CN112438704A - Calibration system of physiological parameter monitor - Google Patents
Calibration system of physiological parameter monitor Download PDFInfo
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- CN112438704A CN112438704A CN202010615256.9A CN202010615256A CN112438704A CN 112438704 A CN112438704 A CN 112438704A CN 202010615256 A CN202010615256 A CN 202010615256A CN 112438704 A CN112438704 A CN 112438704A
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1486—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using enzyme electrodes, e.g. with immobilised oxidase
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- A61B2560/02—Operational features
- A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
- A61B2560/0228—Operational features of calibration, e.g. protocols for calibrating sensors using calibration standards
Abstract
The invention provides a calibration system of a physiological parameter monitor, which is characterized by comprising the following components: the monitoring module is used for monitoring and acquiring original physiological parameter information of a to-be-detected object, the monitoring module is loaded with a calibration algorithm, and the monitoring module calibrates the original physiological parameter information based on the calibration algorithm to generate calibrated physiological parameter information; the collection module is used for collecting blood of a to-be-detected object and acquiring reference physiological parameter information in the blood; and the updating module is used for acquiring the original physiological parameter information, the calibration physiological parameter information and the reference physiological parameter information and updating the calibration algorithm based on the original physiological parameter information, the calibration physiological parameter information and the reference physiological parameter information. Therefore, the updating module can acquire and update the calibration algorithm based on the original physiological parameter information, the calibration physiological parameter information and the reference physiological parameter information, so that the calibration algorithm can be calibrated according to the reference physiological parameter information.
Description
Technical Field
The invention relates to a calibration system of a physiological parameter monitor.
Background
Diabetes is a series of metabolic disorder syndromes of sugar, protein, fat, water, electrolyte and the like, and is caused by hypofunction of pancreatic islets, insulin resistance and the like caused by the action of various pathogenic factors such as genetic factors, immune dysfunction, microbial infection and toxins thereof on organisms. If diabetes is not well controlled, complications such as ketoacidosis, lactic acidosis, chronic renal failure and retinopathy may arise. With the increasing incidence of diabetes, diabetes has become a public health problem worldwide.
Diabetes is a high-incidence disease with a prevalence rate higher than 10% in modern society. Prolonged hyperglycemia can cause a range of diabetes-related complications, and hypoglycemia can cause coma and the like, even life-threatening. Blood glucose monitoring is a very important part of diabetes management, and can significantly reduce the risk of diabetic complications.
The existing blood sugar monitoring modes mainly comprise glycated hemoglobin and blood sugar monitoring. The glycosylated hemoglobin reacts for 2-3 months, the short-term blood glucose concentration cannot be observed, and the timely control of the blood glucose is realized. The blood glucose monitoring can only obtain a single-point blood glucose value, and can not obtain a short-term relatively comprehensive blood glucose value, so that the comprehensive control of the blood glucose is realized; complicated operation procedures, painful user experience of finger blood collection and the like are needed, and the user is required to specify a detection plan for the finger blood and blood glucose monitoring for multiple times every day, so that the compliance of the patient on regular blood glucose monitoring is poor.
Continuous blood glucose monitoring is a developing direction for diabetics to perform blood glucose monitoring. The current blood sugar concentration can be reflected in real time, and continuous and comprehensive blood sugar values can be obtained, so that the blood sugar control of patients and doctors is conveniently guided. The traditional continuous blood glucose monitoring needs 1-2 times or more frequent finger blood glucose monitoring for calibration every day, and great inconvenience is brought to patients.
The continuous blood glucose detection device calibrated by the calibration algorithm can monitor blood glucose concentration for a long time after being pricked painlessly once, blood glucose collection is not required to be frequently performed by blood glucose monitoring, but the calibration algorithm set when leaving the factory is still greatly limited, for example, the user has different physiques, the required calibration algorithm is different, or when the body temperature of the user is higher, or the detection device is influenced by other adverse factors, the calibration algorithm cannot be adjusted according to the special conditions.
Disclosure of Invention
The present invention has been made in view of the above-described conventional circumstances, and an object of the present invention is to provide a calibration system for a physiological parameter monitor, which is capable of adjusting a calibration algorithm and has excellent adaptability.
To this end, the present disclosure provides a calibration system for a physiological parameter monitor, comprising: the monitoring module is used for monitoring and acquiring original physiological parameter information of a to-be-detected object, the monitoring module is loaded with a calibration algorithm, and the monitoring module calibrates the original physiological parameter information based on the calibration algorithm to generate calibrated physiological parameter information; the collection module is used for collecting blood of the object to be detected and acquiring reference physiological parameter information in the blood; an update module that obtains the original physiological parameter information, the calibrated physiological parameter information, and the reference physiological parameter information, and updates the calibration algorithm based on the original physiological parameter information, the calibrated physiological parameter information, and the reference physiological parameter information.
In the calibration system of the physiological parameter monitor, the calibration algorithm can calibrate the original physiological parameter information acquired by the monitoring module and generate the calibrated physiological parameter information, and in this case, the updating module can acquire and update the calibration algorithm based on the original physiological parameter information, the calibrated physiological parameter information and the reference physiological parameter information, so that the calibration algorithm can be calibrated according to the reference physiological parameter information to improve the adaptability of the calibration system.
Further, in the calibration system according to the present invention, optionally, the update module is disposed in the monitoring module. Thus, the calibration algorithm in the monitoring module can be updated in time.
In addition, in the calibration system according to the present invention, optionally, the update module compares the calibration physiological parameter information with the reference physiological parameter information, and obtains a calibration parameter based on the original physiological parameter information and the calibration physiological parameter information to update the calibration algorithm. Therefore, the calibration parameters can be obtained through the updating module and the calibration algorithm can be updated to further improve the adaptability of the calibration system.
In addition, in the calibration system according to the present invention, optionally, the acquisition module is separable from the monitoring module. Therefore, the blood of the object to be detected can be collected through the collection module.
In addition, in the calibration system according to the present invention, optionally, the monitoring module includes a glucose sensor, and the raw physiological parameter information is blood glucose concentration information obtained by the glucose sensor. Therefore, the monitoring module can obtain the blood glucose concentration information of the object to be detected.
In addition, in the calibration system according to the present invention, the calibration parameter may optionally include at least one of an initial sensitivity, a sensitivity drift, a sensitivity attenuation coefficient, and a temperature coefficient of the glucose sensor. This can improve the reliability of the calibration algorithm.
In addition, in the calibration system according to the present invention, the glucose sensor may optionally have a dextranase layer and a semipermeable membrane disposed on the dextranase layer, and the initial sensitivity may be related to the mass, volume, thickness, activity of the dextranase layer in the glucose sensor, and the membrane thickness and diffusion coefficient of the semipermeable membrane. Thus, the calibration parameters can be controlled by controlling the relevant parameters of the glucoamylase layer and the semipermeable membrane.
In addition, in the calibration system according to the present invention, optionally, the acquisition module is a fingertip blood glucose meter, and the reference physiological parameter information is obtained by the fingertip blood glucose meter. Therefore, more accurate blood glucose concentration information can be obtained through blood.
In addition, in the calibration system according to the present invention, optionally, the monitoring module monitors and acquires the original physiological parameter information in the tissue fluid of the subject. Therefore, the original physiological parameter information of the object to be measured can be obtained from the tissue fluid.
In addition, in the calibration system according to the present invention, optionally, the calibration parameter further includes a correlation coefficient between the blood glucose concentration information in the interstitial fluid and the blood glucose concentration information in the blood. Thus, blood glucose concentration information in blood can be obtained from blood glucose concentration information in interstitial fluid.
According to the present invention, it is possible to provide a calibration system for a physiological parameter monitor, which can adjust a calibration algorithm and has excellent adaptability.
Drawings
Embodiments of the present disclosure will now be explained in further detail, by way of example only, with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram illustrating an application scenario of a calibration system of a physiological parameter monitor according to an embodiment of the present disclosure.
Fig. 2 is a signal transmission diagram illustrating a calibration system for a physiological parameter monitor according to an embodiment of the present disclosure.
Fig. 3 is a block diagram illustrating a calibration system for a physiological parameter monitor according to an embodiment of the present disclosure.
Fig. 4 is a block diagram showing a calibration algorithm installed in the calibration system for a physiological parameter monitor according to the embodiment of the present disclosure.
Fig. 5 is a schematic diagram illustrating a glucose sensor configuration of a calibration system for a physiological parameter monitor according to an embodiment of the present disclosure.
Fig. 6 is a schematic diagram illustrating various factors affecting calibration parameters of a calibration system of a physiological parameter monitor according to an embodiment of the present disclosure.
Fig. 7 is a schematic diagram illustrating the relationship between original physiological parameter information and original physiological parameter information in blood according to an embodiment of the present disclosure.
Fig. 8 is a schematic calibration flow diagram illustrating a calibration system for a physiological parameter monitor according to an embodiment of the present disclosure.
The reference numbers illustrate:
1 … calibration system, 10 … monitoring module, 11 … calibration algorithm, 111 … calibration parameters, 12 … glucose sensor, 121 … working electrode, 122 … reference electrode, 123 … counter electrode, S … substrate, 20 … acquisition module, 30 … updating module and 2 … object to be measured.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. In the drawings, the same components or components having the same functions are denoted by the same reference numerals, and redundant description thereof will be omitted.
The invention discloses a calibration system of a physiological parameter monitor. The calibration system of the physiological parameter monitor can adjust the calibration algorithm and has good adaptability. In addition, the calibration system of the physiological parameter monitor according to the present invention may be simply referred to as a calibration system.
Fig. 1 is a schematic diagram illustrating an application scenario of a calibration system 1 of a physiological parameter monitor according to an embodiment of the present disclosure. Fig. 2 is a signal transmission diagram illustrating a calibration system 1 for a physiological parameter monitor according to an embodiment of the present disclosure. Fig. 3 is a block diagram illustrating a calibration system 1 for a physiological parameter monitor according to an embodiment of the present disclosure.
In some examples, as shown in fig. 1 and 2, a calibration system 1 of a physiological parameter monitor according to the present disclosure may include a monitoring module 10, an acquisition module 20, and an update module 30. Here, the monitoring module 10 may be disposed on an arm of the object 2 to be measured (see fig. 1), but examples of the present disclosure are not limited thereto, and the monitoring module 10 may also be disposed at a chest, a leg, an abdomen, or a neck of the object 2 to be measured. The collection module 20 may be used to collect blood of the object 2, for example, may be used to collect finger blood of the object 2.
In some examples, the monitoring module 10 may be used to acquire raw physiological parameter information of the subject 2 and generate calibration physiological parameter information. The collection module 20 may be used to collect blood of the subject 2 to obtain the reference physiological parameter information in the blood. The update module 30 may update the calibration algorithm based on the raw physiological parameter information, the calibration physiological parameter information, and the reference physiological parameter information. The calibration system 1 according to the present disclosure is capable of adjusting the calibration algorithm and has good adaptability.
Fig. 4 is a block diagram showing a calibration algorithm 11 mounted in the calibration system 1 for a physiological parameter monitor according to the embodiment of the present disclosure. Fig. 5 is a schematic diagram showing a configuration of a glucose sensor of the calibration system 1 of the physiological parameter monitor according to the embodiment of the present disclosure.
In some examples, as described above, the calibration system 1 of the physiological parameter monitor may include a monitoring module 10 (see fig. 2 or 3).
In some examples, as shown in fig. 4, the monitoring module 10 may be used to monitor and acquire raw physiological parameter information of the subject 2.
In some examples, the monitoring module 10 may monitor and acquire raw physiological parameter information in the tissue fluid of the subject 2. Thus, the original physiological parameter information of the subject 2 can be acquired from the tissue fluid. In other examples, monitoring module 10 may monitor and acquire raw physiological parameter information in blood of subject 2.
In some examples, the raw physiological parameter information may be blood glucose concentration information. Thereby, the monitoring module 10 can obtain the blood glucose concentration information of the object 2 to be measured.
In some examples, the monitoring module 10 may include a glucose sensor 12 (see fig. 5). The raw physiological parameter information may be blood glucose concentration information obtained by the glucose sensor 12. Thereby, the monitoring module 10 can obtain the blood glucose concentration information of the object 2 to be measured through the glucose sensor 12. However, the present embodiment is not limited thereto, and the original physiological parameter information may be other body fluid component data. For example, by changing the glucose enzyme layer on the glucose sensor 12, it is also possible to acquire body fluid composition data other than glucose. Other body fluid components may be, for example, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase, creatine, creatinine, DNA, fructosamine, glucose, glutamine, growth hormones, ketone bodies, lactate, oxygen, peroxides, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, troponin, and the like.
In other examples, monitoring module 10 may monitor the concentration of a drug in a bodily fluid. For example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, theophylline, and warfarin (warfarin), and the like.
In some examples, the glucose sensor 12 may include a substrate S (see fig. 5), a dextranase layer, and a semi-permeable membrane, which are stacked in sequence.
In some examples, the dextranase layer may react with glucose. A dextranase layer may be disposed on the substrate S.
In some examples, the initial sensitivity of the glucose sensor 12 (described later) is related to the mass, volume, thickness, activity of the glucosidase layer in the glucose sensor 12, and the membrane thickness, diffusion coefficient of the semi-permeable membrane. Thus, the calibration parameters 111 (described later) can be controlled by controlling the relevant parameters of the dextranase layer and the semipermeable membrane.
In some examples, the glucose sensor 12 may be provided with the dextranase layer and the semi-permeable membrane by at least one of spin coating, dip coating, drop coating, and spray coating processes.
In some examples, the substrate S of the glucose sensor 12 may be flexible. This reduces the discomfort of the glucose sensor 12 after implantation in the human body.
In some examples, the substrate S may be a flexible substrate. The flexible substrate may be generally made of at least one of Polyethylene (PE), polypropylene (PP), Polyimide (PI), Polystyrene (PS), polyethylene terephthalate (PET), polyethylene naphthalate (PEN).
In other examples, the flexible substrate S may be substantially made of a metal foil, an ultra-thin glass, a single inorganic thin film, a multi-organic thin film, a multi-inorganic thin film, or the like.
In other examples, substrate S may be a non-flexible substrate. The non-flexible substrate may generally comprise a less conductive ceramic, alumina, silica, or the like. In this case, the glucose sensor 12 having the non-flexible substrate may also have sharp points or sharp edges, thereby enabling the glucose sensor 12 to be implanted into the skin (e.g., superficial skin, etc.) without the need for an auxiliary implantation device (not shown).
In some examples, the thickness of the dextranase layer may be about 0.1 μm to about 100 μm. Preferably, the thickness of the dextranase layer may be about 2 μm to 10 μm, for example 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm or 10 μm.
In one example, the thickness of the dextranase layer may be 10 μm. Under the condition, the thickness of the glucolase is controlled within a certain degree, so that the problems that the adhesion force is reduced due to too much glucolase, the material falls off in vivo, the reaction is insufficient due to too little glucolase, normal glucose concentration information cannot be fed back and the like are solved.
In other examples, the glucolase may be one or more of a glucose oxidase or a glucose dehydrogenase.
In some examples, as described above, the glucose sensor 12 may include a semi-permeable membrane. The semipermeable membrane may control the amount of glucose. The semi-permeable membrane may be disposed over the glucosidase layer.
In some examples, the semi-permeable membrane may be disposed by at least one of spin coating, dip-coating, drop-coating, and spray-coating processes.
In some examples, the semi-permeable membrane may include a diffusion-controlling layer and a tamper-resistant layer laminated on the diffusion-controlling layer. In some examples, the diffusion-control layer may be disposed outside the immunity layer. In the semipermeable membrane, the diffusion control layer can control diffusion of glucose molecules, and the anti-interference layer can prevent diffusion of non-glucose substances. Therefore, tissue fluid or blood components passing through the semipermeable membrane can be reduced firstly, and then the interference object is blocked outside the semipermeable membrane through the anti-interference layer. Common interferents may include uric acid, ascorbic acid, acetaminophen, etc., which are ubiquitous in the body.
In some examples, the semi-permeable membrane may control the rate of passage of glucose molecules, i.e., the semi-permeable membrane may limit the number of glucose molecules in the interstitial fluid or blood that reach the glucosidase layer. Specifically, the diffusion-controlling layer of the semipermeable membrane can effectively reduce the amount of glucose that diffuses into the glucosidase layer by a certain ratio.
In some examples, the semi-permeable membrane may be biocompatible.
In other examples, the glucose sensor 12 may include a biocompatible membrane.
In some examples, the glucose sensor 12 may include a working electrode 121, a reference electrode 122, and a counter electrode 123 (see fig. 5).
In some examples, the glucose sensor 12 after piercing the skin can generate a current signal by a redox reaction of glucose enzyme in the working electrode 121 with glucose in interstitial fluid or blood and looping back with the counter electrode 123.
In other examples, reference electrode 122 may form a known and fixed potential difference with interstitial fluid or blood. In this case, the potential difference between the working electrode 121 and the tissue fluid or blood can be measured by the potential difference formed between the reference electrode 122 and the working electrode 121, so that the voltage generated by the working electrode 121 can be accurately grasped. Therefore, the voltage at the working electrode 121 can be automatically adjusted and maintained to be stable according to the preset voltage value, so that the measured current signal can accurately reflect the glucose concentration value.
In addition, in some examples, the counter electrode 123 may be made of platinum, silver chloride, palladium, titanium, or iridium. Thereby, the electrochemical reaction at the working electrode 121 can be not affected with good conductivity. However, the present embodiment is not limited thereto, and in other examples, the counter electrode 123 may be made of at least one selected from gold, glassy carbon, graphite, silver chloride, palladium, titanium, or iridium. This can reduce the influence on the working electrode 121 while having good conductivity.
In some examples, monitoring module 10 may include an electronic system. The electronic system may be used to store the raw physiological parameter information. In this case, the electronic system may transmit the received original physiological parameter information through wireless communication means, such as bluetooth, wifi, etc.
In some examples, the monitoring module 10 may transmit the acquired raw physiological parameter information to the updating module 30 by way of wireless communication.
In other examples, an external reading device may receive raw physiological parameter information from the electronic system. For example, an external reading device may receive a glucose concentration signal and display the glucose concentration value. In some examples, the glucose concentration value may be represented by a numerical value. In other examples, the reading device may graphically represent a trend of glucose concentration values over a predetermined time period. Additionally, in some examples, the reading device may display information such as pictures, animations, charts, graphs, value ranges, and numerical data.
Further, since the glucose sensor 12 according to the present embodiment can continuously monitor the glucose concentration, the glucose concentration of the human body can be continuously monitored for a long period of time (for example, 1 to 24 days). Additionally, in some examples, the reading device may be a reader or a cell phone APP. In other examples, the reading device may also be an acquisition module 20 (described later).
In some examples, the monitoring module 10 may include a calibration algorithm. In other words, the monitoring module 10 may be loaded with the calibration algorithm 11 (see fig. 4).
In some examples, the monitoring module 10 may calibrate the raw physiological parameter information based on the calibration algorithm 11 to generate calibrated physiological parameter information.
In some examples, the calibration algorithm 11 may be factory-installed, i.e., shipped in the monitoring module 10. In other examples, the calibration algorithm 11 may be downloaded by the user from a location such as a server, algorithm library, or cloud over a network at the time of initial use. In this case, the downloaded calibration algorithm 11 may be adapted according to the personal information of the user, for example, according to the personal information of the user, such as height, weight, age, and reason for use. Therefore, the individuation degree of the calibration algorithm 11 can be improved, and the method is convenient for various people to use so as to improve the adaptability. In this case, the calibration algorithm 11 of the monitoring module 10 may be different for different users.
In some examples, the calibration algorithm 11 has calibration parameters 111. The calibration parameters 111 may be updated. The update method is described later. In this case, after the calibration parameters 111 are updated, the monitoring module 10 may recalibrate the original physiological parameter information based on the calibration algorithm 11 to regenerate the calibrated physiological parameter information.
In some examples, the calibration parameters 111 of the calibration algorithm 11 of the monitoring module 10 may be different for different users.
In some examples, the electronic system of the monitoring module 10 may be used to store calibration physiological parameter information. In this case, the electronic system may transmit the received calibrated physiological parameter information via wireless communication, such as bluetooth, wifi, etc.
In some examples, monitoring module 10 may transmit the calibration physiological parameter information to update module 30 by way of wireless communication.
In some examples, as described above, the calibration system 1 of the physiological parameter monitor may include an acquisition module 20 (see fig. 2 or 3).
In some examples, the collection module 20 may be configured to collect blood of the subject 2 and obtain reference physiological parameter information in the blood.
In some examples, the user or subject can acquire reference physiological parameter information through acquisition module 20 as needed. In other examples, the user or subject may periodically acquire reference physiological parameter information via acquisition module 20. Thereby, the calibration algorithm 11 can be updated with reference physiological parameter information (described later). Specifically, the user may use the acquisition module 20 to acquire the reference physiological parameter information 1 time a day, 1 time a week, etc.
Examples of the present disclosure are not limited thereto, for example, the calibration system 1 may not use the acquisition module 20. I.e. the calibration system 1 may not update the calibration algorithm 11. In this case, for example, type II diabetic, pre-diabetic or even non-diabetic patients, such users who do not require a high requirement for measurement accuracy can obtain good measurement values without the need to update the calibration algorithm 11. Thus, the user of the above type can be facilitated.
In some examples, acquisition module 20 is separable from monitoring module 10. Thus, the blood of the object 2 can be collected by the collection module 20.
In other examples, the acquisition module 20 may be disposed in the monitoring module 10. This enables the acquisition module 20 to perform acquisition at any time.
In some examples, the reference physiological parameter information may be blood glucose concentration information. Thus, the collection module 20 can obtain blood glucose concentration information. The present disclosure is not so limited and the reference physiological parameter information may be other blood component data.
In some examples, acquisition module 20 may be a fingertip glucose meter. The reference physiological parameter information may be blood glucose concentration information obtained by a fingertip blood glucose meter. Thereby, more accurate blood glucose concentration information can be obtained from the blood (see fig. 1).
In some examples, the flow of use of acquisition module 20 is as follows: the fingertip is pricked by the disposable needle, a blood sample of the fingertip blood is obtained by using the test paper or the straw, then the blood sample is placed into the detection device of the collection module 20, and finally, the blood glucose concentration information (such as the blood glucose concentration value) in the blood sample can be obtained.
In some examples, acquisition module 20 may have a wireless communication unit, such as bluetooth, WIFI, and the like. This enables transmission or reception of signals by wireless communication. In this case, the acquisition module 20 may transmit the acquired reference physiological parameter information to the update module 30 by wireless communication.
Fig. 6 is a schematic diagram illustrating various factors affecting calibration parameters 111 of the calibration system 1 of the physiological parameter monitor according to the embodiment of the present disclosure. Fig. 7 is a schematic diagram illustrating the relationship between original physiological parameter information and original physiological parameter information in blood according to an embodiment of the present disclosure. Fig. 8 is a schematic diagram illustrating a calibration flow of the calibration system 1 for a physiological parameter monitor according to an embodiment of the present disclosure.
In some examples, as described above, the calibration system 1 of the physiological parameter monitor may include an update module 30 (see fig. 2 or 3).
In some examples, update module 30 may obtain raw physiological parameter information, calibrated physiological parameter information, and reference physiological parameter information. Specifically, the update module 30 may receive the raw physiological parameter information and the calibrated physiological parameter information output by the monitoring module 10. The update module 30 may receive the reference physiological parameter information output by the acquisition module 20.
In some examples, the update module 30 may update the calibration algorithm 11 based on the raw physiological parameter information, the calibration physiological parameter information, and the reference physiological parameter information.
In some examples, the update module 30 may be disposed in the monitoring module 10. This enables the calibration algorithm 11 in the monitoring module 10 to be updated in a timely manner.
In other examples, update module 30 may be disposed in the cloud. The update module 30 disposed in the cloud may store the calibration parameters (described later) of the calibration algorithm 11 of each user in a database, classify the users, and iteratively update the calibration parameters of the calibration algorithms 11 of the users of the same type to generate the calibration algorithms 11 more suitable for the people of the type. Thereby, the reliability of the calibration algorithm 11 is further improved.
In some examples, update module 30 may compare the calibration physiological parameter information to the reference physiological parameter information. In some examples, it is determined whether the calibrated physiological parameter information and the reference physiological parameter information converge by a comparison of the calibrated physiological parameter information and the reference physiological parameter information.
In some examples, the update module 30 may obtain the calibration parameters 111 based on the raw physiological parameter information and the calibration physiological parameter information to update the calibration algorithm 11. Thereby, the calibration parameters 111 can be obtained by the update module 30 and the calibration algorithm 11 can be updated to further improve the adaptability of the calibration system 1.
In some examples, the calibration parameters 111 are updated if the calibration physiological parameter information does not converge with the reference physiological parameter information. Thus, the calibration algorithm 11 can be updated by updating the calibration parameters 111.
In some examples, as shown in fig. 6, the calibration parameters 111 may include at least one of an initial sensitivity, a sensitivity drift, a decay coefficient of sensitivity, and a temperature coefficient of the glucose sensor 12. This can improve the reliability of the calibration algorithm 11.
In other examples, calibration parameters 111 may include specific relationships between the sensor and sensitivity, baseline, drift, impedance/temperature relationships, and specific relationships of the site (abdomen, arm, etc.) at which the sensor is implanted. In this case, the relationship between the calibration parameters 111 and the respective factors can be more comprehensively considered, so that different calibration algorithms 11 can be set for different users. Therefore, the application range of the physiological parameter monitor can be improved. In particular, the site of implantation of the sensor may be affected by different vessel densities.
In some examples, the calibration algorithm 11 may perform calibration based on the distribution information of the calibration parameters 111. Specifically, the distribution information includes: a range, a distribution function, a distribution parameter (mean, standard deviation, skewness, etc.), a generalized function, a statistical distribution, a distribution, or the like, that represents a plurality of possible values of the calibration information. The a priori calibration distribution information together contains a range or distribution of values (e.g., describing their associated probabilities, probability density functions, likelihoods, or frequency of occurrence) provided prior to a particular calibration process for calibration of the sensor (e.g., sensor data).
In some examples, as shown in fig. 7, calibration parameters 111 may include a correlation coefficient of blood glucose concentration information in interstitial fluid and blood glucose concentration information in blood. Thus, blood glucose concentration information in blood can be obtained from blood glucose concentration information in interstitial fluid. In some examples, there is a delay in blood glucose concentration information in interstitial fluid from blood glucose concentration information in blood. In other examples, the blood glucose concentration information in the interstitial fluid may be obtained by a method of kinetic compensation.
The following describes the calibration flow of the calibration system 1 in detail with reference to fig. 8:
in some examples, as shown in fig. 8, a user may use the monitoring module 10 to continuously monitor the object 2 to be measured (or the user himself or herself), after the monitoring is started, the monitoring module 10 can measure the original physiological parameter information, and the monitoring module 10 calibrates the original physiological parameter information through the calibration algorithm 11 mounted therein, so as to obtain the calibrated physiological parameter information and output the calibrated physiological parameter information.
In some examples, as shown in fig. 8, based on the user's needs or when the user has doubt about the calibrated physiological parameter information, it may be selected to collect blood of the subject 2 through the collection module 20 and generate the reference physiological parameter information.
In some examples, if the acquisition module 20 generates or acquires the reference physiological parameter information, the calibration physiological parameter information and the reference physiological parameter information are matched by the update module 30. Specifically, the updating module 30 is used to obtain the original physiological parameter information, the calibration physiological parameter information and the reference physiological parameter information, and the calibration physiological parameter information and the reference physiological parameter information are matched. Wherein matching may refer to comparing the calibrated physiological parameter information with the reference physiological parameter information.
In some examples, whether the calibration physiological parameter information and the reference physiological parameter information are converged is judged, and if yes, the calibration physiological parameter information is output and the calibration process is ended; if not, the calibration parameters in the calibration algorithm 11 are updated. Specifically, the updating module 30 updates the calibration parameters 111 in the calibration algorithm 11 based on the original physiological parameter information, the calibration physiological parameter information, and the reference physiological parameter information, so as to obtain the calibration algorithm 11 more suitable for the object 2 to be measured.
In some examples, the original physiological parameter information is calibrated again by the updated calibration algorithm 11 and calibrated physiological parameter information is generated. Specifically, in the monitoring module 10, the updated calibration algorithm 11 calibrates the original physiological parameter information to generate the calibration physiological parameter information.
In some examples, the regenerated calibration physiological parameter information is matched with the reference physiological parameter information until convergence, the calibration physiological parameter information is output, and the calibration process is ended.
In the calibration system 1 of the physiological parameter monitor according to the present disclosure, the calibration algorithm 11 can calibrate the original physiological parameter information acquired by the monitoring module 10, and generate the calibrated physiological parameter information, in this case, the updating module 30 can acquire and update the calibration algorithm 11 based on the original physiological parameter information, the calibrated physiological parameter information, and the reference physiological parameter information, so that the calibration algorithm 11 can calibrate according to the reference physiological parameter information to improve the adaptability of the calibration system.
While the invention has been specifically described above in connection with the drawings and examples, it will be understood that the above description is not intended to limit the invention in any way. Those skilled in the art can make modifications and variations to the present invention as needed without departing from the true spirit and scope of the invention, and such modifications and variations are within the scope of the invention.
Claims (10)
1. A calibration system of a physiological parameter monitor is characterized in that,
the method comprises the following steps:
the monitoring module is used for monitoring and acquiring original physiological parameter information of a to-be-detected object, the monitoring module is loaded with a calibration algorithm, and the monitoring module calibrates the original physiological parameter information based on the calibration algorithm to generate calibrated physiological parameter information;
the collection module is used for collecting blood of the object to be detected and acquiring reference physiological parameter information in the blood;
an update module that obtains the original physiological parameter information, the calibrated physiological parameter information, and the reference physiological parameter information, and updates the calibration algorithm based on the original physiological parameter information, the calibrated physiological parameter information, and the reference physiological parameter information.
2. The calibration system of claim 1, wherein:
the update module is disposed in the monitoring module.
3. The calibration system of claim 1, wherein:
the updating module compares the calibration physiological parameter information with the reference physiological parameter information and obtains a calibration parameter based on the original physiological parameter information and the calibration physiological parameter information to update the calibration algorithm.
4. The calibration system of claim 1, wherein:
the acquisition module is separable from the monitoring module.
5. The calibration system of claim 3, wherein:
the monitoring module comprises a glucose sensor, and the original physiological parameter information is blood glucose concentration information obtained by the glucose sensor.
6. The calibration system of claim 5, wherein:
the calibration parameter includes at least one of an initial sensitivity, a sensitivity drift, a decay coefficient of sensitivity, and a temperature coefficient of the glucose sensor.
7. The calibration system of claim 5, wherein:
the glucose sensor is provided with a glucose enzyme layer and a semipermeable membrane arranged on the glucose enzyme layer, and the initial sensitivity is related to the mass, the volume, the thickness and the activity of the glucose enzyme layer in the glucose sensor and the membrane thickness and the diffusion coefficient of the semipermeable membrane.
8. The calibration system of claim 1, wherein:
the acquisition module is a fingertip blood glucose meter, and the reference physiological parameter information is obtained by the fingertip blood glucose meter.
9. The calibration system of claim 5, wherein:
the monitoring module monitors and acquires the original physiological parameter information in the tissue fluid of the object to be detected.
10. The calibration system of claim 9, wherein:
the calibration parameters further include a correlation coefficient of blood glucose concentration information in interstitial fluid and blood glucose concentration information in blood.
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