Blood glucose monitoring method and device, blood glucose monitoring equipment and blood glucose monitoring system

Abstract

The invention provides a blood glucose monitoring method and device, blood glucose monitoring equipment and a blood glucose monitoring system, and relates to the technical field of blood glucose monitoring. The method comprises the following steps: acquiring time sequence data and behavior identification of salivary amylase concentration of a to-be-detected object, determining total food intake, a blood glucose measurement value and historical injection data of the to-be-detected object at the current moment, and determining a blood glucose prediction value of the to-be-detected object at a target moment within a preset time period; according to the blood glucose predicted value and the target blood glucose value, the medicine infusion dosage used for adjusting the blood glucose monitoring value of the to-be-detected object is determined. Based on the salivary amylase concentration of the to-be-detected object, the blood glucose change is predicted, the corresponding insulin supplement amount is calculated, and the problem that a meal model is difficult to establish can be solved; the eating behavior of the to-be-detected object can be well estimated, and the accuracy of blood glucose prediction is improved; and the blood sugar value is predicted in advance, so that hyperglycemia possibly occurring after meal can be controlled more timely and effectively.

Description

Technical Field

[0001] The present application relates to the technical field of blood glucose monitoring, and in particular to a blood glucose monitoring method, device, blood glucose monitoring equipment and blood glucose monitoring system. Background Art

[0002] Blood sugar level is an extremely sensitive indicator that directly reflects the damage of pancreatic islet function in diabetic patients, so blood sugar monitoring is a key part of the daily life of diabetic patients. Due to the delayed response problem after insulin injection, the traditional closed-loop artificial pancreas system is difficult to predict the time and amount of food intake, and therefore cannot fully meet the blood sugar management needs of diabetic patients.

[0003] However, the current practice of judging food intake events based on posture-based physical behavior events may result in misjudgment of events. Alternatively, meal models may be created through historical data for analysis. However, since a large amount of data is required for modeling, meal models are difficult to establish and update. Summary of the invention

[0004] The present application provides a blood glucose monitoring method, apparatus, blood glucose monitoring equipment and blood glucose monitoring system, which can solve the problem that traditional blood glucose monitoring is prone to event misjudgment or difficulty in model establishment and updating.

[0005] In a first aspect, the present application provides a blood glucose monitoring method, comprising:

[0006] Obtain the time series data and behavior identifier of the salivary amylase concentration of the subject to be tested, and determine the total amount of food consumed by the subject to be tested at the current moment; obtain the predicted blood sugar value of the subject to be tested at the target moment within the preset time period based on the total amount of food consumed, the blood sugar measurement value of the subject to be tested at the current moment, and the historical injection data; obtain the target blood sugar value of the subject to be tested at the set target moment; and determine the drug infusion dose used to adjust the blood sugar monitoring value of the subject to be tested based on the predicted blood sugar value and the target blood sugar value.

[0007] The above method determines the eating status based on the amylase concentration in the saliva of the subject to be tested, predicts blood sugar changes based on the eating status, and calculates the corresponding insulin supplement amount, which can solve the problems of difficulty in establishing and updating the meal model; it can achieve a good estimation of the eating status of the subject to be tested and improve the accuracy of blood sugar prediction; and the advance prediction of blood sugar values ​​can achieve more timely and effective control of high blood sugar conditions that may occur after a meal; it has strong ease of use and practicality.

[0008] In an implementable manner of the first aspect, the step of obtaining time series data of salivary amylase concentration of the subject to be tested includes:

[0009] Obtain the time series data of raw salivary amylase concentration;

[0010] Performing linear interpolation and sliding filtering processing on the original salivary amylase concentration time series data to obtain a processed salivary amylase concentration time series of the object to be tested, wherein the number of salivary amylase concentration time series data is n+1;

[0011] The expression of the salivary amylase concentration time series of the object to be tested is:

[0012] (c(t n ),c(t n-1 ),...,c(t0))

[0013] Wherein, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, and n is a positive integer greater than or equal to 1.

[0014] In an implementable manner of the first aspect, the behavior identifier includes starting to eat, continuing to eat, stopping eating, and not eating; and determining the total amount of food intake of the subject at the current moment according to the time series data of the salivary amylase concentration of the subject to be tested includes:

[0015] Acquire the salivary amylase concentration within a preset time period according to the time series data of the salivary amylase concentration of the object to be tested;

[0016] When the salivary amylase concentration increases successively during the preset monitoring time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is identified as starting to eat;

[0017]

[0018] The number of time series data of salivary amylase concentration within the preset time period is m+1, m is a positive integer greater than or equal to 1 and less than or equal to n, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, c(t m ) is t m The salivary amylase concentration at the moment, firstDifferenceThreshold is the first concentration difference threshold, and firstThreshold is the first concentration threshold;

[0019] When the salivary amylase concentration decreases successively during the monitoring preset time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is identified as stopping eating;

[0020]

[0021] The number of time series data of salivary amylase concentration within the preset time period is m+1, m is a positive integer greater than or equal to 1 and less than or equal to n, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, c(t m ) is t m The salivary amylase concentration at the moment, secondDifferenceThreshold is the second concentration difference threshold, and secondThreshold is the second concentration threshold;

[0022] If the behavior mark of the subject to be tested is detected as starting to eat, but the behavior mark of the subject to be tested is not detected as stopping eating, then determining that the behavior mark of the subject to be tested is continuing to eat;

[0023] In other cases, the behavior of the subject to be tested is identified as not eating;

[0024] When it is determined that the behavior of the subject to be tested is marked as starting to eat or continuing to eat, the relationship expression between the salivary amylase concentration c(t0) at the current time t0 and the total amount of food g(t0) consumed from the time when the behavior of the subject to be tested is marked as starting to eat to the current time t0 is obtained, and the relationship expression is:

[0025]

[0026] c0 is the initial salivary amylase concentration in the unfed state; k1 and k2 are correction factors; w is the penalty coefficient, which defaults to 0;

[0027] The total amount of food intake of the subject at the current moment is determined according to the relational expression.

[0028] In an implementable manner of the first aspect, obtaining a predicted blood glucose value of the subject to be tested at a target time within a preset time period according to the total amount of food consumed and the blood glucose measurement value of the subject to be tested at a current time includes:

[0029] Obtaining the current blood sugar measurement value of the subject to be measured through a blood sugar monitoring device;

[0030] Acquiring historical injection data of the subject to be tested through an injection device or a cloud database;

[0031] The predicted blood sugar value of the subject to be tested at a target moment in a future preset time period is obtained based on the total amount of food consumed, the blood sugar measurement value at the current moment and the historical injection data of the subject to be tested.

[0032] In an implementable manner of the first aspect, determining a drug infusion dose for adjusting a blood glucose monitoring value of the subject to be tested according to the predicted blood glucose value and the target blood glucose value includes:

[0033] The drug infusion dose is calculated by the following formula:

[0034]

[0035] Wherein, dose is the infusion dose of the drug, t p is the target time within the preset time period, predictedBg(t p ) is the target time t p The predicted blood glucose value, targetBg is the target blood glucose value, and isf is the drug sensitivity coefficient of the object to be tested.

[0036] In an implementation manner of the first aspect, the method further includes:

[0037] When the behavior marker of the subject to be tested is the start of eating or the continuous eating, or the duration from the switch to the stop of eating is a first duration that does not exceed a threshold, a prompt is issued to infuse the drug infusion dose in a large dose manner;

[0038] When the behavior marker of the subject to be tested is monitored as the start of eating or the continued eating, and the time from the behavior marker of the subject to be tested being monitored to switching to the stop of eating is a first time length, and the first time length does not exceed a threshold, a prompt is issued to infuse the drug infusion dose at a basal rate.

[0039] In a second aspect, the present application provides a blood glucose monitoring device, which is a unit for implementing any method described in the first aspect.

[0040] In a third aspect, the present application provides a blood glucose monitoring device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any one of the methods described in the first aspect when executing the computer program.

[0041] In a fourth aspect, the present application provides a blood glucose monitoring system, comprising the blood glucose monitoring device of the third aspect, and also comprising a user terminal; the user terminal is used to display the drug infusion dose output by the blood glucose monitoring device.

[0042] In a fifth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any one of the methods described in the first aspect.

[0043] In a sixth aspect, the present application provides a computer program product, which, when executed on a device, enables the device to execute any of the methods described in the first aspect.

[0044] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic diagram of a blood glucose monitoring method according to an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of a process for calculating the total amount of food intake provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of the structure of a blood glucose monitoring device provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of the structure of a blood glucose monitoring device is provided for an embodiment of the present application;

[0050] Figure 5 A structural schematic diagram of a blood glucose monitoring system is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0053] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0054] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0056] Diabetes can lead to acute and chronic complications. Currently, there is no cure for diabetes in the medical community. Insulin supplementation in vitro is still an effective means of controlling blood sugar. Dynamic glucose monitoring technology CGM can provide patients with continuous blood sugar monitoring data throughout the day. It and insulin infusion data can reflect the body's blood sugar metabolism and provide data support for individualized treatment. The closed-loop control algorithm of the traditional artificial pancreas system calculates the appropriate insulin injection amount and injection time based on the CGM blood sugar measurement value. The injection execution device injects insulin at a fixed time and quantity according to the calculation results of the algorithm to regulate blood sugar concentration.

[0057] However, the traditional artificial pancreas system cannot fully meet the blood sugar management needs of diabetic patients. For example, the subcutaneous injection method has the problem of delayed insulin response, and the traditional system is difficult to accurately predict the time and amount of food eaten, which makes it difficult to quantify the impact of food on blood sugar concentration and cannot timely control postprandial hyperglycemia, which leads to poor postprandial blood sugar control in patients. Manually entering the time and amount of food eaten will bring burden to patients or make it difficult to accurately estimate carbon water.

[0058] At present, there are large information errors in indirectly judging information such as meal time and quantity through motion and other physical sensors, which may lead to wrong results. Alternatively, a meal model is established based on historical meal data and measured historical blood sugar data to analyze the meal probability in the interval. However, it is difficult to establish a meal detection model because it needs to be based on a large amount of data for modeling, and when the patient's actual physiological characteristics change, the model is updated slowly.

[0059] In response to the above problems, the present application proposes a blood glucose monitoring method, which can actively detect meal time and food intake, and predict blood glucose in future time periods, and adjust the insulin dose required for the test subject based on the prediction results; it can solve the problem of difficulty in establishing and updating meal models; it can achieve a good estimation of the eating status of the test subject and improve the accuracy of blood glucose prediction.

[0060] The specific implementation process of the blood glucose monitoring method is described below by way of example. Figure 1 As shown, the blood glucose monitoring method provided in the embodiment of the present application, the execution subject of the method may be a blood glucose monitoring device, and the method may include the following steps:

[0061] S101, obtaining time series data of salivary amylase concentration of a subject to be tested.

[0062] In an embodiment of the present application, the blood glucose monitoring device may include a salivary amylase concentration monitoring module, which includes a high-sensitivity amylase sensor. The high-sensitivity amylase sensor is used to detect the salivary amylase concentration in the saliva of the object to be tested in real time or periodically at regular intervals to obtain time series data of the salivary amylase concentration.

[0063] Exemplarily, the amylase sensor can be placed in the oral cavity of the subject to be tested for real-time detection. After the amylase sensor detects the time series data of the salivary amylase concentration, the time series data can be transmitted to a subsequent eating detection module to evaluate the eating status of the subject to be tested.

[0064] In some embodiments, obtaining time series data of salivary amylase concentration of a subject to be tested includes:

[0065] Obtaining original salivary amylase concentration time series data; performing linear interpolation and sliding filtering on the original salivary amylase concentration time series data to obtain a processed salivary amylase concentration time series of the object to be tested, wherein the number of salivary amylase concentration time series data is n+1, where n is a positive integer greater than or equal to 1.

[0066] The expression of the salivary amylase concentration time series of the object to be tested is: (c(t n ),c(t n-1 ),...,c(t0)), t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, and the value of n is an integer greater than 0.

[0067] Exemplarily, by performing linear interpolation processing on the original salivary amylase concentration time series data, new sampling point data is inserted between two sampling point data in the salivary amylase concentration time series data, the sampling point data is increased, the data sampling rate is increased, the accuracy of the salivary amylase data is improved, and then the interpolated data is subjected to sliding filtering processing to remove interference data, thereby obtaining smoothly reconstructed salivary amylase data.

[0068] S102, determining the total amount of food intake of the subject at the current moment according to the time series data of the salivary amylase concentration of the subject.

[0069] In an embodiment of the present application, the blood glucose monitoring device may further include an eating detection algorithm module, which may determine a behavior identifier corresponding to the current moment of the subject to be tested based on the time series data of the salivary amylase concentration of the subject to be tested, and the behavior identifier may correspond to the eating status of the subject to be tested, for example, the behavior identifier may be starting to eat, continuing to eat, stopping eating, and not eating, etc.; and then based on the time series data of the salivary amylase concentration of the subject to be tested and the corresponding behavior identifier, the total amount of food eaten by the subject to be tested at the current moment is calculated.

[0070] Exemplarily, in the process of determining the behavior identifier corresponding to the current moment of the object to be tested based on the time series data of the salivary amylase concentration of the object to be tested, the user behavior identifier can be judged based on the change of the salivary amylase concentration of the object to be tested over a period of time. For example, if the salivary amylase concentration has been increasing over a period of time and reaches the concentration value of the eating state at a certain moment, it can be determined that the object to be tested has entered the eating stage, such as the state of starting to eat or continuing to eat; when the salivary amylase concentration has been decreasing over a period of time and reaches the concentration value of the state of not eating at a certain moment, it can be determined that the object to be tested has entered the stage of stopping eating; in other cases, it is judged that the object to be tested has entered the stage of not eating.

[0071] The embodiment of the present application can determine the user's behavior identifier more timely and accurately based on the change in salivary amylase concentration, thereby calculating the user's total food intake based on the behavior identifier, providing reliable data support for subsequent timely prediction of the blood sugar changes of the subject to be tested based on the total food intake.

[0072] The following is a detailed description of the implementation process of calculating the total amount of food intake. Figure 2 , a schematic diagram of the implementation flow of calculating the total amount of food intake provided in the embodiment of the present application. The calculation process includes the following steps:

[0073] S1021, obtaining the salivary amylase concentration within a preset time period according to the time series data of the salivary amylase concentration of the object to be tested.

[0074] In an embodiment of the present application, based on the acquired time series data of the salivary amylase concentration of the object to be tested, the salivary amylase concentration at each moment is judged, and the size relationship of the sequence data of the salivary amylase concentration within a period of time is judged, that is, the data collected at each moment can be compared with the previous data in size; thereby, based on the acquired time series data, the salivary amylase concentration within a preset time period can be determined, and the subsequent behavior identification of the object to be tested can be judged based on the salivary amylase concentration within the preset time period.

[0075] Exemplarily, the preset time period can be determined based on the data of the historical behavior identification of the object to be tested, or it can be set based on the needs of the object to be tested, or it can be set based on the real-time monitoring of the salivary amylase concentration changes of the object to be tested. For example, when the salivary amylase concentration changes greatly or changes rapidly, the duration of the preset time period is shortened and the frequency of evaluating the preset time period data is increased.

[0076] S1022, when the salivary amylase concentration increases successively during the preset monitoring time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is identified as starting to eat.

[0077] In the embodiment of the present application, the number of time series data of salivary amylase concentration within the preset time period is m+1 (less than or equal to the aforementioned n+1), where m is a positive integer greater than or equal to 1 and less than or equal to n. When the monitoring of salivary amylase concentration within the preset time period satisfies the following formula (1), it can be determined that the current behavior of the subject to be tested is identified as starting to eat.

[0078]

[0079] Where t0 is the current time, c(t0) is the salivary amylase concentration at the current time, and c(t m ) is t m The salivary amylase concentration at the moment, firstDifferenceThreshold is the first concentration difference threshold, and firstThreshold is the first concentration threshold.

[0080] Exemplarily, the first concentration difference threshold and the first concentration threshold can be individually set based on the object to be tested, and the values ​​set for different objects to be tested can also be different. When the condition of formula (1) is met, the behavior indicator of the object to be tested can be a state of starting to eat from a state of stopping eating.

[0081] S1023, when the salivary amylase concentration decreases successively during the preset monitoring time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is identified as stopping eating.

[0082] In the embodiment of the present application, the number of time series data of salivary amylase concentration within the preset time period is m+1 (less than or equal to the aforementioned n+1). When the monitoring of salivary amylase concentration within the preset time period satisfies the following formula (2), it can be determined that the current behavior of the subject to be tested is identified as stopping eating.

[0083]

[0084] Where t0 is the current time, c(t0) is the salivary amylase concentration at the current time, and c(t m ) is t m The salivary amylase concentration at the moment, secondDifferenceThreshold is the second concentration difference threshold, and secondThreshold is the second concentration threshold.

[0085] Exemplarily, the second concentration difference threshold and the second concentration threshold can be individually set based on the object to be tested, and the values ​​set for different objects to be tested can also be different. When the condition of formula (2) is met, the behavior indicator of the object to be tested can be a state of entering a state of stopping eating from starting to eat or continuing to eat.

[0086] S1024: If the behavior of the subject to be tested is detected as starting to eat, but the behavior of the subject to be tested is not detected as stopping eating, then the behavior of the subject to be tested is determined to be continuing to eat.

[0087] For example, if the behavior of the subject to be tested has been detected as starting to eat, and the behavior of the subject to be tested has not been detected as stopping eating within a period of time in the future, then during this period of time (from the detection of the start of eating to the detection of the stop of eating), the behavior of the subject to be tested is determined to be continuous eating. In other cases, the behavior of the subject to be tested is determined to be not eating; for example, after the stop of eating is detected, if the state of starting to eat or continuing to eat is not detected within a period of time in the future, the behavior of the subject to be tested is determined to be not eating.

[0088] S1025, when it is determined that the behavior of the subject to be tested is marked as starting to eat or continuing to eat, obtain the relationship expression between the salivary amylase concentration c(t0) at the current time t0 and the total amount of food g(t0) consumed from the time when the behavior of the subject to be tested is marked as starting to eat to the current time t0.

[0089] Exemplarily, the relational expression may be as shown in the following formula (3):

[0090]

[0091] Wherein, c0 is the initial salivary amylase concentration in the unfed state, and the value of this parameter can be measured by a sensor based on the feedback of the subject under test in the unfed state; k1 and k2 are correction factors; and w is the penalty coefficient, which defaults to 0.

[0092] S1026, determining the total amount of food consumed by the subject at the current moment according to the relational expression.

[0093] Exemplarily, when the behavior of the subject to be tested is identified as starting to eat or continuing to eat, the total amount of food eaten at the current moment can be calculated by formula (3). The blood sugar monitoring device can record the initial salivary amylase concentration of the subject to be tested when not eating, thereby deriving the value of the total amount of food eaten g(t0) based on the salivary amylase concentration at the current moment that is further monitored.

[0094] Through the embodiments of the present application, based on the determination of the behavioral identifier of the subject to be measured, the total amount of food intake when starting to eat or continuing to eat is calculated, which can improve the timeliness and accuracy of blood sugar prediction.

[0095] S103, obtaining a predicted blood sugar value of the subject at a target time within a preset time period according to the total amount of food consumed and the current blood sugar measurement value of the subject.

[0096] In an embodiment of the present application, the blood glucose monitoring device also includes an insulin injection indication module, which predicts the blood glucose value of the object to be measured at the future target moment, and adjusts the amount of insulin to be injected based on the corresponding target blood glucose value. The blood glucose value at the current moment can be obtained by real-time measurement by CGM, and the preset time period can be determined based on the active time of insulin and the digestion time of the object to be measured, that is, starting from the current moment, the shorter duration between the active time of insulin and the digestion time of the object to be measured is selected as the preset time period in the future. The target moment is any moment from the current moment to the preset time period.

[0097] Exemplarily, based on the insulin activity time model and the carbohydrate absorption model (i.e., the digestion model), the blood glucose predicted value at the target moment in the future preset time period is predicted for the calculated total food intake and the blood glucose measurement value of the subject to be tested at the current moment, thereby achieving timely and reliable prediction of the future blood glucose changes of the subject to be tested, and facilitating timely control of the future blood glucose changes of the subject to be tested.

[0098] In some embodiments, obtaining the predicted blood glucose value of the subject at a target time within a preset time period based on the total amount of food intake, the current blood glucose measurement value of the subject, and historical injection data includes:

[0099] The current blood sugar measurement value of the subject to be tested is obtained through the blood sugar monitoring device; the historical injection data of the subject to be tested is obtained through the injection device or the cloud database; based on the total amount of food intake, the current blood sugar measurement value and the historical injection data of the subject to be tested, the blood sugar prediction value of the subject to be tested at the target time in the future preset time period is obtained.

[0100] Exemplarily, the blood glucose monitoring device can monitor the blood glucose level of the subject to be tested in real time, which is recorded as the blood glucose measurement value; and can obtain the historical injection data of the subject to be tested through the injection device or the cloud database.

[0101] For example, since both historical injection data and total food intake will affect the blood sugar level of the subject to be tested, the blood sugar prediction value at the target time in the future preset time period can be obtained based on the historical injection data of the subject to be tested and the total food intake of the subject to be tested at the current moment, which can be calculated by the following formula (4):

[0102]

[0103] Among them, t p is any time from the current time t0 to min{t0+DIA,t0+tdigestion}, that is, the target time within the preset time period; currentBg is the blood glucose value at the current time t0, which is measured by CGM in real time; isf is the insulin sensitivity coefficient of the subject to be tested, which can be set by the user; DIA is the insulin active time, which can be set manually by the user; iobModel is the insulin activity-time model, such as the Walsh model, the rapid-acting insulin model (such as based on Humalog, Novolog, Apidra insulin absorption model, etc.), the Fiasp model (based on the Fiasp insulin absorption model), etc.; d(i) is the amount of insulin injected by the subject to be tested at time i, which can be obtained by reading the historical records; icr is the carbohydrate coefficient of the subject to be tested, which can be set by the user; tdigestion is the digestion time of the subject to be tested, which can be set by the user; cobModel is the carbohydrate absorption model, such as the linear carbohydrate absorption model, the dynamic carbohydrate absorption model, etc.; g(i) is the total amount of food consumed by the patient from "starting to eat" to time i.

[0104] S104, obtaining the target blood sugar level of the subject at the target time.

[0105] S105, determining a drug infusion dose for adjusting the blood glucose monitoring value of the subject to be tested according to the predicted blood glucose value and the target blood glucose value.

[0106] In the embodiment of the present application, the target blood glucose value is the blood glucose value that the subject to be tested should reach at a target time in the future within a preset time that meets the subject's physical fitness standards, and can also be an acceptable range of blood glucose values; for different subjects to be tested, the corresponding target blood glucose values ​​may also be different.

[0107] Exemplarily, determining a drug infusion dose for adjusting the blood glucose monitoring value of the subject to be tested according to the predicted blood glucose value and the target blood glucose value includes:

[0108] The drug infusion dose is calculated by the following formula:

[0109]

[0110] Among them, dose is the drug infusion dose, t p is the target time within the preset time period, predictedBg(t p ) is the target time t p The predicted blood glucose value, targetBg is the target blood glucose value, and isf is the drug sensitivity coefficient of the object to be tested.

[0111] For example, the insulin injection instruction module of the blood sugar monitoring device issues an instruction to inject insulin based on the calculation result, so that the amount of insulin required to be injected by the subject can be flexibly adjusted and controlled, and timely monitoring and control of future blood sugar can be achieved.

[0112] In some embodiments, during the insulin injection indication stage, when the behavior of the subject to be tested is marked as starting to eat or continuing to eat, and the behavior of the subject to be tested is marked as the time from switching to stopping eating is a first time, and the first time does not exceed a threshold, a prompt is issued to infuse the drug infusion dose in a large dose; when the behavior of the subject to be tested is marked as the time from switching to stopping eating exceeds a threshold and is a second time without eating, a prompt is issued to infuse the drug infusion dose in a basal rate. The first time and the second time have no specific correlation and can be the same or different.

[0113] For example, when the subject is in the state of starting to eat or continuing to eat, or has just switched to the state of stopping eating, the speed and amplitude of the blood sugar increase in the first time period are large, and a prompt for injection based on a large dose is issued. In other time periods, a prompt for injection based on a basal rate is issued. Therefore, based on the behavioral identification of the subject to be tested, the amount of insulin injection can be flexibly adjusted and controlled, and the blood sugar of the subject to be tested can be monitored more timely and reliably.

[0114] In one possible implementation, the blood glucose monitoring device also includes a data recording and tracking module, which records and tracks blood glucose data and adjusts the parameters in the formula for calculating the total food intake, so that the calculated total food intake is more accurate and more in line with the calculation standards of the user to be tested.

[0115] Exemplarily, the method further includes adjusting the size of the target parameter used to calculate the total amount of food intake based on the user's behavior identifier and the time series data corresponding to the blood glucose measurement value. For example, if the user's blood glucose measurement value is high blood glucose for a second preset time period from the state of starting to eat to the state of stopping eating from the state of starting to eat or the state of continuing to eat, and the time is greater than the preset high blood glucose time ratio threshold, then the value of the target parameter is increased, such as adjusting the penalty coefficient w=w+α (α is a number greater than 0 and less than 1); if the user's blood glucose measurement value is low blood glucose for a second preset time period from the state of starting to eat to the state of stopping eating from the state of starting to eat or the state of continuing to eat, and the time is less than the preset low blood glucose time ratio threshold, then the value of the target parameter is reduced, such as adjusting the penalty coefficient w=w-β (β is a number greater than 0 and less than 1).

[0116] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] Corresponding to the blood glucose monitoring method provided in the above embodiment, Figure 3 A schematic diagram of the structure of a blood glucose monitoring device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0118] Reference Figure 3 , the blood glucose monitoring device comprises:

[0119] A data acquisition unit 31 is used to acquire time series data of salivary amylase concentration of the object to be tested;

[0120] A food intake calculation unit 32, used to determine the total amount of food intake of the subject at the current moment according to the time series data of the salivary amylase concentration of the subject;

[0121] The blood sugar prediction unit 33 is used to obtain the blood sugar prediction value of the subject to be tested at a target time within a preset time period according to the total amount of food intake, the blood sugar measurement value of the subject to be tested at the current time and the historical injection data;

[0122] The data acquisition unit 31 is also used to obtain the target blood sugar value of the subject at the target time.

[0123] The dosage output unit 34 is used to determine the drug infusion dosage for adjusting the blood glucose monitoring value of the subject to be tested according to the predicted blood glucose value and the target blood glucose value.

[0124] In a possible implementation, the data acquisition unit 31 is further used to acquire the time series data of the original salivary amylase concentration; perform linear interpolation and sliding filtering on the time series data of the original salivary amylase concentration to obtain the processed time series data of the salivary amylase concentration of the object to be tested, and the number of the time series data of the salivary amylase concentration is n+1; the expression of the time series data of the salivary amylase concentration of the object to be tested is:

[0125] (c(t n ),c(t n-1 ),...,c(t0))

[0126] Wherein, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, and n is a positive integer greater than or equal to 1.

[0127] In a possible implementation, the food intake calculation unit 32 is further used to obtain the salivary amylase concentration within a preset time period according to the time series data of the salivary amylase concentration of the subject to be tested;

[0128] When the salivary amylase concentration increases successively within the preset monitoring time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is marked as starting to eat;

[0129]

[0130] The number of time series data of salivary amylase concentration in the preset time period is m+1, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, c(t m ) is t m The salivary amylase concentration at the moment, firstDifferenceThreshold is the first concentration difference threshold, and firstThreshold is the first concentration threshold;

[0131] When the salivary amylase concentration decreases successively during the monitoring preset time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is marked as stopping eating;

[0132]

[0133] The number of time series data of salivary amylase concentration in the preset time period is m+1, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, c(t m ) is t mThe salivary amylase concentration at the moment, secondDifferenceThreshold is the second concentration difference threshold, and secondThreshold is the second concentration threshold;

[0134] If the behavior mark of the subject to be tested is detected as starting to eat, but the behavior mark of the subject to be tested is not detected as stopping eating, then determining that the behavior mark of the subject to be tested is continuing to eat;

[0135] When it is determined that the behavior of the subject to be tested is marked as starting to eat or continuing to eat, the relationship expression between the salivary amylase concentration c(t0) at the current time t0 and the total amount of food g(t0) consumed from the time when the behavior of the subject to be tested is marked as starting to eat to the current time t0 is obtained, and the relationship expression is:

[0136]

[0137] c0 is the initial salivary amylase concentration in the unfed state; k1 and k2 are correction factors; w is the penalty coefficient, which defaults to 0;

[0138] The total amount of food intake of the subject at the current moment is determined according to the relational expression.

[0139] In a possible implementation, the blood glucose prediction unit 33 is also used to obtain the current blood glucose measurement value of the subject to be tested through a blood glucose monitoring device; obtain the historical injection data of the subject to be tested through an injection device or a cloud database; and obtain the blood glucose prediction value of the subject to be tested at a target moment within a preset time period in the future based on the total amount of food intake, the current blood glucose measurement value and the historical injection data of the subject to be tested.

[0140] In a possible implementation, the dosage output unit 34 is further configured to calculate the drug infusion dosage using the following formula:

[0141]

[0142] Wherein, dose is the infusion dose of the drug, t p is the target time within the preset time period, predictedBg(t p ) is the target time t p The predicted blood glucose value, targetBg is the target blood glucose value, and isf is the drug sensitivity coefficient of the object to be tested.

[0143] In a possible implementation, the dosage output unit 34 is further used to issue a prompt to infuse the drug infusion dose in a large dose when the behavior of the subject to be tested is monitored as the start of eating or the continuous eating, or the behavior of the subject to be tested is monitored as the time from the switch to the stop eating is a first time, and the first time does not exceed a threshold; when the behavior of the subject to be tested is monitored as the time from the switch to the stop eating is greater than a threshold and is the second time without eating, issue a prompt to infuse the drug infusion dose at a basal rate.

[0144] Through the embodiments of the present application, based on the salivary amylase concentration and behavioral markers of the subject to be tested, blood sugar changes are predicted and the corresponding insulin supplement amount is calculated, which can solve the problems of difficulty in establishing and updating the meal model; it can achieve a good estimation of the behavioral markers of the eating state of the subject to be tested, thereby improving the accuracy of blood sugar prediction; and the early prediction of blood sugar values ​​can achieve more timely and effective control of high blood sugar that may occur after a meal.

[0145] Figure 4 A hardware structure diagram of a blood glucose monitoring device is shown.

[0146] like Figure 4 As shown, the blood glucose monitoring device of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown), a memory 41, wherein the memory 41 stores a computer program 42 that can be run on the processor 40. When the processor 40 executes the computer program 42, the steps in the above method embodiment are implemented, such as Figure 1 Alternatively, when the processor 40 executes the computer program 42, the functions of the modules / units in the above-mentioned device embodiments are realized.

[0147] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the blood glucose monitoring device. In other embodiments of the present application, the blood glucose monitoring device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0148] The blood glucose monitoring device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of a blood glucose monitoring device and does not constitute a limitation of the blood glucose monitoring device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the server may also include an input sending device, a network access device, a bus, etc.

[0149] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0150] The processor 40 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 40 is a cache memory. The memory may store instructions or data that the processor 40 has just used or cyclically used. If the processor 40 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor 40, and thus improves the efficiency of the system.

[0151] In some embodiments, the memory 41 may be an internal storage unit of the blood glucose monitoring device, such as a hard disk or memory of the blood glucose monitoring device. The memory 41 may also be an external storage device of the blood glucose monitoring device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the blood glucose monitoring device. Further, the memory 41 may also include both an internal storage unit and an external storage device of the blood glucose monitoring device. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program code of a computer program, etc. The memory 41 may also be used to temporarily store data that has been sent or is to be sent.

[0152] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0153] It should be noted that the above structure is only an example, and based on different application scenarios, it may also include other physical structures, and the physical structure of the electronic device is not limited here.

[0154] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] like Figure 5 As shown, the embodiment of the present application further provides a blood glucose monitoring system 5 , which includes the above-mentioned blood glucose monitoring device 4 and also includes a user terminal 6 , which is used to display the drug infusion dose output by the blood glucose monitoring device 4 .

[0156] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0157] An embodiment of the present application provides a computer program product. When the computer program product runs on a device, the device can implement the steps in the above-mentioned method embodiments when the computer program product is executed.

[0158] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0159] The electronic device, electrical equipment, computer storage medium, and computer program product provided in the above-mentioned embodiments of the present application are all used to execute the methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects corresponding to the methods provided above, and will not be repeated here.

[0160] It should be understood that the above is only to help those skilled in the art better understand the embodiments of the present application, rather than to limit the scope of the embodiments of the present application. According to the above examples given, those skilled in the art can obviously make various equivalent modifications or changes. For example, some steps in each embodiment of the above method may be unnecessary, or some new steps may be added. Or a combination of any two or any multiple embodiments of the above. Such modifications, changes or combined solutions also fall within the scope of the embodiments of the present application.

[0161] It should also be understood that the division of the methods, situations, categories and embodiments in the embodiments of the present application is only for the convenience of description and should not constitute a special limitation. The features of various methods, categories, situations and embodiments can be combined without contradiction.

[0162] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0163] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0164] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0165] Finally, it should be noted that the above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A blood glucose monitoring method, characterized in that: include: Obtaining time series data of salivary amylase concentration of the subject to be tested; Determining the total amount of food intake of the subject at the current moment according to the time series data of the salivary amylase concentration of the subject; Obtaining a predicted blood sugar value of the subject at a target time within a preset time period according to the total amount of food consumed, the current blood sugar measurement value of the subject, and historical injection data; Obtaining the target blood sugar value of the subject at the target time; According to the predicted blood glucose value and the target blood glucose value, a drug infusion dose for adjusting the blood glucose monitoring value of the subject to be tested is determined.

2. The method according to claim 1, characterized in that The step of obtaining the time series data of the salivary amylase concentration of the subject to be tested comprises: Obtain the time series data of raw salivary amylase concentration; Performing linear interpolation and sliding filtering processing on the original salivary amylase concentration time series data to obtain a processed salivary amylase concentration time series of the object to be tested, wherein the number of salivary amylase concentration time series data is n+1; The expression of the salivary amylase concentration time series of the object to be tested is: (c(t n ),c(t n-1 ),...,c(t0)) Wherein, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, and n is a positive integer greater than or equal to 1.

3. The method according to claim 2, characterized in that Determining the total amount of food intake of the subject at the current moment according to the time series data of the salivary amylase concentration of the subject to be tested comprises: Acquire the salivary amylase concentration within a preset time period according to the time series data of the salivary amylase concentration of the subject to be tested; When the salivary amylase concentration increases successively within the preset monitoring time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is marked as starting to eat; The number of time series data of salivary amylase concentration within the preset time period is m+1, m is a positive integer greater than or equal to 1 and less than or equal to n, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, c(t m ) is t m The salivary amylase concentration at the moment, firstDifferenceThreshold is the first concentration difference threshold, and firstThreshold is the first concentration threshold; When the salivary amylase concentration decreases successively during the preset monitoring time period and satisfies the concentration difference threshold condition, it is determined that the current behavior of the subject to be tested is stopped eating; The number of time series data of salivary amylase concentration in the preset time period is m+1, t0 is the current moment, c(t0) is the salivary amylase concentration at the current moment, c(t m ) is t m The salivary amylase concentration at the moment, secondDifferenceThreshold is the second concentration difference threshold, and secondThreshold is the second concentration threshold; If the behavior mark of the subject to be tested is detected as starting to eat, but the behavior mark of the subject to be tested is not detected as stopping eating, then determining that the behavior mark of the subject to be tested is continuing to eat; When it is determined that the behavior of the subject to be tested is marked as starting to eat or continuing to eat, the relationship expression between the salivary amylase concentration c(t0) at the current time t0 and the total amount of food g(t0) consumed from the time when the behavior of the subject to be tested is marked as starting to eat to the current time t0 is obtained, and the relationship expression is: c0 is the initial salivary amylase concentration in the unfed state; k1 and k2 are correction factors; w is the penalty coefficient, which defaults to 0; The total amount of food intake of the subject at the current moment is determined according to the relational expression.

4. The method according to claim 3, characterized in that According to the total amount of food intake, the current blood sugar measurement value of the subject to be tested and the historical injection data, the predicted blood sugar value of the subject to be tested at a target time within a preset time period is obtained, including: Obtaining the current blood sugar measurement value of the subject to be measured through a blood sugar monitoring device; Acquiring historical injection data of the subject to be tested through an injection device or a cloud database; The predicted blood sugar value of the subject to be tested at a target moment in a future preset time period is obtained based on the total amount of food consumed, the blood sugar measurement value at the current moment and the historical injection data of the subject to be tested.

5. The method according to claim 4, characterized in that The step of determining the drug infusion dose for adjusting the blood glucose monitoring value of the subject to be tested according to the predicted blood glucose value and the target blood glucose value comprises: The drug infusion dose is calculated by the following formula: Wherein, dose is the infusion dose of the drug, t p is the target time within the preset time period, predictedBg(t p ) is the target time t p The predicted blood glucose value, targetBg is the target blood glucose value, and isf is the drug sensitivity coefficient of the object to be tested.

6. The method according to claim 5, characterized in that The method further comprises: When the behavior marker of the subject to be tested is the start of eating or the continuous eating, or the behavior marker of the subject to be tested is the time from switching to stopping eating for a first time, and the first time does not exceed a threshold, a prompt is issued to infuse the drug infusion dose in a large dose; When the behavior marker of the subject to be tested is monitored to indicate that the duration from switching to the stopping of eating exceeds a threshold and is the second duration of not eating, a prompt is issued to infuse the drug infusion dose at a basal rate.

7. A blood glucose monitoring device, characterized in that: The method comprises a unit for implementing the method as claimed in any one of claims 1 to 6.

8. A blood glucose monitoring device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 6 when executing the computer program.

9. A blood sugar monitoring system, characterized in that: It includes the blood glucose monitoring device as described in claim 8, and also includes a user terminal; the user terminal is used to display the drug infusion dose output by the blood glucose monitoring device.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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