Closed-loop artificial pancreas drug infusion control system

Abstract

The application discloses a full-closed-loop artificial pancreas medicine infusion control system, which comprises a program module, at least one detection module, a hypoglycemic medicine infusion module and a hyperglycemic medicine infusion module. An algorithm is preset in the program module, the detection module detects blood glucose values, the hypoglycemic medicine infusion module performs hypoglycemic medicine infusion according to infusion instructions calculated by the algorithm, the hyperglycemic medicine infusion module performs hyperglycemic medicine infusion according to infusion instructions calculated by the algorithm, and full-closed-loop control of artificial pancreas system medicine infusion is realized.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of and priority of the following patent application: PCT patent application filed on October 25, 2021, application number PCT / CN2021 / 126024. Technical Field

[0003] This invention relates primarily to the field of medical devices, and in particular to a fully closed-loop artificial pancreas drug infusion control system. Background Technology

[0004] In a healthy person, the pancreas automatically secretes the necessary insulin / glucagon based on blood glucose levels, thus maintaining a reasonable range of blood sugar fluctuations. However, in diabetic patients, pancreatic function is abnormal, and the pancreas is unable to secrete the required insulin normally. Diabetes is a metabolic disease and is a lifelong condition. Current medical technology cannot cure diabetes; it can only control the occurrence and development of diabetes and its complications by stabilizing blood sugar levels.

[0005] Diabetic patients need to have their blood glucose levels checked before insulin injection. Current methods allow for continuous glucose monitoring, sending the data in real-time to a display device for user viewing. This method is called Continuous Glucose Monitoring (CGM). The device is attached to the skin, with its probe inserted into the subcutaneous fluid to perform the measurement. Based on the CGM reading, the infusion device delivers the required amount of insulin subcutaneously, creating a closed-loop or semi-closed-loop artificial pancreas.

[0006] Current closed-loop artificial pancreas systems can only achieve closed-loop control when patients have high blood sugar and require insulin infusion. They cannot achieve closed-loop control for the infusion of blood sugar-raising drugs in patients with hypoglycemia. Similarly, for the same patient, hypoglycemia or hyperglycemia may occur at different times, and current artificial pancreas systems cannot achieve full closed-loop control for multiple drug infusions.

[0007] Therefore, there is an urgent need for a fully closed-loop artificial pancreas drug infusion control system that can achieve closed-loop control of multi-drug infusion. Summary of the Invention

[0008] The embodiment of the present application discloses a full closed loop artificial pancreas drug infusion control system, which comprises a program module, at least one detection module, a hypoglycemic drug infusion module and a hyperglycemic drug infusion module, the program module is provided with an algorithm, the detection module detects blood glucose value, the hypoglycemic drug infusion module performs hypoglycemic drug infusion according to the infusion indication calculated by the algorithm, and the hyperglycemic drug infusion module performs hyperglycemic drug infusion according to the infusion indication calculated by the algorithm, so that full closed loop control of multiple drugs is realized.

[0009] The present application discloses a full closed loop artificial pancreas drug infusion control system, which comprises a program module, at least one detection module, a hypoglycemic drug infusion module and a hyperglycemic drug infusion module, the program module is provided with an algorithm, the detection module detects blood glucose value, the hypoglycemic drug infusion module performs hypoglycemic drug infusion according to the infusion indication calculated by the algorithm, and the hyperglycemic drug infusion module performs hyperglycemic drug infusion according to the infusion indication calculated by the algorithm, so that full closed loop control of multiple drugs is realized.

[0010] According to one aspect of the present application, two of the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are connected or integrated into one whole.

[0011] According to one aspect of the present application, the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are respectively separate different structures.

[0012] According to one aspect of the present application, the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are respectively separate different structures.

[0013] According to one aspect of the present application, the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are respectively separate different structures.

[0014] According to one aspect of the present application, the algorithm is one or more of a classic MPC algorithm, a classic PID algorithm, an rMPC algorithm, an rPID algorithm or a composite artificial pancreas algorithm.

[0015] According to one aspect of the present application, the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm comprises one or more of a piecewise weighting method, a relative value conversion, a blood glucose risk index conversion and an improved control variability grid analysis conversion.

[0016] According to one aspect of the present application, the composite artificial pancreas algorithm comprises a first algorithm and a second algorithm, the first algorithm is used to calculate a first insulin infusion amount I1, the second algorithm is used to calculate a second insulin infusion amount I2, and the composite artificial pancreas algorithm obtains a final insulin infusion amount I3 by optimizing calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2.

[0017] According to one aspect of the present application, the program module is arranged in the external electronic device.

[0018] According to one aspect of the present application, the program module is arranged in any one of the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module.

[0019] According to one aspect of the present application, the program module is a separate structure.

[0020] According to one aspect of the present application, the program module is connected to one or more of the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module to form an integral whole.

[0021] According to one aspect of the present application, when one of the detection modules fails or is in a hot start state, the other detection module performs detection.

[0022] According to one aspect of the present application, the program module processes the blood glucose value after receiving the blood glucose value detected by the two detection modules.

[0023] According to one aspect of the present application, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are integrated in the same structure, and the hypoglycemic drug infusion module and the hyperglycemic drug infusion module share the same drug infusion pipeline.

[0024] According to one aspect of the present application, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are integrated in the same structure, and the hypoglycemic drug infusion module and the hyperglycemic drug infusion module use different drug infusion pipelines for drug infusion.

[0025] According to one aspect of the present application, the hypoglycemic drug infusion module infuses insulin, and the hyperglycemic drug infusion module infuses glucagon.

[0026] Compared with the prior art, the technical scheme of the present application has the following advantages:

[0027] The present application discloses a closed-loop artificial pancreas multi-drug infusion control system, which comprises a program module, a detection module, a hypoglycemic drug infusion module and a hyperglycemic drug infusion module. An algorithm is preset in the program module. The detection module detects blood glucose values. The hypoglycemic drug infusion module infuses hypoglycemic drugs according to the infusion instructions calculated by the algorithm. The hyperglycemic drug infusion module infuses hyperglycemic drugs according to the infusion instructions calculated by the algorithm, thereby realizing closed-loop control of the artificial pancreas multi-drug infusion system.

[0028] Further, the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are arranged in various manners, and the three modules can be arranged in one structure, or any two modules can be arranged in one structure, or the three modules can be arranged in different structures, and the arrangement can be made according to actual needs to improve user experience.

[0029] Further, two detection modules are arranged in the system, one detection module is connected with or integrated into the hypoglycemic drug infusion module to form one whole, and the other detection module is connected with or integrated into the hyperglycemic drug infusion module to form another whole. On one hand, the program module processes the blood glucose value after receiving the blood glucose value detected by the two detection modules, thereby improving the accuracy of drug infusion. On the other hand, when one of the detection modules fails or is in a hot start state, the other detection module detects, thereby ensuring the continuity of blood glucose detection.

[0030] Further, the program module can be arranged in an external electronic device such as a mobile phone, a handset or an electronic watch, and can be connected with or integrated into other modules to form one whole.

[0031] Further, one or more of the rMPC algorithm, the rPID algorithm and the compound artificial pancreas algorithm for converting the blood glucose in the original physical space to the blood glucose in the approximate symmetric risk space are preset in the program module. The advantages of the rPID algorithm and the rMPC algorithm are fully utilized to face complex situations, so that the artificial pancreas can provide reliable drug types and drug infusion amounts for controlling blood glucose in various situations, thereby making the blood glucose reach an ideal level and realizing precise control of the closed-loop artificial pancreas multi-drug infusion system.

[0032] Further, the final output of the compound artificial pancreas algorithm is the result after optimization of the results calculated by the two different algorithms, and the result is more feasible and reliable. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A schematic diagram of module relationships of a closed-loop artificial pancreas insulin infusion control system according to one embodiment of the present application;

[0034] Figure 2 A comparison diagram of relationships between blood glucose in a risk space obtained by a segmented weighted processing and a relative value conversion method and blood glucose in an original physical space according to one embodiment of the present application;

[0035] Figure 3 A comparison diagram of relationships between blood glucose in a risk space obtained by a BGRI and a CVGA method and blood glucose in an original physical space according to one embodiment of the present application;

[0036] Figure 4 An insulin IOB curve according to one embodiment of the present application;

[0037] Figure 5 A schematic diagram of the relationship between modules of a full closed-loop artificial pancreas insulin infusion control system according to an embodiment of the application;

[0038] Figure 6 A schematic diagram of a dual drug switching according to an embodiment of the application. DETAILED DESCRIPTION

[0039] As mentioned previously, prior art artificial pancreases have not been able to achieve closed-loop control of a glucose-raising drug, and have not been able to achieve full closed-loop control of a multi-drug infusion of a glucose-raising drug and a glucose-lowering drug.

[0040] To solve this problem, the present application provides a full closed-loop artificial pancreas drug infusion control system, which comprises a program module, a detection module, a glucose-lowering drug infusion module and a glucose-raising drug infusion module. The program module has an algorithm preset therein. The detection module detects a blood glucose value. The glucose-lowering drug infusion module infuses a blood glucose-lowering drug according to an infusion instruction calculated by the algorithm. The glucose-raising drug infusion module infuses a blood glucose-raising drug according to an infusion instruction calculated by the algorithm, thereby achieving full closed-loop control of a multi-drug infusion of an artificial pancreas.

[0041] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be appreciated that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments should not be construed as limiting the scope of the present application unless specifically stated otherwise.

[0042] Furthermore, it should be understood that the dimensions of the various components shown in the drawings are not necessarily to scale, for example, the thickness, width, length or distance of certain units can be exaggerated relative to other structures for the purpose of convenience of description.

[0043] The following description of the exemplary embodiments is merely illustrative in nature and is in no way intended to limit the scope of the application, its application, or uses. Techniques, methods, and apparatus known to those of ordinary skill in the art can not be discussed in detail, but should be understood to be a part of the specification to the extent that these techniques, methods, and apparatus are applicable to the practice of the present application.

[0044] It should be noted that like reference numerals and letters refer to like items in the several views of the drawings, and as a result, further discussion of such items in subsequent views is not necessary.

[0045] Figure 1 A schematic diagram of the relationship between modules of a full closed-loop artificial pancreas insulin infusion control system according to an embodiment of the application;

[0046] The closed-loop artificial pancreas insulin infusion control system disclosed by the embodiment of the present application mainly comprises a detection module 100, a program module 101 and an infusion module 102.

[0047] The detection module 100 is used for continuously detecting the current blood glucose value of a user. Generally, the detection module 100 is a continuous glucose monitor (CGM), which can detect the current blood glucose value of the user in real time and monitor the blood glucose change, and send the current blood glucose value to the program module 101.

[0048] The program module 101 is used for controlling the work of the detection module 100 and the infusion module 102, and comprises components such as memory and processor required for realizing the control function. Therefore, the program module 101 is connected with the detection module 100 and the infusion module 102 respectively. Here, the connection includes conventional electrical connection or wireless connection.

[0049] The infusion module 102 comprises mechanical structures required for infusing insulin, such as a medicine storage cartridge for storing medicine, a medicine infusion pipeline comprising an infusion needle for infusing medicine into the user, a driving component for transferring medicine from the medicine storage cartridge to the user through the medicine infusion pipeline, etc. Specifically, the infusion module 102 can be an insulin patch pump of the Medtronic company, and is controlled by the program module 101. According to the current insulin infusion amount data sent by the program module 101, the infusion module 102 infuses the current required insulin into the user. At the same time, the infusion state of the infusion module 102 can also be fed back to the program module 101 in real time.

[0050] The embodiment of the present application does not limit the specific positions and connection relationships of the detection module 100, the program module 101 and the infusion module 102, as long as the above-mentioned functional conditions can be met.

[0051] In one embodiment of the present application, the three are electrically connected or integrated with each other to form an integral structure. Therefore, the three are pasted at the same position on the skin of the user. The three modules are connected into an integral whole and pasted at the same position, so that the number of devices pasted on the skin of the user is reduced, thereby weakening the interference of pasting more devices on the activities of the user; at the same time, the problem of reliability of wireless communication between separated devices is effectively solved, and the user experience is further enhanced.

[0052] In another embodiment of the present application, the program module 101 and the infusion module 102 are connected or integrated with each other to form an integral structure, and the detection module 100 is separately arranged in another structure. At this time, the detection module 100 and the program module 101 transmit wireless signals to each other to realize the connection with each other. Therefore, the program module 101 and the infusion module 102 are pasted at a certain position on the skin of the user, and the detection module 100 is pasted at another position on the skin of the user.

[0053] As in still another embodiment of the present application, the program module 101 is interconnected with the detection module 100 or integrated into one device, while the infusion module 102 is separately arranged in another structure. The program module 101 and the infusion module 102 transmit wireless signals to each other to achieve connection. Thus, the program module 101 and the detection module 100 can be attached to a certain position of the user's skin, while the infusion module 102 can be attached to another position of the user's skin.

[0054] As in still another embodiment of the present application, the three are arranged in different structures respectively. Thus, the three are attached to different positions of the user's skin respectively. At this time, the program module 101 transmits wireless signals to the detection module 100 and the infusion module 102 respectively to achieve connection.

[0055] It should be noted that the program module 101 of the embodiment of the present application also has the functions of storage, recording and access to the database, and thus the program module 101 can be reused. In this way, not only the user's physical condition data can be stored, but also the production cost and the user's use cost can be saved. As described above, when the detection module 100 or the infusion module 102 reaches the end of life, the program module 101 can be separated from the detection module 100 and the infusion module 102 respectively or simultaneously.

[0056] Generally, the service life of the detection module 100, the program module 101 and the infusion module 102 is different. Thus, when the three are electrically connected to form one device, the three can also be separated from each other in pairs. For example, when one module reaches the end of life, the user can only replace the module, and keep the other two modules for continuous use.

[0057] Here, it should be noted that the program module 101 of the embodiment of the present application can also include multiple sub-modules. According to the functions of the sub-modules, different sub-modules can be arranged in different structures, which are not specifically limited here, as long as the control conditions of the program module 101 can be met.

[0058] Specifically, the program module 101 is pre-provided with an rPID (risk-proportional-integral-derivative) algorithm for converting blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space. The rPID algorithm is obtained by converting the classical PID (proportional-integral-derivative) algorithm, and the specific processing manner will be described in detail below. According to the corresponding infusion instructions calculated by the rPID algorithm, the program module 101 controls the infusion module 102 to infuse insulin.

[0059] The classical PID algorithm can be represented by the following formula:

[0060]

[0061] wherein:

[0062] K P is a gain coefficient of the proportional part;

[0063] K I is a gain coefficient of the integral part;

[0064] K D is a gain coefficient of the derivative part;

[0065] G represents a current blood glucose value;

[0066] G B represents a target blood glucose value;

[0067] C represents a constant;

[0068] PID(t) represents an infusion instruction sent to an insulin infusion system;

[0069] t represents a current time.

[0070] Considering the actual distribution characteristics of glucose concentration of a diabetic patient, such as a normal blood glucose range of 80-140 mg / dL, which can also be relaxed to 70-180 mg / dL, a general low blood glucose can reach 20-40 mg / dL, and a high blood glucose can reach 400-600 mg / dL.

[0071] The distribution of high / low blood glucose has significant asymmetry in the original physical space, and the same degree of deviation of blood glucose from the normal range corresponds to significantly different high blood glucose risk and low blood glucose risk in clinical practice, such as a decrease of 70 mg / dL from 120 mg / dL to 50 mg / dL, which is considered to be severe hypoglycemia with high clinical risk, and emergency measures such as supplement of carbohydrates need to be taken; while an increase of 70 mg / dL from 120 mg / dL to 190 mg / dL just exceeds the normal range, and the degree of high blood glucose is not serious for a diabetic patient, and it is often reached in daily situations, and basically no treatment measures need to be taken.

[0072] In view of the asymmetric characteristics of the clinical risk of glucose concentration, the asymmetric blood glucose in the original physical space is converted to the blood glucose risk in the risk space which is approximately symmetric, so that the PID algorithm is more robust.

[0073] Correspondingly, the rPID algorithm formula is converted into the following form:

[0074]

[0075] wherein:

[0076] rPID(t) represents the infusion indication sent to the insulin infusion system after risk conversion;

[0077] r represents the blood glucose risk;

[0078] Other symbols represent the meaning as described above.

[0079] In order to maintain the stability of the PID integral, combined with the physiological effect of insulin in reducing blood glucose, in an embodiment of the present application, the input parameter of the PID, the blood glucose deviation Ge=G-G B is processed, such as Ge=G-G B is segmented and weighted, as follows:

[0080]

[0081] In another embodiment of the present application, the relative value is used for conversion for the deviation greater than the target blood glucose G B , as follows:

[0082]

[0083] Figure 2 The blood glucose risk space obtained by the segmented weighting processing and the relative value conversion is compared with the blood glucose relationship in the original physical space.

[0084] In the original PID algorithm, the blood glucose risk (i.e. Ge) on both sides of the target blood glucose value presents a serious asymmetry consistent with the original physical space, and after conversion to the blood glucose risk space, the blood glucose risk on both sides of the target blood glucose value is approximately symmetrical, so that the integral term can remain stable, making the rPID algorithm more robust.

[0085] In another embodiment of the present application, there is a fixed zero risk point in the risk conversion, and the data deviating from both sides of the zero risk point is processed. The original parameter corresponding to the greater than zero risk point is positive when converted to the risk space, and the original parameter corresponding to the less than zero risk is negative when converted to the risk space. Specifically, the classic blood glucose risk index (BGRI) method can be used for reference, which is based on clinical practice and considers that the clinical risk of hypoglycemia of 20 mg / dL and hyperglycemia of 600 mg / dL is equivalent, and the blood glucose in the range of 20-600 mg / dL is processed as a whole by logarithmization. The blood glucose value corresponding to the zero risk point of this method is set as the target blood glucose value G B . Its risk space conversion formula is as follows:

[0086]

[0087] Wherein:

[0088] r(G) = 10*f(G) 2

[0089] The conversion function f(G) is as follows:

[0090] f(G) = 1.509 * [(ln(G)) 1.084 - 5.381]

[0091] In the classic blood glucose risk index method, the blood glucose value corresponding to the zero risk point of the method is 112 mg / dL. In other embodiments of the present application, the zero risk point blood glucose value can also be adjusted in combination with the risk and data trends of clinical practice, which is not specifically limited here. The risk space of the blood glucose value greater than the zero risk point of the blood glucose value is fitted, and the specific fitting method is not specifically limited.

[0092] In another embodiment of the present application, the improved Control Variability Grid Analysis (CVGA) method is used for reference. The zero risk point blood glucose value defined by the original CVGA is 110 mg / dL, and the following equal risk blood glucose value data pairs (90 mg / dL, 180 mg / dL; 70 mg / dL, 300 mg / dL; 50 mg / dL, 400 mg / dL) are assumed. In the embodiment of the present application, the real risk and data trend characteristics of clinical practice are considered, and the (70 mg / dL, 300 mg / dL) equal risk data pair is modified to (70 mg / dL, 250 mg / dL), and the zero risk point blood glucose value is set as the target blood glucose value G B . A polynomial model fitting is performed thereon, and the following risk function processed on both sides of the zero risk point is obtained:

[0093]

[0094] And the maximum value thereof is limited:

[0095] |r| = min(|r|, n)

[0096] The value range of the limiting maximum value n is 0-80 mg / dL, and the value of n is preferably 60 mg / dL.

[0097] In other embodiments of the present application, the zero risk point blood glucose value and the equal risk data pair can also be adjusted in combination with the real risk and data trends of clinical practice, which is not specifically limited here. The equal risk point is fitted, and the specific fitting method is not specifically limited. The specific numerical value for limiting the maximum value is also not specifically limited.

[0098] Figure 3 The figure is a comparison of the relationship between the blood glucose risk converted to the risk space by the BGRI and CVGA methods and the blood glucose in the original physical space.

[0099] Similar to the processing of Zone-MPC, the blood glucose risk converted by BGRI and CVGA methods is relatively flat within the normal range of blood glucose, especially within 80-140 mg / dL. Unlike Zone-MPC, which is completely 0 within this range and loses the ability to further optimize, the risk of rPID is flat within this range, but still has stable and slow adjustment ability, which can further adjust blood glucose to the target value and achieve more accurate blood glucose control.

[0100] In another embodiment of the present application, a unified processing method can be used for data deviating from both sides of the zero risk point. As in the foregoing embodiment, the data deviating from both sides of the zero risk point can be processed by BGRI or CVGA method; or different processing methods can be used, such as combining BGRI and CVGA methods at the same time. At this time, the same zero risk point blood glucose value can be used, such as the target blood glucose value G B When the blood glucose value is less than the target blood glucose value G B , the BGRI method is used, and when the blood glucose value is greater than the target blood glucose value G B , the CVGA method is used. At this time:

[0101] r = -r(G), if G ≤ G B

[0102] Wherein:

[0103] r(G) = 10*f(G) 2

[0104] The conversion function f(G) is as follows:

[0105] f(G) = 1.509*[(ln(G) 1.084 -5.381]

[0106] r = -4.8265*10 4 -4*G 2 +0.45563*G-44.855, if G > G B .

[0107] Similarly, the CVGA method can be used when the blood glucose value is less than the target blood glucose value G B , and the BGRI method can be used when the blood glucose value is greater than the target blood glucose value G B . At this time:

[0108] r = r(G), if G > G B

[0109] Wherein:

[0110] r(G) = 10*f(G)2

[0111] Under the conversion function f(G):

[0112] f(G) = 1.509 * [(ln(G)) 1.084 - 5.381]

[0113] r = G - G B , if G < G B .

[0114] At the same time, the maximum value can also be limited:

[0115] |r| = min(|r|, n)

[0116] Wherein the value range of the maximum value n is 0-80 mg / dL, preferably, the value of n is 60 mg / dL.

[0117] In other embodiments of the present application, the blood glucose value of the zero risk point can also be set as the target blood glucose value G B , the BGRI method is used for data less than or equal to the target blood glucose value G B , and the deviation amount processing method is used for data greater than the target blood glucose value G B , specifically, such as piecewise weighted processing or relative value processing.

[0118] When using piecewise weighted processing, at this time:

[0119] r = -r(G), if G < G B

[0120] Wherein:

[0121] r(G) = 10*f(G) 2

[0122] Under the conversion function f(G):

[0123] f(G) = 1.509 * [(ln(G)) 1.084 - 5.381]

[0124]

[0125] When using relative value processing:

[0126] r = -r(G), if G < G B

[0127] Wherein:

[0128] r(G) = 10*f(G) 2

[0129] Under the fitted symmetric conversion function f(G):

[0130] f(G) = 1.509 * [(ln(G)) 1.084 - 5.381]

[0131] r = 100 * (G - G B ) / G, if G > G B

[0132] When the zero risk point corresponds to the blood glucose value G B , for the data less than or equal to the target blood glucose value G B , the processing functions are consistent when using the segmented weighted processing, relative value processing and CVGA method, therefore, when the segmented weighted processing or the relative value processing is adopted for the data less than or equal to the target blood glucose value G B , and the BGRI method is adopted for the data greater than the zero risk point blood glucose value, the processing results are equivalent to the aforementioned CVGA method when the blood glucose value is less than or equal to the target blood glucose value G B , and the BGRI method when the blood glucose value is greater than the target blood glucose value G B , the calculation formula is not repeated.

[0133] It should be noted that in each embodiment of the present application, the target blood glucose value G B is 80-140 mg / dL, preferably, the target blood glucose value G B is 110-120 mg / dL.

[0134] The above processing methods can make the rPID algorithm convert the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, so as to not only retain the simple and robust characteristics of the PID algorithm, but also have the targeted and clinically valuable blood glucose risk control function, and realize the precise control of the closed-loop artificial pancreas insulin infusion system.

[0135] In the closed-loop artificial pancreas control system, there are three major delay effects: insulin absorption delay (about 20 minutes from subcutaneous to blood circulation tissue, about 100 minutes to reach the liver), insulin onset delay (about 30-100 minutes), and sensing delay of interstitial fluid glucose concentration and blood glucose (about 5-15 minutes). Any attempt to accelerate the responsiveness of the closed-loop system can lead to unstable system behavior and system oscillation. In order to compensate for the insulin absorption delay in the closed-loop artificial pancreas control system, in an embodiment of the present application, an insulin feedback compensation mechanism is introduced. The amount of insulin in the body that has not been absorbed is deducted from the output, a component proportional to the estimated plasma insulin concentration (the actual human insulin secretion also uses the insulin concentration in the blood as a negative feedback signal). The formula is as follows:

[0136]

[0137] wherein:

[0138] PID(t) represents the infusion indication sent to the insulin infusion system;

[0139] PID c (t) represents the compensated infusion indication sent to the insulin infusion system;

[0140] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output, the coefficient becomes larger, the algorithm becomes relatively conservative, and the coefficient becomes smaller, the algorithm becomes relatively aggressive, therefore, in the embodiment of the present application, γ ranges from 0.4 to 0.6, preferably, γ is 0.5.

[0141] represents the estimation of the plasma insulin concentration, which can be obtained by various conventional prediction algorithms, such as directly calculated from the infused insulin according to the pharmacokinetic curve of insulin, or using the conventional autoregressive method:

[0142]

[0143] wherein:

[0144] represents the estimation of the plasma insulin concentration at the current time;

[0145] PID c (n-1) represents the output at the last time with compensation;

[0146] represents the estimation of the plasma insulin concentration at the last time;

[0147] represents the estimation of the plasma insulin concentration at the time before the last time;

[0148] K0 represents the coefficient of the output part at the last time with compensation;

[0149] K1 represents the coefficient of the estimation part of the plasma insulin concentration at the last time;

[0150] K2 represents the coefficient of the estimation part of the plasma insulin concentration at the time before the last time;

[0151] wherein, the initial value is The time interval between each time can be selected according to actual needs.

[0152] Correspondingly, the compensation output formula after the risk conversion by the foregoing method is as follows:

[0153]

[0154] in:

[0155] rPIDc(t) represents the compensated infusion instruction sent to the insulin infusion system after risk conversion;

[0156] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0157] The meanings of the other characters are as described above.

[0158] To compensate for the delayed onset of insulin in the closed-loop artificial pancreas control system, in one embodiment of the present invention, insulin IOB (insulin on board) that has not yet taken effect in the body is introduced. IOB is deducted from the insulin output to prevent insulin accumulation and overdose, which could lead to risks such as postprandial hypoglycemia.

[0159] Figure 4 This is the insulin IOB curve according to an embodiment of the present invention.

[0160] according to Figure 4 The IOB curve shown can be used to calculate the cumulative residual amount of previously infused insulin. The specific curve can be selected based on the user's actual insulin action time.

[0161] PID′(t) = PID(t) - IOB(t)

[0162] in:

[0163] PID'(t) represents the infusion instruction sent to the insulin infusion system after deducting IOB;

[0164] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0165] IOB(t) represents the amount of insulin in the body that has not yet taken effect at time t.

[0166] Accordingly, the output formula after risk conversion using the aforementioned method, minus the amount of insulin that has not yet taken effect in the body, is as follows:

[0167] rPID′(t)=rPID(t)-IOB(t)

[0168] in:

[0169] rPID′(t) represents the infusion instruction sent to the insulin infusion system after risk conversion, excluding the amount of insulin that has not yet taken effect in the body;

[0170] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0171] The meaning of other characters is as described above.

[0172] In order to obtain more ideal control effect, the calculation of IOB is processed as follows, IOB m , IOB o respectively correspond to IOB of meal insulin and other insulin except meal insulin. The formula is as follows:

[0173] IOB(t) = IOB m，t + IOB o，t

[0174] Wherein:

[0175]

[0176] Wherein:

[0177] IOB m，t represents the amount of meal insulin that has not yet acted in the body at time t;

[0178] IOB o，t represents the amount of non-meal insulin that has not yet acted in the body at time t;

[0179] D i (i = 2-8) respectively represent the corresponding coefficients of the IOB curve corresponding to the insulin action time i;

[0180] I m，t represents the amount of meal insulin;

[0181] I 0，t represents the amount of non-meal insulin;

[0182] IOB(t) represents the amount of insulin that has not yet acted in the body at time t.

[0183] The distinction between meal insulin and non-meal insulin for IOB can make insulin be cleared faster when meal or blood glucose is too high, can obtain greater insulin output, and blood glucose regulation is faster. When close to the target, longer insulin action time curve is used to make insulin be cleared slower, and blood glucose regulation is more conservative and stable.

[0184] When PID'(t) > 0 or rPID'(t) > 0, the final amount of insulin infusion is PID'(t) or rPID'(t);

[0185] When PID'(t) < 0 or rPID'(t) < 0, the final amount of insulin infusion is 0.

[0186] In order to compensate the sensing delay of tissue fluid glucose concentration and blood glucose in the closed-loop artificial pancreas control system, in an embodiment of the present application, an autoregressive method is adopted for compensation, and the formula is as follows:

[0187]

[0188] Wherein:

[0189] G SC (n) represents the current time tissue interstitial fluid glucose concentration, that is, the measurement value of the sensing system;

[0190] represents the estimated concentration of blood glucose at the last time;

[0191] G SC (n-1) and G SC (n-2) represent the tissue interstitial fluid glucose concentration at the last time and the time before the last time respectively;

[0192] K3 represents the coefficient of the estimated concentration of blood glucose at the last time;

[0193] K4 and K5 represent the coefficients of the tissue interstitial fluid glucose concentration at the last time and the time before the last time respectively.

[0194] Wherein, at the initial time,

[0195] By estimating the blood glucose concentration through the tissue interstitial fluid glucose concentration, the sensing delay of tissue fluid glucose concentration and blood glucose is compensated, so that the PID algorithm is more accurate, and the rPID algorithm can also more accurately calculate the actual demand of the human body for insulin.

[0196] In the embodiment of the present application, the insulin absorption delay, the insulin onset delay, and the sensing delay of tissue fluid glucose concentration and blood glucose can be partially compensated or fully compensated, preferably, all delay factors are considered for full compensation, so that the rPID algorithm is more accurate.

[0197] In another embodiment of the present application, the program module 101 is pre-provided with an rMPC (risk-model-predictive-control) algorithm for converting the blood glucose which is asymmetric in the original physical space to the blood glucose risk which is approximately symmetric in the risk space, the rMPC algorithm is obtained by converting processing on the basis of the classical MPC (model-predictive-control) algorithm, and the corresponding infusion instruction calculated according to the rMPC algorithm is used by the program module 101 to control the infusion module 102 to infuse insulin.

[0198] The classical MPC algorithm is composed of three elements, a prediction model, a value function and a constraint condition. The prediction model of the classical MPC is as follows:

[0199] x t+1 = Ax t + BI t

[0200] G t = Cx t

[0201] wherein:

[0202] x t+1 denotes the state parameter at the next time instant,

[0203] x t denotes the state parameter at the current time instant,

[0204] I t denotes the insulin infusion at the current time instant;

[0205] G t denotes the blood glucose concentration at the current time instant.

[0206] The parameter matrix is as follows:

[0207]

[0208]

[0209] C = [1 0 0]

[0210] b1, b2, b3, K are prior values.

[0211] The value function of the MPC is composed of the square sum of the deviation of the output G (blood glucose value) and the square sum of the change of the input I (insulin amount). The MPC needs to obtain the minimum solution of the value function, wherein the value function is as follows:

[0212]

[0213] wherein:

[0214] I' t+j denotes the change of the insulin infusion after the jth step;

[0215] denotes the difference between the predicted blood glucose concentration after the jth step and the target blood glucose value;

[0216] t denotes the current time instant;

[0217] N, P are respectively the step number in the control time window and the prediction time window;

[0218] R is the weighted coefficient of the insulin component.

[0219] The insulin infusion amount of the jth step is I t + I' t+j .

[0220] In the embodiment of the present application, the control time window T c = 30 min, the prediction time window T p = 60 min, and the weighting coefficient R of the insulin amount is 11000. It should be noted that although the control time window of 30 min is used in the calculation, only the first step operation result of the insulin output is used in the actual operation, and after the operation, the minimum solution of the above value function is recalculated according to the latest blood glucose value obtained.

[0221] In the embodiment of the present application, the infusion time step in the control time window is j n , the value range of j n is 0-30 min, and preferably 2 min. The number of steps N = T c / j n , and the range of j is 0 to N.

[0222] In other embodiments of the present application, the control time window, the prediction time window, and the weighting coefficient of the insulin amount can also be selected as other values, which are not specifically limited here.

[0223] As described above, since the distribution (original physical space) of high / low blood glucose has significant asymmetry, the risk of high blood glucose and the risk of low blood glucose corresponding to the same deviation of blood glucose from the normal range in clinical practice are obviously different. In view of the asymmetric characteristics of the clinical risk of glucose concentration, the blood glucose in the original physical space is converted to the blood glucose risk in the risk space which is approximately symmetric, so that the MPC algorithm is more accurate and flexible. The value function of the rMPC algorithm after the risk conversion is as follows:

[0224]

[0225] Wherein:

[0226] r t+j represents the blood glucose risk value after the jth step;

[0227] I' t+j represents the change of the insulin infusion amount after the jth step.

[0228] The deviation of the blood glucose value is converted into the corresponding blood glucose risk, and the specific conversion method is the same as that in the rPID algorithm, such as the piecewise weighting processing and the relative value processing; it also includes setting a fixed zero risk point in the risk space, and the blood glucose concentration of the zero risk point can be set as the target blood glucose value. The data deviating from both sides of the zero risk point is processed, such as using the BGRI and the improved CVGA method; it also includes using different methods to process the data deviating from both sides of the target blood glucose value.

[0229] In particular, when using the segmented weighting process:

[0230]

[0231] wherein:

[0232] G t+j represents the blood glucose value detected at the jth step.

[0233] When using the relative value process:

[0234]

[0235] When using the classic blood glucose risk index method:

[0236]

[0237] wherein:

[0238] r(G t+j ) = 10*f(G t+j ) 2

[0239] The conversion function f(G t+j ) is as follows:

[0240] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 - 5.381]

[0241] When using the control variability grid analysis method:

[0242]

[0243] The maximum value thereof is also limited:

[0244] |r t+j | = min(|r t+j |, n)

[0245] wherein the value range of the maximum value n is 0-80 mg / dL, and preferably the value of n is 60 mg / d.

[0246] When the blood glucose value is less than the target blood glucose value G B , the BGRI method is used, and when the blood glucose value is greater than the target blood glucose value G B , the CVGA method is used:

[0247] r t+j = -r(G t+j ), if G t+j ≤ GB

[0248] where:

[0249] r(G t+j ) = 10*f(G t+j ) 2

[0250] The conversion function f(G t+j ) is as follows:

[0251] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 -5.381]

[0252] r t+j = -4.8265*10 4 -4*G t+j 2 +0.45563*G t+j -44.855, if G t+j > G B

[0253] When the CVGA method is used when the blood glucose value is less than the target blood glucose value G B and the BGRI method is used when the blood glucose value is greater than the target blood glucose value G B :

[0254] r t+j = r(G t+j ), if G t+j > G B

[0255] where:

[0256] r(G t+j ) = 10*f(G t+j ) 2

[0257] The conversion function f(G t+j ) is as follows:

[0258] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 -5.381]

[0259] r t+j = G t+j - G B , if G t+j ≤ G B .

[0260] The maximum value can also be limited simultaneously:

[0261] |r t+j |=min(|r t+j |,n)

[0262] where n is defined as the maximum value in the range of 0-80 mg / dL, preferably n is 60 mg / dL.

[0263] When the blood glucose value is less than the target blood glucose value G B , the BGRI method is used, and when the blood glucose value is greater than the target blood glucose value G B , the piecewise weighted method is used:

[0264] r t+j =-r(G t+j ), if G t+j ≤G B

[0265] where:

[0266] r(G t+j )=10*f(G t+j ) 2

[0267] The conversion function f(G t+j ) is as follows:

[0268] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0269]

[0270] When the blood glucose value is less than the target blood glucose value G B , the BGRI method is used, and when the blood glucose value is greater than the target blood glucose value G B , the relative value conversion is used:

[0271] r t+j =-r(G t+j ), if G t+j ≤G B

[0272] where:

[0273] r(G t+j )=10*f(G t+j ) 2

[0274] The conversion function f(G t+j ) is as follows:

[0275] f(G t+j) = 1.509 * [(ln(G t+j ) 1.084 -5.381]

[0276]

[0277] When the data less than or equal to the target blood glucose value G B is processed by the segmented weighted processing or the relative value processing, and the data greater than the zero risk point blood glucose value is processed by the BGRI method, the processing result is equivalent to the aforementioned CVGA method when the blood glucose value is less than or equal to the target blood glucose value G B , and the BGRI method when the blood glucose value is greater than the target blood glucose value G B , and the calculation formula is not repeated.

[0278] It should be noted that in the above various conversion formulas:

[0279] r t+j is the blood glucose risk value at the jth step;

[0280] G t+j is the detected blood glucose value at the jth step.

[0281] The target blood glucose value G B is 80-140 mg / dL, and preferably, the target blood glucose value G B is 110-120 mg / dL.

[0282] The beneficial effects after risk conversion and the relationship between blood glucose and blood glucose risk are consistent with the rPID algorithm, and are not repeated here.

[0283] Similarly, in order to compensate for the insulin absorption delay, an insulin feedback compensation mechanism can also be used for compensation; in order to make up for the delay in the effect of insulin, an IOB compensation can also be used; the sensing delay of interstitial fluid glucose concentration and blood glucose concentration can also be compensated by autoregressive compensation, and the specific compensation methods are consistent with the rPID algorithm, and specifically:

[0284] For insulin absorption delay, the compensation formula is as follows:

[0285]

[0286] Among them:

[0287] I t+j represents the infusion instruction sent to the insulin infusion system at the jth step;

[0288] rI c(t+j) represents the infusion instruction sent to the insulin infusion system at the jth step after risk conversion;

[0289] γ represents the compensation factor of the estimated plasma insulin concentration to the algorithm output, the coefficient becomes larger, the algorithm becomes relatively conservative, and the coefficient becomes smaller, the algorithm becomes relatively aggressive, therefore, in the embodiment of the present application, the range of γ is 0.4-0.6, preferably, γ is 0.5.

[0290] represents the estimation of the plasma insulin concentration at the jth step.

[0291] For the delay of insulin onset, the compensation formula is as follows:

[0292] rI′ t+j = rI t+j - IOB(t + j)

[0293] Wherein:

[0294] rI′ t+j represents the infusion indication sent to the insulin infusion system after deducting IOB at the jth step after risk conversion;

[0295] rI t+j represents the infusion indication sent to the insulin infusion system at the jth step after risk conversion;

[0296] IOB(t + j) represents the amount of insulin in the body that has not yet taken effect at t + j.

[0297] Similarly, the meal and non-meal can also be distinguished for IOB(t + j), at this time:

[0298] IOB(t + j) = IOB m，t+j + IOB o，t+j

[0299] Wherein:

[0300]

[0301] Wherein:

[0302] IOB m，t+j represents the amount of meal insulin in the body that has not yet taken effect at t + j;

[0303] IOB o，t+j represents the amount of non-meal insulin in the body that has not yet taken effect at t + j;

[0304] D i (i = 2-8) represents the corresponding coefficient of the IOB curve corresponding to the insulin action time i respectively;

[0305] I m，t+j represents the amount of meal insulin at t + j;

[0306] I 0，t+jThis represents the non-meal insulin amount at time t+j;

[0307] IOB(t+j) represents the amount of insulin in the body at time t+j that has not yet taken effect.

[0308] When rI′ t+j When >0, the final infused insulin volume is rI′. t+j ;

[0309] When rI′ t+j When <0, the final amount of insulin infused is 0.

[0310] For the sensing delay of tissue fluid glucose concentration and blood glucose concentration, autoregressive compensation can also be used, as shown in the following formula:

[0311]

[0312] in:

[0313] G SC (t+j) represents the glucose concentration in the interstitial fluid at time t+j, which is the measured value of the sensing system;

[0314] This represents the estimated blood glucose concentration at time t+j-1;

[0315] G SC (t+j-1) and G SC (t+j-2) represent the glucose concentrations in the interstitial fluid at times t+j-1 and t+j-2, respectively;

[0316] K6 represents the coefficient of the estimated blood glucose concentration at time t+j-1;

[0317] K7 and K8 represent the coefficients of interstitial fluid glucose concentration at time t+j-1 and t+j-2, respectively.

[0318] At the initial moment,

[0319] The beneficial effects of various compensation methods are consistent with those of the rPID algorithm, and will not be repeated here.

[0320] It should be noted that, in the rMPC algorithm, it is preferable to compensate for the delay in insulin onset and the sensing delay in tissue fluid glucose concentration and blood glucose concentration.

[0321] In another embodiment of the present application, the program module 101 is preset with a composite artificial pancreas algorithm, the composite artificial pancreas algorithm comprises a first algorithm and a second algorithm, when the detection module 100 detects the current blood glucose value and sends the current blood glucose value to the program module 101, the first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, the composite artificial pancreas algorithm optimizes and calculates the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain a final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module 102, and the infusion module 102 performs insulin infusion according to the final infusion amount I3.

[0322] The first algorithm and the second algorithm are one of a classic PID algorithm, a classic MPC algorithm, an rMPC algorithm or an rPID algorithm. The rMPC algorithm or the rPID algorithm is an algorithm for converting blood glucose which is asymmetric in an original physical space to blood glucose risk which is approximately symmetric in a risk space. The conversion mode of the blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.

[0323] When I1 = I2, I3 = I1 = I2;

[0324] When I1 ≠ I2, the arithmetic mean of I1 and I2 can be substituted into the first algorithm and the second algorithm respectively to re-optimize the algorithm parameters, and after the parameter optimization, the first algorithm and the second algorithm are used again to calculate the required insulin infusion amount at the current time, if I1 and I2 are still different, the arithmetic mean of I1 and I2 is taken again to repeat the above process until I1 and I2 are the same, that is:

[0325] ①Solving the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0326] ②The average value is substituted into the first algorithm and the second algorithm respectively, and the algorithm parameters are adjusted;

[0327] ③The first insulin infusion amount I1 and the second insulin infusion amount I2 are recalculated based on the current blood glucose value, the first algorithm and the second algorithm after the adjustment of the parameters;

[0328]

[0329] ④The steps ①-③ are calculated in a loop until I1 = I2, and the final insulin infusion amount I3 = I1 = I2.

[0330] At this time, when the first algorithm or the second algorithm is a PID or rPID algorithm, the algorithm parameters are KP and K D = T D / K p , T D ​K I = T I *K P , T I may take 150-450 min. When the first algorithm or the second algorithm is an MPC or rPMC algorithm, the algorithm parameter is K.

[0331] When I1≠I2, I1and I2may also be weighted, and the calculated values after the weighting are respectively substituted into the first algorithm and the second algorithm to re-optimize the algorithm parameters. After the parameter optimization, the insulin infusion amount required at the current time is calculated again by the first algorithm and the second algorithm, respectively. If I1and I2are still not the same, I1and I2are again weighted, the weighting coefficient is adjusted, and the above process is repeated until I1and I2are the same, i.e.:

[0332] ①The weighted average of the first insulin infusion amount I1and the second insulin infusion amount I2is calculated wherein α and β are the weighting coefficients of the first insulin infusion amount I1and the second insulin infusion amount I2, respectively;

[0333] ②The weighted average is substituted into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0334] ③The first insulin infusion amount I1and the second insulin infusion amount I2are recalculated based on the current blood glucose value, the first algorithm and the second algorithm after the adjustment of the parameters;

[0335] ④Steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0336] Similarly, when the first algorithm or the second algorithm is a PID or RPID algorithm, the algorithm parameter is K P , and K D = T D / K P , T D may take 60-90 min, K I = T I *K P , T I may take 150-450 min. When the first algorithm or the second algorithm is an MPC or rPMC algorithm, the algorithm parameter is K.

[0337] In the embodiments of the present application, the values of a and b can be adjusted according to the sizes of the first insulin infusion amount I1 and the second insulin infusion amount I2, when I1≥I2, a≤b; when I1≤I2, a≥b; preferably, a+b=1. In other embodiments of the present application, the values of a and b can also be in other ranges, which are not limited here.

[0338] When the calculation results of the two are the same, i.e. I3=I1=I2, it can be considered that the insulin infusion amount at the current time can make the blood glucose value reach the ideal level. Through the processing in the above manner, the algorithms are mutually referenced, preferably, the first algorithm and the second algorithm are rMPC algorithm and rPID algorithm respectively, the two are mutually referenced, further improving the accuracy of the output result, making the result more feasible and reliable.

[0339] In another embodiment of the present application, the program module 101 is also provided with a memory for storing information of the user's historical body state, blood glucose value and insulin infusion amount, etc. Statistical analysis can be performed based on the information in the memory to obtain a statistical analysis result I4. When I1≠I2, I1, I2 and I4 are compared respectively, the final insulin infusion amount I3 is calculated, and one of I1 and I2 closer to the statistical analysis result I4 is selected as the calculation result of the final composite artificial pancreas algorithm, i.e. the final insulin infusion amount I3. The program module 101 sends the final insulin infusion amount I3 to the infusion device 102 for infusion; that is:

[0340]

[0341] Through comparison with the historical data, the reliability of the insulin infusion amount is ensured from another aspect.

[0342] In another embodiment of the present application, when I1 and I2 are inconsistent and the difference is large, the blood glucose risk space conversion mode and / or the compensation mode for the delay effect in the rMPC algorithm and / or the rPID algorithm can also be adjusted to make them similar, and then the output result of the composite artificial pancreas algorithm is finally determined through the above arithmetic mean, weighted processing, or comparison with the statistical analysis result.

[0343] In another embodiment of the present application, the closed-loop artificial pancreas control system further comprises a meal recognition module and a motion recognition module. The meal recognition module and the motion recognition module are respectively used to identify whether the user is eating or exercising. Common meal recognition can be based on the blood glucose change rate and judged through a specific threshold. The blood glucose change rate can be calculated from two time points before and after, or obtained by linear regression of multiple time points in a period of time. Specifically, when the change rate of the two time points before and after is used for calculation, the calculation formula is:

[0344] dG tdG / dt = (G t - G t-1 ) / Δt

[0345] wherein:

[0346] G t represents the blood glucose value at the current time point;

[0347] G t-1 represents the blood glucose value at the previous time point;

[0348] Δt represents the time interval between the current time point and the previous time point.

[0349] When the rate of change of three time points is used for calculation, the calculation formula is:

[0350] dG t / dt = (3G t - 4G t-1 + G t-2 ) / (2Δt)

[0351] wherein:

[0352] G t represents the blood glucose value at the current time point;

[0353] G t-1 represents the blood glucose value at the previous time point;

[0354] G t-2 represents the blood glucose value at the time point before the previous time point;

[0355] Δt represents the time interval between the current time point and the previous time point.

[0356] Before calculating the rate of change of blood glucose, the original continuous glucose data can also be filtered or smoothed. The threshold value can be set to 1.8 mg / mL-3 mg / mL, or it can be personalized.

[0357] Similar to meal recognition, since exercise can cause rapid decline in blood glucose, exercise recognition can also be based on the rate of change of blood glucose and determined by a specific threshold value. The calculation of the rate of change of blood glucose can also be as described above, and the threshold value can be personalized. In order to determine the occurrence of exercise more quickly, the closed-loop artificial pancreas insulin infusion control system also includes a motion sensor (not shown). The motion sensor is used to automatically detect the physical activity of the user, and the program module 101 can receive the physical activity information. The motion sensor can automatically and accurately sense the physical activity state of the user and send the activity state parameter to the program module 101, improving the output reliability of the composite artificial pancreas algorithm in the exercise scenario.

[0358] The motion sensor can be arranged in the detection module 100, the program module 101 or the infusion module 102. Preferably, in the embodiment of the present application, the motion sensor is arranged in the program module 101.

[0359] It should be noted that the embodiment of the present application does not limit the number of motion sensors and the arrangement position of the plurality of motion sensors, as long as the condition that the motion sensor can perceive the user's activity condition is met.

[0360] The motion sensor includes a three-axis acceleration sensor or a gyroscope. The three-axis acceleration sensor or the gyroscope can more accurately sense the activity intensity, activity mode or body posture of the body. Preferably, in the embodiment of the present application, the motion sensor is a combination of a three-axis acceleration sensor and a gyroscope.

[0361] It should be noted that in the calculation process, the blood glucose risk conversion mode adopted by the rMPC algorithm and the rPID algorithm can be the same or different, the compensation mode of the delay effect can be the same or different, and the calculation process can also be adjusted according to the actual situation.

[0362] Figure 5 The module relationship schematic diagram of the full-closed-loop artificial pancreas drug infusion control system of another embodiment of the present application.

[0363] The closed-loop artificial pancreas insulin infusion control system in the embodiment of the present application includes a detection module 100, a program module 101, a hypoglycemic drug infusion module 104 and a hyperglycemic drug infusion module 103. The hypoglycemic drug includes insulin and its analogues and other hypoglycemic drugs. The hyperglycemic drug includes hyperglycemic drugs with opposite effects, such as glucagon and its analogues, cortisol and its analogues, growth hormone and its analogues, epinephrine and its analogues, glucose and the like. The hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103 can infuse hypoglycemic drugs and / or hyperglycemic drugs into the user's body according to the hypoglycemic drug infusion instructions and / or hyperglycemic drug infusion instructions issued by the program module 101. The program module 101 is pre-set with a classic PID algorithm, a classic MPC algorithm, an rMPC algorithm, an rPID algorithm or a compound artificial pancreas algorithm. According to the blood glucose value G detected by the detection module 100, the pre-set algorithm calculates the amount of hypoglycemic drugs or hyperglycemic drugs required by the user.

[0364] As mentioned above, generally, the detection module 100 is a continuous glucose detection meter; the program module 101 is used to control the operation of the detection module 100, the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103, including the memory and processor and other components required for realizing the control function; the hypoglycemic drug infusion module 104 contains the mechanical structure necessary for infusing the hypoglycemic drug, and the hyperglycemic drug infusion module 103 contains the mechanical structure necessary for infusing the hyperglycemic drug, such as a drug cartridge for storing the drug; a drug infusion pipeline, including an infusion needle, for infusing the drug into the user's body; a driving component for transferring the drug from the drug cartridge to the user's body through the drug infusion pipeline, etc., specifically, a patch pump of the Medtronic company or other pumps with corresponding functions.

[0365] In an embodiment of the present application, the detection module 100, the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103 are respectively arranged in three different structures, thus, the three are respectively attached to different positions of the user's skin. In another embodiment of the present application, any two of the detection module 100, the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103 are connected with each other or arranged in the same structure, and attached to a certain position of the skin, while the third module is arranged in another structure and attached to another position of the skin. When the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103 are arranged in the same structure by integration, the hypoglycemic drug and the hyperglycemic drug can be respectively infused through different drug pipelines, or can be infused at different times through the same drug pipeline, and the specific design of the drug pipeline is not limited herein. In yet another embodiment of the present application, the detection module 100, the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103 are connected with each other or integrated to form a whole, and attached to the same position of the skin.

[0366] It should be noted that the connection to form a whole in the embodiments of the present application means that two separate components are directly connected or connected through an intermediate structure to form a whole, and the integration to form a whole means that one component is arranged in another component to become a part of the other component, thus making the two a whole.

[0367] In another embodiment of the present application, the full closed-loop artificial pancreas drug infusion control system comprises two detection modules, one of which is connected or integrated with the hypoglycemic drug infusion module 104 to be attached to a certain position on the skin, and the other detection module is connected or integrated with the hypoglycemic drug infusion module 103 to be attached to another position on the skin. Generally, the detection module is a continuous glucose monitor (CGM), which may cause deviation in the blood glucose value detected by the CGM due to process or calibration, etc. On the one hand, the two detection modules send the corresponding blood glucose values to the program module 101 after detecting the blood glucose, and the program module 101 can appropriately process the two received blood glucose values, such as comparing with historical data, selecting appropriate blood glucose value to calculate drug infusion amount, or taking the weighted average of the two blood glucose values as the basis to calculate the drug infusion amount according to the pre-design, so that the drug infusion amount required by the user is more accurate. On the other hand, when one of the detection modules fails or is in a hot start state, the other detection module can detect and send the blood glucose value to the program module 101, ensuring the continuity of blood glucose detection.

[0368] It should be noted that in the embodiments of the present application, the location and connection relationship of the program module 101 and other modules are not specifically limited, as long as the control of the detection module and the infusion module can be realized. For example, in one embodiment of the present application, the full-closed-loop artificial pancreas drug infusion control system further comprises an external electronic device, such as a handset or a mobile phone, and the program module 101 is arranged in the external electronic device, and the program module 101 communicates with other modules through a wireless mode. In another embodiment of the present application, the program module 101 is integrally arranged in any one of the detection module 100, the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103, for example, the detection module 100, the hypoglycemic drug infusion module 104 or the hyperglycemic drug infusion module 103 comprises an analog and / or digital circuit, and the circuit can be realized as a program module. In still another embodiment of the present application, the program module 101 is separately arranged in a structure and is separately attached to a certain position of the skin, and is connected with other modules through a wireless mode. In yet another embodiment of the present application, the program module 101 is separately arranged in a structure, and can be connected with one or more of the detection module 100, the hypoglycemic drug infusion module 104 or the hyperglycemic drug infusion module 103 to form an integral whole, and is attached to the same position of the skin with one or more of them, and the connection mode can be wired or wireless. For example, when the detection module 100, the hypoglycemic drug infusion module 104 or the hyperglycemic drug infusion module 103 are all arranged in separate structures, the program module 100 can be connected with any one of them to form an integral whole, and thus the full-closed-loop artificial pancreas drug infusion system comprises three separate structures attached to three different positions of the skin. When any two of the detection module 100, the hypoglycemic drug infusion module 104 or the hyperglycemic drug infusion module 103 are connected or integrated with each other to form an integral whole, and the other one is a separate structure, the program module 100 can be connected with any one of them, and thus the full-closed-loop artificial pancreas drug infusion system comprises two separate structures attached to two different positions of the skin. When the detection module 100, the hypoglycemic drug infusion module 104 and the hyperglycemic drug infusion module 103 are connected or integrated with each other to form an integral whole, the program module 100 is connected with the integral whole, and thus the full-closed-loop artificial pancreas drug infusion system comprises only one separate structure attached to one position of the skin.

[0369] Figure 6 The schematic diagram of the double-drug infusion switching according to the two embodiments of the present application.

[0370] In one embodiment of the present application, the hypoglycemic drug infusion instruction and / or the current hyperglycemic drug infusion instruction is obtained by comparing the blood glucose concentration estimate G P with the target blood glucose value G B , and the blood glucose concentration estimate G PBlood glucose levels can be estimated using the rMPC prediction model or other suitable blood glucose prediction algorithms; blood glucose-lowering drug infusion data and / or blood glucose-raising drug infusion data can be calculated using the aforementioned classical PID algorithm, classical MPC algorithm, rMPC algorithm, rPID algorithm, or composite artificial pancreas algorithm. Specifically:

[0371] When G P ≥G B At that time, the infusion module 102 begins to calculate the hypoglycemic drug infusion data I based on the classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm, or composite artificial pancreas algorithm. t Administer hypoglycemic drugs via infusion;

[0372] When G P <G B At that time, the infusion module 102 begins to calculate the blood glucose-raising drug infusion data D based on the classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm, or composite artificial pancreas algorithm. t Administer blood glucose-raising medication via infusion.

[0373] It should be noted that, in the embodiments of the present invention, I b This means controlling blood glucose at the target blood glucose level (G) without interference. B The amount of hypoglycemic drugs that need to be infused at that time, when G P =G B At that time, I t =I b When G P >G B At that time, with the infusion of hypoglycemic drugs, G P Further reduction, I t It will also decrease. When the infusion module 102 has only one set of drug infusion tubing, when G P <G B At that time, i.e., I t <I b At that time, the infusion module 102 begins infusion of blood glucose-raising drugs, and the blood glucose-raising drug infusion data D... t It can calculate using classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm, or a composite artificial pancreas algorithm, while simultaneously stopping the infusion of hypoglycemic drugs to prevent hypoglycemic drugs and hypoglycemic drugs from interfering with each other due to antagonistic effects. When the infusion module 102 has at least two sets of drug infusion tubing, when 0≤I t <I b At the same time, while infusing blood glucose-raising drugs, blood glucose-lowering drugs can also be continued, which can effectively prevent hypoglycemia; when I t When the blood glucose level is <0, the infusion of hypoglycemic drugs should be stopped and only hypoglycemic drugs should be infused.

[0374] In another embodiment of the present application, the hypoglycemic drug infusion instruction and / or the current hyperglycemic drug infusion instruction can be directly calculated by comparing the required amount of hypoglycemic drug I t and the target hypoglycemic drug amount I b The required amount of hypoglycemic drug I t and the target hypoglycemic drug amount I b can be calculated by the aforementioned classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm. Specifically:

[0375] When the infusion module 102 has at least two sets of drug infusion pipelines:

[0376] When I t ≥ I b , the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I t calculated by the classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm;

[0377] When 0 ≤ I t < I b , the infusion of the hypoglycemic drug can be continued while the infusion of the hyperglycemic drug is started, which can effectively prevent the occurrence of hypoglycemia. The hypoglycemic drug infusion data I t and the hyperglycemic drug infusion data D t can be calculated by the aforementioned classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm;

[0378] When I t < 0, the infusion of the hypoglycemic drug is stopped and only the hyperglycemic drug is infused. The hyperglycemic drug infusion data D t can be calculated by the classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm.

[0379] When the infusion module 102 has only one set of drug infusion pipeline:

[0380] When I t ≥ 0, the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I t calculated by the classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm;

[0381] When I t < 0, the infusion of the hypoglycemic drug is stopped and only the hyperglycemic drug is infused.

[0382] Preferably, in the embodiments of the present application, the hypoglycemic drug is insulin, and the hyperglycemic drug is glucagon.

[0383] It should be noted that in the above embodiments, the calculation methods of the hypoglycemic drug infusion data and the glucagon infusion data in each stage can be the same or different, preferably, the same algorithm architecture is used for calculation to ensure the consistency of the basic conditions during calculation, so that the calculation result is more accurate. More preferably, the compound artificial pancreas algorithm is used for calculation to make full use of the advantages of rPID algorithm and rMPC algorithm to face complex situations, so that the blood glucose control level is more ideal.

[0384] In summary, the present application discloses a double closed loop artificial pancreas insulin infusion control system, which comprises a program module, at least one detection module, a hypoglycemic drug infusion module and a hyperglycemic drug infusion module. The program module is pre-set with an algorithm. The detection module detects blood glucose value. The hypoglycemic drug infusion module infuses hypoglycemic drug according to the infusion instruction calculated by the algorithm. The hyperglycemic drug infusion module infuses hyperglycemic drug according to the infusion instruction calculated by the algorithm, so as to realize the full closed loop control of the artificial pancreas multi-drug infusion system.

[0385] Although some specific embodiments of the present application have been described in detail through examples, those skilled in the art should understand that the above examples are only for illustration, but not for limiting the scope of the present application. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A fully closed loop artificial pancreas drug infusion control system, characterized by, The application comprises: a program module, in which an algorithm is preset, the algorithm comprising an rMPC algorithm and an rPID algorithm, the rMPC algorithm and the rPID algorithm converting blood glucose risks in an original physical space that is asymmetric to blood glucose risks in a risk space that is approximately symmetric; at least one detection module for continuously detecting a current blood glucose value; a hypoglycemic drug infusion module for hypoglycemic drug infusion according to a hypoglycemic drug infusion indication calculated by the algorithm; and a hyperglycemic drug infusion module for hyperglycemic drug infusion according to a hyperglycemic drug infusion indication calculated by the algorithm. The value function of the rMPC algorithm after risk conversion is as follows: wherein r t+j represents the blood glucose risk value after the jth step, I' t+j represents the change of insulin infusion amount after the jth step, t represents the current time, R is a weighted coefficient of the insulin component, N and P are the step numbers in the control time window and the prediction time window, respectively; The blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm is improved control variability grid analysis conversion. wherein G B represents the target blood glucose value; G t+j represents the blood glucose value at the jth step of detection; Meanwhile, the maximum value is limited as follows: r t+j | = min(|r t+j |,n) The value range of the maximum value n is 0-80 mg / dL.

2. The fully closed loop artificial pancreas medication infusion control system of claim 1, wherein, Two of the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are connected or integrated into one whole.

3. The fully closed loop artificial pancreas medication infusion control system of claim 1, wherein, The detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are respectively separate different structures.

4. The fully closed loop artificial pancreas medication infusion control system of claim 1, wherein, The detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module are connected or integrated into one whole.

5. The fully closed loop artificial pancreas medication infusion control system of claim 1, wherein, The number of the detection module is two, one of the detection modules is connected or integrated with the hypoglycemic drug infusion module into one whole, and the other of the detection modules is connected or integrated with the hyperglycemic drug infusion module into another whole.

6. The fully closed loop artificial pancreas medication infusion control system according to any one of claims 1-5, wherein, The application further comprises an external electronic device, and the program module is arranged in the external electronic device.

7. The fully closed loop artificial pancreas medication infusion control system according to any one of claims 1-5, wherein, The program module is arranged in any one of the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module.

8. The fully closed loop artificial pancreas medication infusion control system according to any one of claims 1-5, wherein, The program module is a separate structure.

9. The fully closed loop artificial pancreas medication infusion control system of claim 8, wherein, The program module is connected with one or more of the detection module, the hypoglycemic drug infusion module and the hyperglycemic drug infusion module into one whole.

10. The fully closed loop artificial pancreas medication infusion control system of claim 5, wherein, When one of the detection modules fails or is in a hot start state, the other detection module detects.

11. The fully closed loop artificial pancreas medication infusion control system of claim 5, wherein, The program module processes the blood glucose values detected by the two detection modules.

12. The fully closed loop artificial pancreas medication infusion control system of claim 2, wherein, The hypoglycemic drug infusion module and the hyperglycemic drug infusion module are integrated into one whole, and the hypoglycemic drug infusion module and the hyperglycemic drug infusion module share the same drug infusion pipeline.

13. The fully closed loop artificial pancreas medication infusion control system of claim 2, wherein, The hypoglycemic drug infusion module and the hyperglycemic drug infusion module are integrated into one whole, and the hypoglycemic drug infusion module and the hyperglycemic drug infusion module adopt different drug infusion pipelines for drug infusion.

14. The fully closed loop artificial pancreas medication infusion control system according to any one of claims 1-5, wherein, The hypoglycemic drug infusion module infuses insulin, and the hyperglycemic drug infusion module infuses glucagon.

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