CN116942153A - Dual-wavelength near infrared measurement method for blood glucose nondestructive measurement - Google Patents
Dual-wavelength near infrared measurement method for blood glucose nondestructive measurement Download PDFInfo
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- 210000004369 blood Anatomy 0.000 title claims abstract description 132
- 239000008280 blood Substances 0.000 title claims abstract description 132
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 title claims abstract description 82
- 239000008103 glucose Substances 0.000 title claims abstract description 82
- 238000005259 measurement Methods 0.000 title claims abstract description 52
- 238000000691 measurement method Methods 0.000 title claims abstract description 20
- 125000002791 glucosyl group Chemical group C1([C@H](O)[C@@H](O)[C@H](O)[C@H](O1)CO)* 0.000 claims abstract description 17
- 238000012549 training Methods 0.000 claims abstract description 12
- 238000007619 statistical method Methods 0.000 claims abstract description 11
- 230000009977 dual effect Effects 0.000 claims description 16
- 238000000034 method Methods 0.000 claims description 13
- 238000001514 detection method Methods 0.000 claims description 10
- 238000003062 neural network model Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000010238 partial least squares regression Methods 0.000 claims description 4
- 230000035945 sensitivity Effects 0.000 claims description 4
- 230000001066 destructive effect Effects 0.000 claims 9
- 230000005540 biological transmission Effects 0.000 abstract description 6
- 230000008859 change Effects 0.000 description 7
- 210000001519 tissue Anatomy 0.000 description 5
- 230000000472 traumatic effect Effects 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 210000000988 bone and bone Anatomy 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 206010012601 diabetes mellitus Diseases 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 210000003205 muscle Anatomy 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 208000017667 Chronic Disease Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 238000001237 Raman spectrum Methods 0.000 description 1
- 208000028990 Skin injury Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
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- 208000026106 cerebrovascular disease Diseases 0.000 description 1
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- 238000012067 mathematical method Methods 0.000 description 1
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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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
- A61B5/1455—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 optical sensors, e.g. spectral photometrical oximeters
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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
- A61B5/14532—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
Abstract
The application discloses a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement, which comprises the following reference steps: acquiring a reference time t 0 Corresponding to blood glucose related parameters; the measurement step is to obtain the relevant blood sugar parameters corresponding to the measurement time t 1; based on the reference time t 0 The blood sugar value G at the time t1 is calculated by a statistical method model corresponding to the blood sugar related parameter and the related blood sugar parameter corresponding to the measurement time t1 1 . The application has the advantages that: the fingertip blood glucose signal related parameters are measured in a dual-wavelength transmission mode, and a prediction result is obtained based on a pre-trained model related to the blood glucose signal, so that the blood glucose prediction result is more accurate, and the pre-trained blood glucose model considers the interference of the non-blood glucose signal and takes the interference into the model to enable the output of the model after training to be more accurate and reliable.
Description
Technical Field
The application relates to the field of blood glucose detection, in particular to a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement.
Background
Diabetes has become the third most serious chronic disease in the world following tumor and cardiovascular and cerebrovascular disease that endangers human health and causes death. At present, no method for radically treating diabetes exists, frequent measurement of blood sugar is needed, blood sugar detection is mainly carried out in clinic and at home in a invasive mode, long-term skin injury is easily caused to a patient in invasive detection, the operation is complicated, the compliance of the patient is reduced, and the real-time long-term monitoring of blood sugar value cannot be met. In recent years, noninvasive wearable blood glucose measuring instruments are widely focused on, and are rapidly developed, so that the noninvasive wearable blood glucose measuring instruments become hot spots for domestic and foreign researches.
The application discloses a blood glucose measuring device and a blood glucose measuring method, as disclosed in patent application number 202010675203.6, which aim to solve the technical problems that in blood glucose measurement in the prior art, raman spectrum signals are weak and are easily influenced by fluorescence and parasitic light, and measurement sensitivity and accuracy are low; the light source unit comprises a laser light source, a mounting cylinder and a plurality of light source optical fibers; the detection unit comprises a mounting column, a detection optical fiber, a spectrum imaging assembly and a computer. The blood glucose measuring device disclosed by the patent can realize non-invasive measurement of the tissue to be measured, minimally invasive measurement of the internal components of the tissue to be measured, and can select different measuring modes, namely reflection mode measurement and transmission mode measurement, according to different characteristics of the tissue to be measured, and the measuring modes suitable for different tissue parts are selected, so that the measurement is more accurate and reliable. However, the technology disclosed in this patent still cannot avoid interference signals from other components in the human muscle tissue, bone, and blood.
The traditional near infrared spectroscopy is to correlate the spectrum signal with detection information with the detected component concentration, establish a correction model through a mathematical method, and finally predict the blood sugar concentration through the correction model and the spectrum information of the detected sample. Because the effective blood sugar signal is weak in the process of measuring blood sugar by using near infrared light, and the interference signal of other components in muscle tissue, bones and blood of a human body is strong, the measurement result of the traditional near infrared spectroscopy is unstable.
Disclosure of Invention
The application aims to overcome the defects of the prior art and provides a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement, which is used for reducing interference and outputting accurate blood glucose data.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows: a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement comprises,
the reference steps are as follows: acquiring a reference time t 0 Corresponding to blood glucose related parameters;
the measuring step comprises the following steps: acquiring a relevant blood glucose parameter corresponding to a measurement time t 1;
based on the reference time t 0 The blood sugar value G at the time t1 is calculated by a statistical method model corresponding to the blood sugar related parameter and the related blood sugar parameter corresponding to the measurement time t1 1 。
The reference step includes obtaining t 0 Time-of-day calibration blood glucose level G 0 Finger tip temperatureSensor temperature->And calculates a zero value coefficient alpha.
t 0 Time-of-day calibration blood glucose level G 0 Blood is obtained for detection by invasive means.
Sensor temperatureThe temperature of the infrared signal receiving sensor is behind the infrared light projection fingertip of dual wavelength.
In the reference step, the light having the wavelength lambda is passed through b 、λ a Infrared light sourceAfter the infrared signal is projected to the finger, the infrared signal is received by the infrared receiving sensor, and a corresponding result is output:let->And calculating a corresponding zero value coefficient alpha.
The measuring step comprises the following steps: acquiring a measurement time t 1 Fingertip temperature of (2)Sensor temperature->Sum and difference compensation result I abs The method comprises the steps of carrying out a first treatment on the surface of the Wherein the sensor temperature->The temperature of the infrared light receiving sensor; at time T1, the passing wavelength is lambda b 、λ a After the infrared light is projected to the finger in sequence, the infrared receiving sensor receives the infrared signals and outputs corresponding results: />Differential compensation result->
The statistical method model comprises a neural network module or a partial least squares regression model.
The statistical method model is a trained neural network model, and the model training adopts a plurality of groups of calibration fingertip temperatures obtained through model trainingCalibrating sensor temperature +.>Calibrating blood sugar G 0 Fingertip temperature during measurement->Sensor temperature +.>And t 1 Time compensated differential result I abs Blood glucose level G at time t1 1 The data trains the model.
Wavelength lambda b 、λ a The near infrared light is selected according to the blood sugar sensitivity level.
Selecting near infrared light sensitive to blood glucose signals as dominant wavelength lambda a Selecting near infrared light insensitive to blood glucose signals as background wavelength lambda b ，λ a Is 1550nm, lambda b Is 1310nm.
The application has the advantages that: the fingertip blood glucose signal related parameters are measured in a dual-wavelength transmission mode, a prediction result is obtained based on a pre-trained model related to the blood glucose signal, so that the blood glucose prediction result is more accurate, the pre-trained blood glucose model considers the interference of a non-blood glucose signal, the pre-trained blood glucose model is considered to enter the model, so that the output of the model after training is more accurate and reliable, the fingertip blood glucose signal is measured in a dual-wavelength transmission mode through analyzing the absorption spectrum characteristics of fingertip tissues and fingertip blood, the influence of the non-blood glucose signal on the measurement result is reduced through a differential method, and the method is suitable for any measurement moment under the condition that the physical condition of a measured individual does not change obviously after reference calibration, and non-invasive prediction is performed.
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The contents of the drawings and the marks in the drawings of the present specification are briefly described as follows:
FIG. 1 is a schematic flow chart of the principle of the application.
Detailed Description
The following detailed description of the application refers to the accompanying drawings, which illustrate preferred embodiments of the application in further detail.
Example 1:
a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement comprises the steps of acquiring fingertip blood glucose related parameter information in a projection mode by adopting dual wavelengths;
the method comprises the following steps of:
the reference steps are as follows:
acquiring a blood sugar related parameter corresponding to a reference time t 0: calibrating blood glucose value G0 and fingertip temperatureSensor temperature->
Reference t 0 Calibration blood glucose value G obtained at moment 0 Finger tip temperatureSensor temperature->And calculates a zero value coefficient alpha. Wherein t is 0 Time-of-day calibration blood glucose level G 0 Blood is obtained in a traumatic mode for detection, and blood glucose parameters are obtained and analyzed in a traumatic mode;
the finger tip temperature refers to the temperature of the finger tip which can be measured and obtained by a thermometer, an electronic thermometer and the like;
sensor temperatureThe temperature of the infrared signal receiving sensor is behind the infrared light projection fingertip of dual wavelength.
In the reference step, the light having the wavelength lambda is passed through b 、λ a After the infrared light is projected to the finger in sequence, the infrared receiving sensor receives the infrared signals and outputs corresponding results:let->And calculating a corresponding zero value coefficient alpha.
The measuring step comprises the following steps:
acquiring relevant blood glucose parameters corresponding to the measurement time t 1: acquiring a measurement time t 1 Fingertip temperature of (2)Sensor temperature->Calculating differential compensation result I abs ；
Wherein the sensor temperatureFor the temperature of the infrared light receiving sensor, the wavelength lambda is respectively passed at the time t1 b 、λ a After the infrared light is projected to the finger in sequence, the infrared receiving sensor receives the infrared signals and outputs corresponding results:differential compensation result->
And an output step:
this step is based on the reference time t 0 The blood sugar value G at the time t1 is calculated by a statistical method model corresponding to the blood sugar related parameter and the related blood sugar parameter corresponding to the measurement time t1 1 。
The statistical method model of the application comprises a neural network module or a partial least squares regression model. Taking a neural network model as an example: the model training adopts the acquired multiple groups of calibrated fingertip temperaturesCalibrating sensor temperature +.>Calibrating blood sugar G 0 Fingertip temperature during measurement->Sensor temperature +.>And t 1 Time compensated differential result I abs Blood glucose level G at time t1 1 The data trains the model. Reference time t is needed during pre-training 0 Blood is obtained through a traumatic mode corresponding to the blood sugar related parameter, the related blood sugar parameter corresponding to the measurement time t1 and the time t1, blood sugar content data in the blood is analyzed, and the time t is referred to 0 The relevant blood sugar parameters corresponding to the blood sugar relevant parameters and the relevant blood sugar parameters corresponding to the measurement time t1 are respectively obtained by adopting the reference step and the measurement step; the obtained parameters are used as the input of a neural network model, blood is obtained in a traumatic mode at the moment t1, blood sugar content data in the blood is analyzed to be used as the output of the neural network model, and the model is trained through a plurality of groups of input and output data until the training is completed, and the model is used as a statistical method model of the application.
In practical use, only one invasive analysis is required at reference time t0 to obtain blood glucose G0 and fingertip temperature after dual wave inputSensor temperature->And the zero value coefficient alpha is calculated, so that the measurement time t can be obtained only by the measurement step in actual use 1 Fingertip temperature->Sensor temperature->Calculating differential compensation result I abs The input information required by the whole neural network model can be obtained, the neural network model outputs corresponding estimated blood sugar data based on the information, the predicted blood sugar is accurate when the human body does not have great body change, and the parameters of the calibration time can be obtained again after the body change, so that the G0 of the calibration time is obtained only by one wound when the human body is used, and the human body is not damagedHis need not be wounded to obtain the corresponding blood glucose estimate.
The wavelength of the application is lambda b 、λ a The near infrared light is selected according to the blood sugar sensitivity level. Selecting near infrared light sensitive to blood glucose signals as dominant wavelength lambda a Selecting near infrared light insensitive to blood glucose signals as background wavelength lambda b ，λ a Is 1550nm, lambda b Is 1310nm.
The application discloses a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement, which selects dual-wavelength lambda a And lambda (lambda) b Measuring fingertip blood glucose signals in a transmission mode, and obtaining corresponding predicted blood glucose values through a neural network model according to parameters obtained after the projection of the dual wavelengths;
the measuring method of the application establishes an expression capable of reflecting the change of the blood sugar level by analyzing the absorption characteristics of each component of the fingertip, considers the change amount existing in the measuring process, improves the measuring precision, is a beneficial improvement on the existing noninvasive blood sugar detecting technology, and has the advantages of noninvasive, high precision and convenient operation compared with the prior noninvasive blood sugar detection.
The application relates to a dual-wavelength near infrared measurement method for blood glucose nondestructive measurement, which uses dual wavelengths to transmit (incident light is recorded as) Is used for measuring the fingertip blood glucose signal (denoted +.>) The method comprises the following specific steps:
(1) selecting a reference time t 0 Obtaining t 0 Time-of-day calibration blood glucose level G 0 Finger tip temperatureSensor temperature->Zero value coefficient alpha;
(2) measurement of t 1 Obtaining fingertip temperature at time of blood glucose levelSensor temperature->Calculating t 1 Compensation difference result +.>(denoted as I) abs )；
(3) Will calibrate the blood glucose level G 0 Finger tip temperatureSensor temperature->t 1 Fingertip temperature at time->Sensor temperature->And I abs Calculating t by statistical method (neural network, partial least squares regression, etc.) 1 Blood glucose level G at time 1 Typically, as with the neural network approach, the basic steps are: training the existing data through a neural network to obtain a training model, and calibrating the fingertip temperature +.>Calibrating sensor temperature +.>Calibrating blood sugar G 0 Fingertip temperature during measurement->Sensor temperature +.>And t 1 Time of dayCompensating the difference result I abs As input quantity, obtaining final predicted blood glucose value G by training model 1 ；
By analysing the fingertip change (mainly caused by the change of water and glucose content, respectively denoted as ) And an unchanged part (denoted>) Is the absorption relation of (a) to give the expression +.>
At t 1 When the moment is actually measured, only t is calculated 1 Time compensated differential result I abs (wherein) And get t 1 Fingertip temperature at time->Sensor temperature->Invasive measurement is not required; measuring time t 1 In order to finish the initial calibration, the accuracy can meet the requirements at any measurement moment when the physical condition of the tested individual is not changed obviously; the reference data should be recalibrated when significant changes occur.
The application adopts a dual-wavelength transmission measurement method, and selects a t before formal measurement 0 Calibrating the moment and simultaneously obtaining t 0 Time-of-day calibration blood glucose level G 0 Temperature T of calibration 0 At the actual measurement t 1 When the blood glucose level is at the moment, only t is calculated 1 Time-of-day compensating differential resultsRelative to t 0 Time compensated differential result->Is the difference I of (2) abs According to the difference I abs And t 0 Time-of-day calibration blood glucose level G 0 Temperature T of calibration 0 And t 1 Time temperature T 1 T is obtained 1 Blood glucose value at the time.
Fig. 1 is a schematic flow chart of an embodiment of the present application, and the method includes: using lambda a And lambda (lambda) b As a light source, measuring a blood glucose signal in the blood of a fingertip, and performing the steps of:
step S1: selecting an initial time t 0 Obtaining a calibrated blood glucose value G by taking as a reference 0 Calibrating temperature T 0 ；
Step S2: establishing the absorption expression of each component of the fingertip
Step S3: let t 0 Time differential compensationObtaining a zero value coefficient alpha;
step S4: calculating t 1 Differential compensation results for time of day
Step S5: calibration temperature T 0 Blood glucose calibration G 0 Measuring temperature T 1 And t 1 Time compensated differential result I abs Output t through a pre-trained neural network model 1 Blood glucose value at the time.
It is obvious that the specific implementation of the present application is not limited by the above-mentioned modes, and that it is within the scope of protection of the present application only to adopt various insubstantial modifications made by the method conception and technical scheme of the present application.
Claims (10)
1. A dual-wavelength near infrared measurement method for blood glucose nondestructive measurement is characterized by comprising the following steps of: comprising the steps of (a) a step of,
the reference steps are as follows: acquiring a reference time t 0 Corresponding to blood glucose related parameters;
the measurement step is to obtain the relevant blood sugar parameters corresponding to the measurement time t 1;
based on the reference time t 0 The blood sugar value G at the time t1 is calculated by a statistical method model corresponding to the blood sugar related parameter and the related blood sugar parameter corresponding to the measurement time t1 1 。
2. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 1, wherein:
the reference step includes obtaining t 0 Time-of-day calibration blood glucose level G 0 Finger tip temperatureSensor temperature->And calculates a zero value coefficient alpha.
3. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 2, wherein: t is t 0 Time-of-day calibration blood glucose level G 0 Blood is obtained for detection by invasive means.
4. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 2, wherein: sensor temperatureThe temperature of the infrared signal receiving sensor is behind the infrared light projection fingertip of dual wavelength.
5. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 2, wherein: in the reference step, respectively byWavelength lambda b 、λ a After the infrared light is projected to the finger in sequence, the infrared receiving sensor receives the infrared signals and outputs corresponding results:let->And calculating a corresponding zero value coefficient alpha.
6. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 1, wherein:
the measuring step is to obtain the measuring time t 1 Fingertip temperature of (2)Sensor temperature->Sum and difference compensation result I abs The method comprises the steps of carrying out a first treatment on the surface of the Wherein the sensor temperature->The temperature of the infrared light receiving sensor; at time T1, the passing wavelength is lambda b 、λ a After the infrared light is projected to the finger in sequence, the infrared receiving sensor receives the infrared signals and outputs corresponding results: />Differential compensation result->
7. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 1, wherein: the statistical method model comprises a neural network module or a partial least squares regression model.
8. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 7, wherein: the statistical method model is a trained neural network model, and the model training adopts a plurality of groups of calibration fingertip temperatures obtained through model trainingCalibrating sensor temperature +.>Calibrating blood sugar G 0 Fingertip temperature during measurement->Sensor temperature +.>And t 1 Time compensated differential result I abs Blood glucose level G at time t1 1 The data trains the model.
9. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 5 or 6, wherein: wavelength lambda b 、λ a The near infrared light is selected according to the blood sugar sensitivity level.
10. A dual wavelength near infrared measurement method for non-destructive measurement of blood glucose according to claim 9, wherein: selecting near infrared light sensitive to blood glucose signals as dominant wavelength lambda a Selecting near infrared light insensitive to blood glucose signals as background wavelength lambda b ，λ a Is 1550nm, lambda b Is 1310nm.
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