoothness can be obtained from multiple consecutive second current signals, or the blood glucose smoothness can be obtained from multiple consecutive blood glucose concentrations.

[0029] S16. Based on the probability value, data smoothness, and preset calibration function, obtain the calibrated coefficients and update the calibrated coefficients to the blood glucose concentration calculation formula.

[0030] In this embodiment, the preset calibration function is a pre-set calibration function. It can be a calibrated coefficient obtained based on the probability value of instantaneous sensitivity, current smoothness, and the preset calibration function, or it can be a calibrated coefficient obtained based on the probability value of instantaneous sensitivity, blood glucose smoothness, and the preset calibration function.

[0031] Compared with existing technologies, the calibration method and apparatus for a continuous blood glucose monitoring system provided in this application obtains a reference blood glucose concentration of the user's blood and a first current signal synchronously acquired by a glucose sensor. Based on the reference blood glucose concentration and the first current signal, an instantaneous sensitivity is obtained. Based on the instantaneous sensitivity and a preset formula, a probability value of the instantaneous sensitivity is obtained. Multiple continuous detection data points of the monitored subject within a preset time period are obtained, where the detection data is a second current signal or blood glucose concentration. Based on the multiple detection data points, data smoothness is obtained. Based on the probability value, data smoothness, and a preset calibration function, a calibrated coefficient is obtained, and the calibration is then applied. The coefficients after calibration are updated to the blood glucose concentration calculation formula. In this application, by using the instantaneous sensitivity obtained from the acquired reference blood glucose concentration and the first current signal, and the preset relationship equation between sensitivity and probability, the probability value of instantaneous sensitivity can be determined. This allows calibration to be completed with only a single or a small number of reference blood glucose concentration values, significantly reducing the number of blood samples taken by the user and improving ease of use. By using the continuous second current signal or blood glucose concentration within a preset time to calculate the smoothness of the data, combined with the probability value of instantaneous sensitivity, the blood glucose fluctuation state and sensor sensitivity changes can be reflected in real time, thereby dynamically adjusting the calibration parameters and effectively improving the accuracy of calibration.

[0032] As one implementation method, in this embodiment of the application, the probability value of the instantaneous sensitivity is obtained according to the relationship equation between the instantaneous sensitivity and the preset sensitivity and probability, including: Substituting the instantaneous sensitivity into the preset equation relating sensitivity and probability, we obtain the probability value of the instantaneous sensitivity.

[0033] In this embodiment, by substituting the current instantaneous sensitivity into the preset equation relating sensitivity and probability, the probability value corresponding to the instantaneous sensitivity can be obtained. This probability value represents the credibility or reliability of the current instantaneous sensitivity.

[0034] As one implementation method, in this embodiment of the application, the preset relationship equation between sensitivity and probability is obtained through the following method: Based on the preset cumulative distribution function of sensitivity, the first preset value, and the second preset value, the first relationship equation between sensitivity and probability is obtained, and the first relationship equation is used as the preset relationship equation between sensitivity and probability.

[0035] In this embodiment, the first preset value and the second preset value are pre-set values, and the first relationship equation between sensitivity and probability can be specifically as follows: , in, The probability of sensitivity. The preset sensitivity cumulative distribution function, For sensitivity, The first preset value, The second preset value, specifically, for , for .

[0036] As one implementation method, in this embodiment of the application, the preset sensitivity cumulative distribution function is obtained by the following method: S21. Obtain the reference blood glucose test concentration of blood samples from multiple subjects, as well as multiple first test current signals synchronously acquired by the glucose sensor. In this embodiment, the reference blood glucose test concentration of the subject's blood sample can be the blood glucose concentration of the subject's fingertip blood or venous blood, and the multiple second current signals can be the current signals generated by the electrochemical reaction between the electrodes of the glucose sensor and the blood glucose in the body fluid, which are collected by glucose sensors implanted in multiple subjects (such as subcutaneous tissue).

[0037] S22. Based on multiple reference blood glucose test concentrations and multiple first test current signals, obtain the sensitivity cumulative distribution function.

[0038] In this embodiment, multiple instantaneous test sensitivities can be obtained based on multiple reference blood glucose test concentrations and multiple first test current signals. Based on the multiple instantaneous test sensitivities and a preset kernel function, a sensitivity cumulative distribution function is constructed.

[0039] As one implementation method, in this embodiment of the application, step S22 includes: S221. Based on multiple reference blood glucose test concentrations and multiple first test current signals, multiple instantaneous test sensitivities are obtained; In this embodiment, each reference blood glucose test concentration can be divided by the corresponding first test current signal to obtain each instantaneous test sensitivity.

[0040] S222. Based on multiple instantaneous test sensitivities and preset kernel functions, construct a cumulative sensitivity distribution function.

[0041] In this embodiment, the preset kernel function is a pre-set kernel function. The preset kernel function can be any one of the Gaussian kernel function, uniform kernel function, and Epanechnikov kernel function. Preferably, the preset kernel function is the Gaussian kernel function. Multiple instantaneous test sensitivities and the preset kernel function can be substituted into the initial sensitivity cumulative distribution function to obtain the constructed sensitivity cumulative distribution function.

[0042] Specifically, multiple instantaneous sensitivity tests The specific expression for the Gaussian kernel function is: , The expression for the cumulative distribution function of sensitivity is as follows: , in, For sensitivity, Let be the sensitivity of the i-th instantaneous test, h be the bandwidth, h is used to control the smoothness of the estimate; a smaller h makes the estimate closer to the data point, while a larger h makes the estimate smoother, n be the number of instantaneous test sensitivities, and e be the natural constant. The preset sensitivity cumulative distribution function, This is the Gaussian kernel function.

[0043] As one implementation method, in this embodiment of the application, the preset relationship equation between sensitivity and probability is obtained through the following method: S31. Obtain the reference blood glucose test concentration of blood samples from multiple subjects, as well as multiple first test current signals synchronously acquired by the glucose sensor. S32. Based on multiple reference blood glucose test concentrations and multiple first test current signals, obtain multiple instantaneous test sensitivities; S33. Construct a cumulative sensitivity distribution function based on multiple instantaneous test sensitivities and a preset kernel function; S34. Based on the cumulative distribution function of sensitivity, the first preset value, and the second preset value, the first relationship equation between sensitivity and probability is obtained; S35. Substitute multiple instantaneous test sensitivities into the first relational equation to obtain the test probability values ​​corresponding to the multiple instantaneous test sensitivities; In this embodiment, each instantaneous test sensitivity is substituted into the first relational equation to obtain the test probability value corresponding to each instantaneous test sensitivity.

[0044] S36. Fit multiple instantaneous test sensitivities and multiple test probability values ​​to obtain a second relationship equation between sensitivity and probability, and use the second relationship equation as the preset relationship equation between sensitivity and probability.

[0045] In this embodiment, as Figure 2 As shown, multiple instantaneous test sensitivities (horizontal axis) and multiple test probability values ​​(vertical axis) can be plotted as a sensitivity-probability relationship curve. In the figure, the slope represents the instantaneous test sensitivity, and the probability value represents the test probability value. Based on the trend of the curve, a suitable mathematical model is selected for fitting to obtain the second relationship equation between sensitivity and probability. Specifically, an exponential function can be chosen for fitting, and the resulting second relationship equation between sensitivity and probability is as follows:

[0046] in, The probability of sensitivity. For sensitivity, It is a scaling factor used to control the overall size of the probability, where e is the natural constant. It is the average of the sensitivity of multiple instantaneous tests. It is the variance of the sensitivity of multiple instantaneous tests.

[0047] As one implementation method, in this embodiment of the application, step S15 includes: S151. Based on multiple detection data, obtain multiple data features; In this embodiment, the data features can be the rate of change, intercept, variance, and coefficient of variation. Specifically, it can be obtained by fitting a linear equation of each second current signal based on least squares to obtain the rate of change and the intercept, and then calculating the current variance of the fitting residual or the current variance of multiple second current signals. The coefficient of variation of the current is then calculated based on the mean and standard deviation of multiple second current signals. Alternatively, it can be obtained by fitting a linear equation of each blood glucose concentration based on least squares to obtain the rate of change and the intercept, and then calculating the blood glucose variance of the fitting residual or the blood glucose variance of multiple blood glucose concentrations. The coefficient of variation of the blood glucose is then calculated based on the mean and standard deviation of multiple blood glucose concentrations.

[0048] S152. Based on multiple data features, obtain the data smoothness.

[0049] In this embodiment, the current smoothness can be obtained based on the current rate of change, current intercept, current variance, and current coefficient of variation, or the blood glucose smoothness can be obtained based on the blood glucose rate of change, blood glucose intercept, blood glucose variance, and blood glucose coefficient of variation.

[0050] As one implementation method, in this embodiment of the application, data smoothness is obtained based on multiple data features, including: The smoothness of the data is obtained by assigning weights to multiple data features.

[0051] In this embodiment, the current smoothness can be obtained by weighting the current rate of change, current intercept, current variance, and current coefficient of variation. Alternatively, the blood glucose smoothness can be obtained by weighting the blood glucose rate of change, blood glucose intercept, blood glucose variance, and blood glucose coefficient of variation. The specific formula can be: Smooth=A1*k+A2*b+A3*Cov+A4*CV, Wherein, Smooth is the smoothness of current or blood glucose, k is the rate of change of current or blood glucose, b is the intercept of current or blood glucose, Cov is the variance of current or blood glucose, CV is the coefficient of variation of current or blood glucose, A1 is the weight of the rate of change of current or blood glucose, A2 is the weight of the intercept of current or blood glucose, A3 is the weight of the variance of current or blood glucose, and A4 is the weight of the coefficient of variation of current or blood glucose.

[0052] In this embodiment, the weights can be dynamically set according to the current change rate, current intercept, current variance, and current coefficient of variation. The weights can also be dynamically set according to the blood glucose change rate, blood glucose intercept, blood glucose variance, and blood glucose coefficient of variation. For example, when the current change rate or blood glucose change rate is greater than the corresponding preset change rate (at which time the user may be in a mealtime), the weight of the current change rate or blood glucose change rate is increased so that the current smoothness or blood glucose smoothness can better reflect the blood glucose fluctuation state.

[0053] As one implementation method, in this embodiment of the application, the calibrated coefficients are obtained based on the probability value, data smoothness, and a preset calibration function, including: Substitute the probability value and data smoothness into the preset calibration function to obtain the calibrated coefficients.

[0054] In this embodiment, the preset calibration function can be a smoothing function based on an exponential function, a weighted average based on a polynomial, or a combination of a smoothing function based on an exponential function and a weighted average based on a polynomial. Preferably, the preset calibration function is a smoothing function based on an exponential function. By using a smoothing function based on an exponential function, the calibrated coefficients can be smoothed without abrupt changes, and the maximum and minimum values ​​can be limited by the smoothing function. Specifically, the smoothing function based on an exponential function is as follows: prob=2*(pMax-pMin) / (1+exp(-m*(ref_prob-n)-test_prob / t))+pMin, Where prob is the calibrated coefficient, pMax is the preset maximum value, pMin is the preset minimum value, exp represents the exponential function, m, n, and t are preset parameters, ref_prob is the probability value, and test_prob is the data smoothness.

[0055] like Figure 3 As shown, this application also provides a calibration device for a continuous glucose monitoring system, the device comprising: The first acquisition module 31 is used to acquire the reference blood glucose concentration of the user's blood and the first current signal synchronously collected by the glucose sensor. The first processing module 32 is used to obtain the instantaneous sensitivity based on the reference blood glucose concentration and the first current signal; The second processing module 33 is used to obtain the probability value of the instantaneous sensitivity based on the instantaneous sensitivity and the preset relationship. The second acquisition module 34 is used to acquire multiple consecutive detection data of the user within a preset time period, wherein the detection data is a second current signal or blood glucose concentration; The third processing module 35 is used to obtain the data smoothness based on multiple detection data. The calibration module 36 is used to obtain the calibrated coefficients based on the probability value, data smoothness and preset calibration function, and update the calibrated coefficients to the blood glucose concentration calculation formula.

[0056] The embodiments in this specification are described in a progressive manner, with each embodiment focusing on the aspects related to... For any differences between the embodiments, or for the same or similar parts between the embodiments, please refer to each other.

[0057] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A calibration method for a continuous glucose monitoring system, characterized in that, The method includes: The reference blood glucose concentration of the user's blood and the first current signal synchronously collected by the glucose sensor are obtained. The instantaneous sensitivity is obtained based on the reference blood glucose concentration and the first current signal; The probability value of the instantaneous sensitivity is obtained based on the relationship equation between the instantaneous sensitivity and the preset sensitivity and probability. The preset relationship equation between the sensitivity and probability is pre-constructed based on the cumulative sensitivity distribution function, which is generated based on multiple instantaneous test sensitivities determined by the reference blood glucose test concentrations of multiple subjects and the synchronously acquired test current signals. Acquire multiple consecutive detection data from the user within a preset time period, wherein the detection data is a second current signal or blood glucose concentration; The data smoothness is obtained based on multiple sets of detection data; Based on the probability value, the data smoothness, and the preset calibration function, the calibrated coefficients are obtained, and the calibrated coefficients are updated in the blood glucose concentration calculation formula.

2. The method according to claim 1, characterized in that, The step of obtaining the probability value of the instantaneous sensitivity based on the instantaneous sensitivity and a preset relationship equation between sensitivity and probability includes: Substituting the instantaneous sensitivity into the preset equation relating sensitivity and probability, we obtain the probability value of the instantaneous sensitivity.

3. The method according to claim 2, characterized in that, The preset equation relating sensitivity and probability is obtained through the following method: Based on the preset cumulative distribution function of sensitivity, the first preset value and the second preset value, the first relationship equation between sensitivity and probability is obtained, and the first relationship equation is used as the preset relationship equation between sensitivity and probability. The preset sensitivity cumulative distribution function is obtained through the following method: The reference blood glucose test concentrations of blood samples from multiple subjects were obtained, along with multiple first test current signals synchronously acquired by the glucose sensor. Based on the multiple reference blood glucose test concentrations and the multiple first test current signals, the sensitivity cumulative distribution function is obtained; The step of obtaining the sensitivity cumulative distribution function based on multiple reference blood glucose test concentrations and multiple first test current signals includes: Based on the multiple reference blood glucose test concentrations and the multiple first test current signals, multiple instantaneous test sensitivities are obtained; Based on multiple instantaneous test sensitivities and preset kernel functions, a cumulative sensitivity distribution function is constructed.

4. The method according to claim 2, characterized in that, The preset equation relating sensitivity and probability is obtained through the following method: The reference blood glucose test concentrations of blood samples from multiple subjects were obtained, along with multiple first test current signals synchronously acquired by the glucose sensor. Based on the multiple reference blood glucose test concentrations and the multiple first test current signals, multiple instantaneous test sensitivities are obtained; Based on multiple instantaneous test sensitivities and preset kernel functions, a cumulative sensitivity distribution function is constructed; Based on the cumulative distribution function of sensitivity, the first preset value, and the second preset value, the first equation relating sensitivity and probability is obtained. Substituting the multiple instantaneous test sensitivities into the first relational equation yields the test probability values ​​corresponding to the multiple instantaneous test sensitivities; The multiple instantaneous test sensitivities and multiple test probability values ​​are fitted to obtain a second relationship equation between sensitivity and probability, and the second relationship equation is used as the preset relationship equation between sensitivity and probability.

5. The method according to claim 1, characterized in that, The step of obtaining data smoothness based on multiple detection data includes: Based on the multiple detection data, multiple data features are obtained; Data smoothness is obtained based on multiple data features.

6. The method according to claim 5, characterized in that, The process of obtaining data smoothness based on multiple data features includes: The data smoothness is obtained by assigning weights to multiple data features.

7. The method according to claim 1, characterized in that, The step of obtaining the calibrated coefficients based on the probability value, the data smoothness, and the preset calibration function includes: Substituting the probability value and the data smoothness into a preset calibration function yields the calibrated coefficients.

8. A calibration device for a continuous glucose monitoring system, characterized in that, The device includes: The first acquisition module is used to acquire the user's reference blood glucose concentration and the first current signal synchronously collected by the glucose sensor. The first processing module is used to obtain the instantaneous sensitivity based on the reference blood glucose concentration and the first current signal; The second processing module is used to obtain the probability value of the instantaneous sensitivity based on the instantaneous sensitivity and the preset relationship equation between sensitivity and probability. The preset relationship equation between sensitivity and probability is based on a cumulative sensitivity distribution function pre-constructed. The cumulative sensitivity distribution function is generated based on multiple instantaneous test sensitivities determined by the reference blood glucose test concentrations of multiple subjects and the synchronously acquired test current signals. The second acquisition module is used to acquire multiple consecutive detection data of the user within a preset time period, wherein the detection data is a second current signal or blood glucose concentration; The third processing module is used to obtain the data smoothness based on the multiple detection data; The calibration module is used to obtain calibrated coefficients based on the probability value, the data smoothness, and the preset calibration function, and to update the calibrated coefficients to the blood glucose concentration calculation formula.

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