CN113397538A - Optical blood glucose algorithm of wearable embedded system - Google Patents
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Abstract
The invention relates to the technical field of optical blood sugar, in particular to an optical blood sugar algorithm of a wearable embedded system, which comprises the following steps: s1, the energy metabolism conservation method can obtain the correlation between the human body heat production and blood sugar, metabolic rate, blood flow rate and blood oxygen, and deduces the mathematical description formula BG of the blood sugar according to the principle. And S2, calculating the metabolic heat production quantity H at the corresponding moment by monitoring the real-time heat dissipation quantity. And S3, substituting the metabolic heat production quantity H in the step S2 into the mathematical description formula BG in the step S1 to obtain the final blood sugar measurement relational expression. The invention has the propulsion effect in the field of near-infrared detection of blood sugar concentration and hemoglobin concentration, is suitable for an algorithm for detecting human blood sugar by an energy metabolism conservation method, effectively avoids the influence of environmental factors on the detection of the blood sugar content, further improves the noninvasive blood sugar detection precision, achieves the aim of applying the technology to clinical standards, and realizes the industrialization of noninvasive blood sugar instruments.
Description
Technical Field
The invention relates to the technical field of optical blood glucose algorithms, in particular to an optical blood glucose algorithm of a wearable embedded system.
Background
Diabetes mellitus is a disease in which blood sugar deviates from a normal value due to abnormal insulin secretion in a human body, and is remarkably characterized by hyperglycemia. Research shows that diabetes easily causes various concurrent diseases, such as retinopathy, cardiovascular and cerebrovascular diseases, cataract, heart disease and the like. The current main method for clinically treating diabetes is to adjust the injection amount of insulin in a human body by frequently monitoring the blood glucose concentration. Blood sugar detection means are classified into invasive, minimally invasive and non-invasive. Compared with invasive and minimally invasive blood glucose concentration detection technologies, noninvasive blood glucose detection can be used as a long-term detection method, and the method has the advantages of low cost, no wound, capability of continuous real-time measurement and the like.
In the prior art, for example, Chinese patent numbers are: CN 112022167A, "a noninvasive blood glucose detection method based on a spectrum sensor", breakthrough the noninvasive blood glucose detection technology using a spectrum sensor, adopt the improved near infrared spectrum transmission measurement technology, the original calibration mechanism, no material consumption, accurate clinical verification data, little influence from the individual state and environmental change of human body, less than +/-15% error, and comparable with the detection equipment with wound; the method comprises the following steps: the method comprises the following steps: a spectrum sensor is designed at the fingertip position, and an LED is designed at the other side relative to the fingertip position; step two: a tunable filter of a Fabry-Perot interferometer is adapted in the spectrum sensor, and the optical receiving range of the tunable filter is adjusted to reach the nm level; step three: light rays emitted by a 1650nmLED penetrate through human tissues and are collected by a spectral sensor with the wavelength of 1350nm-1650 nm; step four: the light emitted by 1720nm LED is collected by 1550nm-1850nm spectrum sensor after passing through human tissue. The LED and the spectrum sensor with different wavelengths can be designed to work in two devices simultaneously, so that the problems of insufficient power of the LED and interference of the spectrum sensor are avoided.
However, in the prior art, when the spectral sensor is used for measuring the blood sugar of a human body, the error of blood sugar data is only caused by weak spectral signals, meanwhile, the tissue structures of different parts of the human body are greatly different, and the change of measurement conditions such as the temperature, the humidity, the incident area of light, the angle and the like of the measurement part directly influences the light propagation and directly influences the detection precision.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the optical blood glucose algorithm of the wearable embedded system, which plays a role in propelling the near-infrared detection of the blood glucose concentration and the hemoglobin concentration field, is suitable for the algorithm for detecting the blood glucose of a human body by an energy metabolism conservation method, effectively avoids the influence of environmental factors on the blood glucose content detection, further improves the noninvasive blood glucose detection precision, achieves the aim of applying the technology to the clinical standard, and realizes the industrialization of noninvasive blood glucose meters.
In order to achieve the purpose, the invention provides the following technical scheme:
an optical blood glucose algorithm of a wearable embedded system, comprising:
s1, the energy metabolism conservation method can obtain the correlation between the human body heat production and blood sugar, metabolic rate, blood flow rate and blood oxygen, and deduces the mathematical description formula BG of the blood sugar according to the principle.
And S2, calculating the metabolic heat production quantity H at the corresponding moment by monitoring the real-time heat dissipation quantity.
And S3, substituting the metabolic heat production quantity H in the step S2 into the mathematical description formula BG in the step S1 to obtain the final blood sugar measurement relational expression.
S4, selecting a finger tip measuring probe to measure energy metabolism parameter variables and blood sugar minimal invasive value variables of the finger tip of the individual to be measured to perform three types of PLS modeling.
And S5, eliminating noise by using a PLS prediction algorithm and improving the quality of the model.
Further, the mathematical model of energy metabolism conservation method in step S1 is:
H＝f(BG，SPO2bv, HB, ξ), where BG is the blood glucose concentration, HR is the heart rate, H is the metabolic heat production, Bv is the blood flow velocity; SPO2 is the blood oxygen saturation, xi is the algorithm correction factor, then the formula is shifted to get the mathematical description formula BG-f of blood sugar1(H，SPO2，Bv，HB，ξ)。
Further, the step S1 includes the following steps:
s101, H is M, wherein H is metabolic heat generation, M is metabolic heat dissipation, and the unit is (W/M)2) And monitoring the real-time heat dissipation capacity to obtain the metabolic heat production quantity H at the corresponding moment.
S102, introducing a human body heat balance formula M which is S +/-C- (+/-W) +/-R +/-E, wherein S is a human body local heat load and is basically zero in a short time, C is a convection heat exchange quantity between a human body local surface and the environment, W is energy consumed by the human body local acting, the external acting is not considered when only basic metabolism is measured, R is heat quantity exchanged between the human body skin surface and environment radiation, and E is heat quantity exchanged by human body skin evaporation.
S103, step S102 shows that M ═ F1(C, R, E), wherein C is the difference between the local surface of the human body and the ambient temperature, R is the difference between the surface of the human body and the ambient radiation temperature, and E is the difference between the surface of the human body and the ambient humidity.
Further, substituting M obtained in step S103 into formula BG for mathematical description of blood glucose yields BG ═ F (C, R, E, SPO)2Bv, HR, M, ξ), in which the blood oxygen saturation is SPO2The blood oxygen saturation calculated for the near infrared light measures the heart rate HR through a theory of a bielastic cavity, and calculates the blood flow velocity Bv and xi as algorithm correction factors by combining the measured blood pressure and waveform characteristic parameters.
Further, the blood oxygen saturation SPO2The calculation principle is that an infrared emission source emits two near infrared lights with different wavelengths to penetrate through a monitoring part, the lights are attenuated in different degrees, and an infrared receiving end performs spectral analysis on received signals and combines spectral characteristics and pulse rate calculation.
Further, in step S4, the blood glucose levels monitored in the sample are classified into a low blood glucose class a having a blood glucose level of 0 to 6, a medium blood glucose class b having a blood glucose level of 6 to 9, and a high blood glucose class c having a blood glucose level of 9 to 30, and the energy metabolism parameters in the three kinds of saccharide sample data are used as input parameters for PLS.
Furthermore, the minimally invasive blood glucose value is used as a calibration parameter, the minimally invasive blood glucose value calibration parameter is used as an expected output value of a partial least square method, and an original model of the non-invasive blood glucose value estimation system is established. Obtaining a hypoglycemia PLS prediction algorithm, a hyperglycemia PLS prediction algorithm and a hyperglycemia PLS prediction algorithm, which are respectively as follows:
BGa＝Fa(C，R，E，SPO2，Bv，HR，M，ξ)；
BGb＝Fb(C，R，E，SPO2，Bv，HR，M，ξ)；
BGc＝Fc(C，R，E，SPO2，Bv，HR，M，ξ)。
an optical blood glucose monitoring device of a wearable embedded system, comprising: the finger stall comprises a first protective shell, a second protective shell, a connecting piece and a finger stall body, wherein the bottom of the front end of the first protective shell is movably installed at the top of the connecting piece, the bottom of the connecting piece is movably installed at the top of the front end of the second protective shell, and the inner sides of the first protective shell and the second protective shell are connected with the outer side of the finger stall body in a clamping mode.
Further, the bottom of the finger sleeve is clamped with a finger surface temperature sensor, the outer side of the top of the finger sleeve is symmetrically and fixedly provided with near infrared light emitting diodes, the outer side of the near-infrared light-emitting diode is fixedly provided with a lithium battery, the outer side of the lithium battery is clamped with the inner side of the first protective shell, the front end of the first protective shell is uniformly provided with through holes, the inner side of the front end of the first protective shell is fixedly provided with an installation rod, a microprocessor is clamped at the outer side of the mounting rod, an environment humidity sensor is fixedly mounted at the top of the microprocessor, a space radiation temperature sensor is fixedly arranged at the bottom of the microprocessor, a photoelectric receiving sensor is fixedly arranged at the rear end of the top of the microprocessor, the rear end of the bottom of the microprocessor is fixedly provided with a wireless transmitter, the top of the rear end of the first protective shell is provided with an observation groove, and a transparent cover is fixedly arranged inside the observation groove.
Advantageous effects
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
1. the optical blood glucose algorithm of the wearable embedded system is suitable for measuring physiological parameters of a human body, has a good fitting effect, effectively improves the precision of non-invasive blood glucose measurement, obtains that the metabolic rate of the human body is obviously related to the true value of the blood glucose concentration through correlation analysis, and verifies the theory of detecting the non-invasive blood glucose by an energy metabolism conservation method.
2. The optical blood glucose algorithm of the wearable embedded system plays a role in propelling in the field of near-infrared detection of blood glucose concentration and hemoglobin concentration, is suitable for an algorithm for detecting human blood glucose by an energy metabolism conservation method, effectively avoids the influence of environmental factors on blood glucose content detection, further improves the noninvasive blood glucose detection precision, achieves the standard of applying the technology to clinic, and realizes industrialization of noninvasive blood glucose meters.
3. The optical blood sugar monitoring equipment of the wearable embedded system uses the arrangement of the embedded system, the kernel is much smaller than the traditional operating system, the designed monitoring equipment meets the daily wearing requirements of people, simultaneously has strong support capability on real-time tasks, can complete the simultaneous working tasks of multiple sensors during blood sugar monitoring, and has shorter interrupt response time, thereby reducing the execution time of internal codes and the real-time inner core to the minimum.
Drawings
FIG. 1 is a flow chart of an optical blood glucose algorithm for a wearable embedded system;
FIG. 2 is a flowchart of step S4 in the optical blood glucose algorithm of the wearable embedded system;
FIG. 3 is a diagram of the internal back end structure of an optical blood glucose monitoring device of a wearable embedded system;
FIG. 4 is a front-end internal block diagram of an optical blood glucose monitoring device of a wearable embedded system;
fig. 5 is a structural diagram of the appearance of an optical blood glucose monitoring device of a wearable embedded system.
Illustration of the drawings:
1. a first protective shell; 2. a second protective shell; 3. a connecting member; 4. finger stall; 12. a finger surface temperature sensor; 13. a near-infrared light emitting diode; 5. a lithium battery; 101. a through hole; 10. mounting a rod; 6. a microprocessor; 7. an ambient humidity sensor; 9. a spatial radiation temperature sensor; 8. a photoelectric receiving sensor; 11. a wireless transmitter; 102. an observation tank; 103. a transparent cover.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention will be further described with reference to the following examples.
Example 1
Referring to fig. 1-2, an optical blood glucose algorithm of a wearable embedded system includes:
step one, an energy metabolism conservation method can obtain the correlation between human body heat production and blood sugar, metabolic rate, blood flow rate and blood oxygen, and deduces a mathematical description formula BG of the blood sugar according to the principle.
The energy metabolism conservation mathematical module is as follows: h ═ f (BG, SPO)2Bv, HB, ξ), where BG is the blood glucose concentration, HR is the heart rate, H is the metabolic heat production, Bv is the blood flow velocity; SPO2 is the blood oxygen saturation, xi is the algorithm correction factor, then the formula is shifted to obtain the mathematical description formula of blood sugar
BG＝f1(H，SPO2，Bv，HB，ξ)。
And step two, calculating the metabolic heat production H at the corresponding moment by monitoring the real-time heat dissipation.
Under normal conditions, the body temperature of a human body is kept within a certain range, the sum of heat generated by the human body and dissipated by evaporation, conduction, convection, radiation and the like is equal, and therefore the metabolic heat generation H at the corresponding moment can be obtained by monitoring the real-time heat dissipation capacity.
A human body heat balance formula M is introduced, wherein S is a human body local heat load and is basically zero in a short time, C is a convection heat exchange quantity between a human body local surface and the environment, W is energy consumed by the human body local acting, the external acting is not considered when only basic metabolism is measured, R is heat quantity exchanged between the human body skin surface and environment radiation, and E is heat quantity exchanged by human body skin evaporation.
Obtained by the steps as above, M ═ F1(C, R, E), wherein C is the difference between the local surface of the human body and the ambient temperature, R is the difference between the surface of the human body and the ambient radiation temperature, and E is the difference between the surface of the human body and the ambient humidity.
And step three, substituting the metabolic heat production quantity H in the step S2 into the mathematical description formula BG in the step S1 to obtain a final blood sugar measurement relational expression.
Substituting the M obtained in the second step into a formula BG for mathematical description of blood sugar to obtain BG ═ F (C, R, E, SPO)2Bv, HR, M, ξ), in which the blood oxygen saturation is SPO2Calculated for near infrared light, blood oxygen saturation, SPO2The calculation principle is that an infrared emission source emits two near infrared lights with different wavelengths to penetrate through a monitoring part, the lights are attenuated in different degrees, an infrared receiving end carries out spectrum analysis on received signals, the heart rate HR is measured through a double-elastic-cavity theory in combination with spectral characteristics and pulse rate calculation, and blood flow velocity Bv and xi are calculated in combination with measured blood pressure and waveform characteristic parameters as algorithm correction factors.
And step four, selecting a finger tip measuring probe to measure the energy metabolism parameter variable and the blood sugar minimal invasive value variable of the finger tip of the individual to be measured to carry out three types of PLS modeling.
The blood sugar content monitored by the sample is divided into a hypoglycemia type a with blood sugar value of 0-6, a hyperglycemia type b with blood sugar value of 6-9 and a hyperglycemia type c with blood sugar value of 9-30, and the energy metabolism parameters in the sample data of the three kinds of saccharides are used as input parameters of the corresponding PLS.
And fifthly, eliminating noise by using a PLS prediction algorithm and improving the quality of the model.
And establishing an original model of the noninvasive blood glucose value estimation system by taking the minimally invasive blood glucose value as a calibration parameter and taking the minimally invasive blood glucose value calibration parameter as an expected output value of a partial least square method. Obtaining a hypoglycemia PLS prediction algorithm, a hyperglycemia PLS prediction algorithm and a hyperglycemia PLS prediction algorithm, which are respectively as follows:
BGa＝Fa(C，R，E，SPO2，Bv，HR，M，ξ)；
BGb＝Fb(C，R，E，SPO2，Bv，HR，M，ξ)；
BGc＝Fc(C，R，E，SPO2，Bv，HR，M，ξ)。
example 2
Referring to fig. 3-5, an optical blood glucose monitoring device of a wearable embedded system includes: the finger stall comprises a first protective shell 1, a second protective shell 2, a connecting piece 3 and a finger stall 4, wherein the bottom of the front end of the first protective shell 1 is movably installed with the top of the connecting piece 3, the bottom of the connecting piece 3 is movably installed with the top of the front end of the second protective shell 2, and the inner sides of the first protective shell 1 and the second protective shell 2 are clamped with the outer side of the finger stall 4; this equipment is installed in measurement personnel's left hand index finger end, at first separates first protective case 1 and second protective case 2 along connecting piece 3, inserts the finger in dactylotheca 4 after, because of the volute spiral spring in connecting piece 3 resumes elastic deformation, and presss from both sides tightly first protective case 1 and second protective case 2 in the finger end once more, accomplishes equipment and dresses.
A finger surface temperature sensor 12 is clamped at the bottom of the finger sleeve 4, near-infrared light-emitting diodes 13 are symmetrically and fixedly mounted on the outer side of the top of the finger sleeve 4, a lithium battery 5 is fixedly mounted on the outer side of the near-infrared light-emitting diodes 13, the outer side of the lithium battery 5 is clamped with the inner side of the first protective shell 1, through holes 101 are uniformly formed in the front end of the first protective shell 1, a mounting rod 10 is fixedly mounted on the inner side of the front end of the first protective shell 1, a microprocessor 6 is clamped on the outer side of the mounting rod 10, an environment humidity sensor 7 is fixedly mounted on the top of the microprocessor 6, a space radiation temperature sensor 9 is fixedly mounted on the bottom of the microprocessor 6, a photoelectric receiving sensor 8 is fixedly mounted on the rear end of the top of the microprocessor 6, a wireless transmitter 11 is fixedly mounted on the rear end of the first protective shell 1, an observation groove; the finger surface temperature sensor 12 is responsible for monitoring the finger surface temperature and providing data for monitoring, the near-infrared light emitting diode 13 monitors the blood sugar content of blood inside the finger through infrared light, the space radiation temperature sensor 9 monitors the environment temperature outside the finger, the environment humidity sensor 7 monitors the environment humidity outside the finger end, when the blood sugar content of a monitoring person needs to be calculated, the temperature measured by each sensor can be input into the microprocessor 6, and the blood sugar content can be calculated by using the algorithm model obtained in the embodiment 1.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. An optical blood glucose algorithm of a wearable embedded system, comprising:
s1, obtaining that the human body heat production quantity is related to blood sugar, metabolic rate, blood flow rate and blood oxygen by an energy metabolism conservation method, and deducing a mathematical description formula BG of the blood sugar according to a principle;
s2, calculating the metabolic heat production quantity H at the corresponding moment by monitoring the real-time heat dissipation quantity;
s3, substituting the metabolic heat production quantity H in the step S2 into the mathematical description formula BG in the step S1 to obtain a final blood sugar determination relational expression;
s4, selecting a finger tip measuring probe to measure energy metabolism parameter variables and blood sugar minimal invasive value variables of the finger tip of the individual to be measured to perform three types of PLS modeling;
and S5, eliminating noise by using a PLS prediction algorithm and improving the quality of the model.
2. The optical blood glucose algorithm of the wearable embedded system according to claim 1, wherein: step S1The mathematical model of the medium energy metabolism conservation method is as follows: h ═ f (BG, SPO)2Bv, HB, ξ), where BG is the blood glucose concentration, HR is the heart rate, H is the metabolic heat production, Bv is the blood flow velocity; SPO2 is the blood oxygen saturation, ξ isCorrecting the factor by algorithm, and then shifting the formula to obtain the mathematical description formula of the blood sugar
BG＝f1(H，SPO2，Bv，HB，ξ)。
3. The optical blood glucose algorithm of the wearable embedded system according to claim 1, wherein: the step S1 includes the steps of:
s101, H is M, wherein H is metabolic heat generation, M is metabolic heat dissipation, and the unit is (W/M)2) Monitoring the real-time heat dissipation capacity to obtain the metabolic heat production quantity H at the corresponding moment;
s102, introducing a human body heat balance formula M which is S +/-C- (+/-W) +/-R +/-E, wherein S is a human body local heat load, C is a convection heat exchange quantity between a human body local surface and the environment, W is energy consumed by the human body local acting, R is heat quantity radiated and exchanged between the human body skin surface and the environment, and E is heat quantity exchanged by human body skin evaporation;
s103, step S102 shows that M ═ F1(C, R, E), wherein C is the difference between the local surface of the human body and the ambient temperature, R is the difference between the surface of the human body and the ambient radiation temperature, and E is the difference between the surface of the human body and the ambient humidity.
4. The optical blood glucose algorithm of the wearable embedded system according to claim 3, wherein: substituting M obtained in step S103 into formula BG for mathematical description of blood glucose to obtain BG ═ F (C, R, E, SPO)2Bv, HR, M, ξ), in which the blood oxygen saturation is SPO2The blood oxygen saturation calculated for the near infrared light measures the heart rate HR through a theory of a bielastic cavity, and calculates the blood flow velocity Bv and xi as algorithm correction factors by combining the measured blood pressure and waveform characteristic parameters.
5. The optical blood glucose algorithm of the wearable embedded system according to claim 4, wherein: blood oxygen saturation SPO2The calculation principle is that an infrared emission source emits two near infrared lights with different wavelengths to pass through a monitoring part, and the lights are attenuated to different degrees, namely redAnd the external receiving end performs spectral analysis on the received signal and calculates the pulse rate by combining spectral characteristics.
6. The optical blood glucose algorithm of the wearable embedded system according to claim 2, wherein: in step S4, the blood glucose levels monitored in the sample are classified into a hypoglycemic type a with a blood glucose level of 0 to 6, a hyperglycemic type b with a blood glucose level of 6 to 9, and a hyperglycemic type c with a blood glucose level of 9 to 30, and the energy metabolism parameters in the three types of sugar sample data are used as input parameters for the PLS.
7. The optical blood glucose algorithm of the wearable embedded system according to claim 6, wherein: and establishing an original model of the noninvasive blood glucose value estimation system by taking the minimally invasive blood glucose value as a calibration parameter and taking the minimally invasive blood glucose value calibration parameter as an expected output value of a partial least square method. Obtaining a hypoglycemia PLS prediction algorithm, a hyperglycemia PLS prediction algorithm and a hyperglycemia PLS prediction algorithm, which are respectively as follows:
BGa＝Fa(C，R，E，SPO2，Bv，HR，M，ξ)；
BGb＝Fb(C，R，E，SPO2，Bv，HR，M，ξ)；
BGc＝Fc(C，R，E，SPO2，Bv，HR，M，ξ)。
8. an optical blood glucose monitoring device of a wearable embedded system, comprising: the finger stall comprises a first protective shell (1), a second protective shell (2), a connecting piece (3) and a finger stall (4), wherein the bottom of the front end of the first protective shell (1) is movably mounted with the top of the connecting piece (3), the bottom of the connecting piece (3) is movably mounted with the top of the front end of the second protective shell (2), and the inner sides of the first protective shell (1) and the second protective shell (2) are connected with the outer side of the finger stall (4) in a clamping mode.
9. The optical blood glucose monitoring device of a wearable embedded system according to claim 8, wherein a finger surface temperature sensor (12) is clamped at the bottom of the finger stall (4), near infrared light emitting diodes (13) are symmetrically and fixedly mounted at the outer side of the top of the finger stall (4), a lithium battery (5) is fixedly mounted at the outer side of the near infrared light emitting diodes (13), the outer side of the lithium battery (5) is clamped with the inner side of a first protective shell (1), through holes (101) are uniformly formed in the front end of the first protective shell (1), a mounting rod (10) is fixedly mounted at the inner side of the front end of the first protective shell (1), a microprocessor (6) is clamped at the outer side of the mounting rod (10), an environment humidity sensor (7) is fixedly mounted at the top of the microprocessor (6), and a space radiation temperature sensor (9) is fixedly mounted at the bottom of the microprocessor (6), the top rear end fixed mounting of microprocessor (6) has photoelectric receiving sensor (8), the bottom rear end fixed mounting of microprocessor (6) has wireless transmitter (11), observation groove (102) have been seted up to the rear end top of first protective case (1), the inside fixed mounting of observation groove (102) has translucent cover (103).
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