A small-scale non-invasive blood glucose detection system and method based on frequency shift and amplitude constraints

Abstract

This invention relates to the field of biomedical detection equipment technology, and proposes a small, non-invasive blood glucose detection system and method based on frequency shift and amplitude constraints. The system and method include: a field-programmable gate array (FPGA), a digital-to-analog converter (DAC), a voltage-controlled oscillator (VCO), a dual-frequency complementary open-circuit resonant ring (CSRR) sensor, a detector, an analog-to-digital converter (ADC), an LCD screen, a digital tube, and a power supply module. After frequency sweeping, the detector output voltage data stored in RAM is converted into a power value through calculation, according to S... 21 The amplitude of the sensor's transmission coefficient is calculated using a parameter formula. The FPGA controls the digital display to show the sensor's resonant frequency and resonance depth via buttons. This invention is primarily applied in the design and manufacture of medical devices.

Description

Technical Field

[0001] This invention relates to the field of biomedical testing equipment technology, specifically to a small, non-invasive blood glucose testing system and method based on frequency shift and amplitude constraints. Background Technology

[0002] Diabetes mellitus is a metabolic disease characterized by high blood sugar and accompanied by various complications. Monitoring blood glucose levels can effectively help diabetic patients manage their condition. Currently, invasive blood glucose monitoring methods are commonly used, but these methods pose risks of pain and infection to diabetic patients. Furthermore, frequent invasive blood glucose testing can lead to decreased adherence to medical advice, reduced self-management, and ultimately, a worsening of the condition.

[0003] Non-invasive blood glucose testing methods overcome the drawbacks of invasive methods, causing no pain or discomfort during measurement and making blood glucose monitoring safer and more comfortable for diabetic patients. Among various non-invasive blood glucose testing methods, microwave detection methods demonstrate considerable performance and lower manufacturing costs. Furthermore, microwave sensors are small in size, making them suitable for integration into wearable glucose sensing systems. Microwave sensors detect blood glucose concentration primarily through changes in resonant frequency and depth. Blood glucose concentration detection based on resonant frequency changes has high accuracy in predicting low glucose concentrations, while blood glucose concentration detection based on resonant depth changes has high accuracy in predicting high glucose concentrations.

[0004] Currently, vector network analyzers (VNAs) are mainly used to detect the resonant frequency and resonant depth of sensors. VNAs are very accurate in measuring the resonant frequency and resonant depth of sensors, but their high price and large size have greatly hindered the widespread adoption of non-invasive blood glucose testing based on microwave sensors. Summary of the Invention

[0005] In response to the need for widespread adoption of existing non-invasive blood glucose testing technologies, this invention aims to propose a small non-invasive blood glucose testing system and method based on frequency shift and amplitude constraints. The technical solution adopted by this invention is as follows: A small non-invasive blood glucose testing system based on frequency shift and amplitude constraints includes: a field-programmable gate array (FPGA), a digital-to-analog converter (DAC), a voltage-controlled oscillator (VCO), a dual-frequency complementary open-circuit resonant ring (CSRR) sensor, a detector, an analog-to-digital converter (ADC), an LCD screen, a digital tube, and a power supply module responsible for power supply.

[0006] The FPGA controls the DAC to output a sweep voltage signal, which is used as the tuning voltage of the VCO, thereby enabling the VCO to generate a microwave signal corresponding to the tuning voltage.

[0007] The microwave signal generated by the VCO is input to the microwave sensor. The detector measures the power of the microwave signal output by the sensor and converts the power value into an analog voltage signal, which is then output to the ADC. The voltage signal is converted into a digital signal by the ADC module, read by the FPGA, and stored in the FPGA's random access memory (RAM). After storage, the FPGA increases the DAC output voltage, and the ADC module reads the new analog voltage signal. This process is repeated until the DAC output voltage reaches the maximum value of the VCO input tuning voltage, at which point the system completes the frequency sweep of the sensor's operating frequency band.

[0008] The transmission coefficient S of the sensor 21 The parameters are as follows:

[0009]

[0010] Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN This is the power input to the VCO sensor. After frequency sweeping, the detector output voltage data stored in RAM is converted into a power value through calculation, based on S. 21 The amplitude of the sensor's transmission coefficient is obtained from the parameter formula, and the FGP controls the LCD display of the sensor S. 21 The amplitude curve of the parameter, S 21 The frequency and amplitude at the parameter resonance peak are the sensor's resonant frequency and resonance depth. The FGPA controls the digital tube to display the sensor's resonant frequency and resonance depth according to the key.

[0011] The dual-frequency complementary open-circuit resonant ring (CSRR) sensor includes an open / combiner microstrip line, a substrate, and complementary open-circuit resonant ring units. Specifically: the open / combiner microstrip line is etched onto a copper layer on the top of the substrate, and consists of two combiner microstrip lines and two open-circuit microstrip lines; two double-ring circular dual-frequency complementary open-circuit resonant ring (CSRR) units and two single-ring circular dual-frequency complementary open-circuit resonant ring (CSRR) units are etched onto a copper layer on the bottom of the substrate. The double-ring CSRR unit consists of two concentric circular grooves with symmetrical openings, and the single-ring CSRR unit consists of a single circular opening groove. When a microwave signal is applied to the microstrip line, if the symmetry line of the CSRR unit is perpendicular to the axis of the microstrip line, the microstrip line provides electric field excitation to the CSRR unit; if the symmetry line of the CSRR unit is parallel to the axis of the microstrip line, the microstrip line provides both electric and magnetic field excitation to the CSRR unit; the symmetry lines of the four CSRR units are perpendicular to the axis of the open / combiner microstrip line.

[0012] A small, non-invasive blood glucose detection method based on frequency shift and amplitude constraints is implemented using the aforementioned system, and the steps are as follows:

[0013] (1) Place your finger on the dual-frequency CSRR sensor, and the detection system applies a microwave signal to the sensor to measure the sensor's transmission coefficient S. 21 The transmission coefficient S 21 Two resonance peaks are displayed. The minimum points of these two resonance peaks, i.e., the transmission zero points, are named Resonance Mode 1 and Resonance Mode 2. The resonance depth of Resonance Mode 1 and the resonance frequency of Resonance Mode 2 are used as constraints for data fitting to construct a blood glucose concentration prediction model. The expression of the prediction model is as follows:

[0014]

[0015] Where y is the blood glucose concentration x s1 x f2 These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. w0 is the constant to be fitted, and w s1 w f2 These are the weights corresponding to each independent variable;

[0016] (2) After the blood glucose concentration prediction model is constructed, place your finger on the microwave sensor, import the measured resonant frequency and resonant depth into the blood glucose concentration prediction model, and calculate the current blood glucose concentration.

[0017] The detailed steps are as follows:

[0018] (1) After the system is powered on, the ADC and DAC are initialized first. After initialization, the FPGA sets the initial output voltage of the DAC to 0V. After the FPGA completes the setting of the DAC, the FPGA sends a signal that the data transmission is complete to the ADC as the start signal of the ADC. The FPGA controls the ADC to read the output signal of the detector and stores the read data in the RAM of the FPGA. The above operation is repeated until the frequency sweep is completed.

[0019] (2) After the frequency sweep is completed, the excitation voltage value can be converted into frequency according to the mathematical model of the detector output voltage amplitude and the sensor output power. The relationship between the VCO tuning voltage and the output frequency is as follows:

[0020]

[0021] Among them, f VCO V represents the output voltage of the VCO, measured in GHz. T The tuning voltage of the VCO is expressed in volts (V).

[0022] (3) Sensor S 21 The parameters are represented as follows:

[0023]

[0024] Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN It is the power of the VCO input sensor, P IN Taking a typical VCO output power value of 11 dBm, when the detector's input power is between 0 and 10 dBm, the relationship between the power of the signal received by the detector and the input voltage of the ADC module is expressed as follows:

[0025] P OUT =28.6V ADC -33.6

[0026] The S of the sensor at the corresponding frequency point can be calculated using the above formula. 21 Parameters; S for traversing all frequency points 21 The amplitude of the parameters is used to find the excitation voltage and resonance depth corresponding to the resonant point. The content displayed on the digital tube is controlled by the key. After the traversal is completed, the DAC re-outputs the initial sweep voltage signal.

[0027] (4) Place the index finger of the person to be tested in the gap of the pearl cotton foam board. Start timing after eating. Record the resonant frequency and resonant depth of the sensor every 5 minutes. Use a blood glucose meter to perform an invasive measurement. The blood glucose concentration of the sensor and the resonant characteristics of the sensor measured by the system within 60 minutes after the volunteer eats.

[0028] (5) The dataset recorded in the measurement is divided into a training dataset and a test dataset for multiple linear regression analysis. The data sampled at minutes 0, 10, 20, 30, 40, 50, and 60 are used as the training dataset, and the data sampled at minutes 5, 15, 25, 35, 45, and 55 are used as the test dataset.

[0029] (6) Using the resonant frequency and resonant depth of resonant mode 1 and the resonant frequency of resonant mode 2 as features, and blood glucose concentration as the label data, a multiple linear regression equation was fitted. The prediction formula for blood glucose concentration is shown below:

[0030] y = w0 + w s1 x s1 +w f2 x f2

[0031] Where y is the blood glucose concentration x s1 x f2 These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. w0 is the constant to be fitted, and w s1 w f2 These are the weights corresponding to each independent variable;

[0032] The features and beneficial effects of this invention are:

[0033] (1) This invention uses resonance depth and resonance frequency to predict blood glucose concentration, thereby improving the accuracy of blood glucose concentration measurement.

[0034] (2) This invention measures the S of a microwave sensor. 21 The parameters are used to predict blood glucose concentration with minimal interference.

[0035] (3) The present invention achieves the measurement of the resonant frequency and resonant depth of the sensor at a lower cost. Attached Figure Description

[0036] Figure 1 This is a block diagram of a small, non-invasive blood glucose detection system based on frequency shift and amplitude constraints.

[0037] Figure 2 The schematic diagrams of the dual-frequency CSRR sensor for non-invasive blood glucose detection provided by the present invention are as follows: (a) top-level structure; (b) bottom-level structure.

[0038] Figure 3 S21 parameters of the dual-frequency CSRR sensor under no-load conditions in full-wave simulation

[0039] Figure 4 A flowchart illustrating the workflow of a small, non-invasive blood glucose monitoring system based on frequency shift and amplitude constraints;

[0040] Figure 5 A physical image of a small, non-invasive blood glucose detection system based on frequency shift and amplitude constraints;

[0041] Figure 6 Comparison of blood glucose concentration within 60 minutes after a volunteer's meal and the resonant characteristics of the sensor measured by the system: (a) Comparison of the resonant depth of the microwave sensor with blood glucose concentration; (a) Comparison of the resonant frequency of the microwave sensor in resonant mode 2 with blood glucose concentration.

[0042] Figure 7 To predict blood glucose concentration and reference blood glucose concentration for a small, non-invasive blood glucose detection system based on frequency shift and amplitude constraints. Detailed Implementation

[0043] In response to the need for widespread adoption of existing non-invasive blood glucose testing technologies, this invention aims to propose a small non-invasive blood glucose testing system and method based on frequency shift and amplitude constraints. The technical solution adopted by this invention is as follows: A small non-invasive blood glucose testing system based on frequency shift and amplitude constraints includes: a field-programmable gate array (FPGA), a digital-to-analog converter (DAC), a voltage-controlled oscillator (VCO), a dual-frequency complementary open-circuit resonant ring (CSRR) sensor, a detector, an analog-to-digital converter (ADC), an LCD screen, a digital tube, and a power supply module responsible for power supply.

[0044] The FPGA controls the DAC to output a sweep voltage signal, which is used as the tuning voltage of the VCO, thereby enabling the VCO to generate a microwave signal corresponding to the tuning voltage.

[0045] The microwave signal generated by the VCO is input to the microwave sensor. The detector measures the power of the microwave signal output by the sensor and converts the power value into an analog voltage signal, which is then output to the ADC. The voltage signal is converted into a digital signal by the ADC module, read by the FPGA, and stored in the FPGA's random access memory (RAM). After storage, the FPGA increases the DAC output voltage, and the ADC module reads the new analog voltage signal. This process is repeated until the DAC output voltage reaches the maximum value of the VCO input tuning voltage, at which point the system completes the frequency sweep of the sensor's operating frequency band.

[0046] The transmission coefficient S of the sensor 21 The parameters are as follows:

[0047]

[0048] Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN The power input to the VCO sensor is the voltage. After frequency sweep, the detector output voltage data stored in RAM is converted into a power value through calculation. The amplitude of the sensor's transmission coefficient can then be obtained using the formula described above. The FGPA controls the LCD display of sensor S. 21 The amplitude curve of the parameter. S 21 The frequency and amplitude at the parameter resonance peak are the sensor's resonant frequency and resonance depth. The FGPA controls the digital tube to display the sensor's resonant frequency and resonance depth according to the key.

[0049] The dual-frequency complementary open-circuit resonant ring (CSRR) sensor includes an open / combiner microstrip line, a substrate, and complementary open-circuit resonant ring units. Specifically: the open / combiner microstrip line is etched onto a copper layer on the top of the substrate, and consists of two combiner microstrip lines and two open-circuit microstrip lines; two double-ring circular dual-frequency complementary open-circuit resonant ring (CSRR) units and two single-ring circular dual-frequency complementary open-circuit resonant ring (CSRR) units are etched onto a copper layer on the bottom of the substrate. The double-ring CSRR unit consists of two concentric circular grooves with symmetrical openings, and the single-ring CSRR unit consists of a single circular opening groove. When a microwave signal is applied to the microstrip line, if the symmetry line of the CSRR unit is perpendicular to the axis of the microstrip line, the microstrip line provides electric field excitation to the CSRR unit; if the symmetry line of the CSRR unit is parallel to the axis of the microstrip line, the microstrip line provides both electric and magnetic field excitation to the CSRR unit; the symmetry lines of the four CSRR units are perpendicular to the axis of the open / combiner microstrip line.

[0050] A small, non-invasive blood glucose detection method based on frequency shift and amplitude constraints is implemented using the aforementioned system, and the steps are as follows:

[0051] (1) Place your finger on the dual-frequency CSRR sensor, and the detection system applies a microwave signal to the microstrip line of the sensor to measure the sensor's transmission coefficient S. 21 The transmission coefficient S 21 Two resonant peaks are displayed. The minimum points of these two resonant peaks, i.e., the transmission zero points, are named Resonance Mode 1 and Resonance Mode 2. Since blood glucose concentration detection based on resonant frequency variation has high accuracy in predicting low glucose concentrations, while blood glucose concentration detection based on resonant depth variation has high accuracy in predicting high glucose concentrations, and the sensor has high resonant frequency detection sensitivity in the first resonant mode and high resonant depth detection sensitivity in the second resonant mode, the resonant depth of Resonance Mode 1 and the resonant frequency of Resonance Mode 2 are selected as constraints for data fitting to construct a blood glucose concentration prediction model. The expression of the prediction model is as follows:

[0052]

[0053] Where y is the blood glucose concentration x s1 x f2 These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. w0 is the constant to be fitted, and w s1 w f2 Let be the weights corresponding to each independent variable;

[0054] (2) After the blood glucose concentration prediction model is constructed, place your finger on the microwave sensor, import the measured resonant frequency and resonant depth into the blood glucose concentration prediction model, and calculate the current blood glucose concentration.

[0055] The detailed steps are as follows:

[0056] (1) After the system is powered on, the ADC and DAC are initialized first. After initialization, the FPGA sets the initial output voltage of the DAC to 0V. After the FPGA completes the setting of the DAC, the FPGA sends a signal that the data transmission is complete to the ADC as the start signal of the ADC. The FPGA controls the ADC to read the output signal of the detector and stores the read data in the RAM of the FPGA. The above operation is repeated until the frequency sweep is completed.

[0057] (2) After the frequency sweep is completed, the excitation voltage value can be converted into frequency according to the mathematical model of the detector output voltage amplitude and the sensor output power. The relationship between the VCO tuning voltage and the output frequency is as follows:

[0058]

[0059] Among them, f VCO V represents the output voltage of the VCO, measured in GHz. T The tuning voltage of the VCO is expressed in volts (V).

[0060] (3) Sensor S 21 The parameters are represented as follows:

[0061]

[0062] Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN It is the power of the VCO input sensor, P IN Taking a typical VCO output power value of 11 dBm, when the detector's input power is between 0 and 10 dBm, the relationship between the power of the signal received by the detector and the input voltage of the ADC module is expressed as follows:

[0063] P OUT =28.6V ADC -33.6

[0064] The S of the sensor at the corresponding frequency point can be calculated using the above formula. 21 Parameters; S for traversing all frequency points 21 The amplitude of the parameters is used to find the excitation voltage and resonance depth corresponding to the resonant point. The content displayed on the digital tube is controlled by the key. After the traversal is completed, the DAC re-outputs the initial sweep voltage signal.

[0065] (4) Place the index finger of the person to be tested in the gap of the pearl cotton foam board. Start timing after eating. Record the resonant frequency and resonant depth of the sensor every 5 minutes. Use a blood glucose meter to perform an invasive measurement. The blood glucose concentration of the sensor and the resonant characteristics of the sensor measured by the system within 60 minutes after the volunteer eats.

[0066] (5) The dataset recorded in the experiment was divided into training dataset and test dataset for multiple linear regression analysis. The data sampled at minutes 0, 10, 20, 30, 40, 50, and 60 were used as the training dataset, and the data sampled at minutes 5, 15, 25, 35, 45, and 55 were used as the test dataset.

[0067] (6) Using the resonant frequency and resonant depth of resonant mode 1 and the resonant frequency of resonant mode 2 as features, and blood glucose concentration as the label data, a multiple linear regression equation was fitted. The prediction formula for blood glucose concentration is shown below:

[0068]

[0069] Where y is the blood glucose concentration xs1 x f2 These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. w0 is the constant to be fitted, and w s1 w f2 These are the weights corresponding to each independent variable;

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, specific examples are provided below to illustrate the implementation of this invention. It should be understood that this invention can also be implemented or applied through other different specific embodiments and is not intended to limit the invention. A specific technical solution for a small, non-invasive blood glucose detection system based on frequency shift and amplitude constraints is as follows:

[0071] (1) The structural block diagram of the non-invasive blood glucose detection system based on microwave sensors is as follows: Figure 1 As shown, the system consists of an FPGA, a digital-to-analog converter, a VCO, a dual-frequency CSRR sensor, a detector, an ADC, an LCD, a digital tube, and a power supply module.

[0072] (2) The structure of the dual-frequency CSRR sensor is as follows: Figure 2 As shown in the diagram, gray represents metal, and white represents the etched portion. FR4 was selected as the sensor substrate, which is 40mm long, 20mm wide, and 0.8mm thick. The open / close microstrip line is etched onto a copper layer on top of the 35μm thick substrate, consisting of two W-width lines. C The combiner microstrip line and two W-width... S The circuit consists of microstrip lines for opening circuits. In this example, the width of the microstrip line for the combiner is 1.5 mm, and the width of the microstrip line for the splitter is 0.3 mm.

[0073] (3) Two double-ring CSRR units and two single-ring CSRR units are etched onto a copper layer at the bottom of a 35 μm thick substrate. The horizontal distance between the two circular double-ring CSRR units is b, and the vertical distance between the two circular single-ring CSRR units is c. The circular double-ring CSRR unit consists of two concentric circular grooves with symmetrical openings spaced t apart. The diameter of the outer ring is a1, the width of the opening is g, and the width of the groove between the inner and outer metals is s. The circular single-ring CSRR unit consists of a single circular opening groove. The diameter of the outer ring is a2, the width of the opening is g, and the width of the groove between the inner and outer metals is s.

[0074] (4) Sensor S under no-load condition 21 Parameters such as Figure 3As shown in the figure, the sensor displays two transmission zeros, which is consistent with the analysis results of the sensor's equivalent circuit. These two transmission zeros are named Resonance Mode 1 and Resonance Mode 2. The resonant frequency and resonance depth of Resonance Mode 1 are 4.51 GHz and 34.3 dB, respectively, while the resonant frequency and resonance depth of Resonance Mode 2 are 6.35 GHz and 20.1 dB, respectively. Compared with Resonance Mode 2, Resonance Mode 1 has a larger resonance depth.

[0075] (5) Select a VCO with an input tuning voltage range of 0-10V, an output frequency of 1.73-2.83GHz, and a power of 11dBm; select a DAC module to output a voltage of 0-10V with an output accuracy of 20 bits; the sensor's resonance depth varies from -2.4dB to -1.8dB during operation; select a detector with an input frequency range of DC to 6GHz and a nominal range of -30dBm to +15dBm for the root mean square power measurement function to perform power detection on the transmitted signal; select a 16-bit successive approximation ADC to sample the output signal of the detector;

[0076] (6) The flowchart of the non-invasive blood glucose detection system based on microwave sensors is as follows: Figure 4 As shown, after the system powers on, the ADC and DAC are initialized first. After initialization, the FPGA sets the initial output voltage of the DAC to 0V. Once the FPGA has completed setting the DAC, it sends a data transmission completion signal to the ADC as the ADC's start signal. The FPGA controls the ADC to read the detector's output signal and stores the read data in the FPGA's RAM. This process is repeated until the frequency sweep is complete.

[0077] (7) After the frequency sweep is completed, the excitation voltage value can be converted into frequency according to the mathematical model of the detector output voltage amplitude and the sensor output power. The relationship between the VCO tuning voltage and the output frequency is as follows:

[0078]

[0079] Among them, f VCO V represents the output voltage of the VCO, measured in GHz. T The tuning voltage of the VCO is expressed in volts (V).

[0080] (8) Sensor S 21 The parameter can be expressed as:

[0081]

[0082] Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN It is the power of the VCO input sensor, PIN Taking a typical VCO output power value of 11 dBm, when the detector's input power is between 0 and 10 dBm, the relationship between the power of the signal received by the detector and the input voltage of the ADC module is expressed as follows:

[0083] P OUT =28.6V ADC -33.6

[0084] The S of the sensor at the corresponding frequency point can be calculated using the above formula. 21 Parameters; S for traversing all frequency points 21 The amplitude of the parameters is used to find the excitation voltage and resonance depth corresponding to the resonant point, and the display content on the digital tube is controlled by the buttons. After the traversal is completed, the DAC re-outputs the initial sweep frequency voltage signal.

[0085] (9) A physical diagram of the non-invasive blood glucose detection system based on a microwave sensor is shown below. Figure 5 As shown, the entire system is powered by a lithium battery. The power management module steps down the lithium battery voltage and distributes it to each module. The radio frequency modules are connected using coaxial cables.

[0086] (10) This experiment involved continuous blood glucose prediction on a healthy volunteer. The experiment began after the volunteer had rested in their seat for 15 minutes. After the experiment started, the volunteer placed their index finger in the gap of the pearl cotton foam board. Timing began after the volunteer finished eating, and the resonant frequency and depth of the sensor were recorded every 5 minutes. An invasive measurement was then performed using a blood glucose meter. The sensor's blood glucose concentration and the resonant characteristics of the sensor measured by the system within 60 minutes after the volunteer's meal were recorded as follows: Figure 6 As shown.

[0087] (11) The dataset recorded in the experiment was divided into a training dataset and a test dataset for multiple linear regression analysis. The data sampled at minutes 0, 10, 20, 30, 40, 50, and 60 were used as the training dataset, and the data sampled at minutes 5, 15, 25, 35, 45, and 55 were used as the test dataset.

[0088] (12) The Pearson correlation coefficient is used to measure the degree of correlation between two variables. The formula for calculating the Pearson correlation coefficient is as follows:

[0089]

[0090] Where, x i Let y be the value of variable 1. i For the value of variable 2, Let be the mean of variable 1. The mean of variable 2 is given. The Pearson correlation coefficient between the resonance depth of resonance mode 1 and blood glucose concentration is 0.736, and the Pearson correlation coefficient between the resonance frequency of resonance mode 2 and blood glucose concentration is 0.798. This indicates that the resonance depth of resonance mode 1 and the resonance frequency of resonance mode 2 have a good linear relationship with blood glucose concentration. Using the resonance frequency and resonance depth of resonance mode 1 and the resonance frequency of resonance mode 2 as features, and blood glucose concentration as the label data, a multiple linear regression equation is fitted. The formula for predicting blood glucose concentration is shown below:

[0091]

[0092] Where y represents blood glucose concentration, in mg / dL. and These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. The unit is dB. The unit is GHz.

[0093] In this process, the resonant depth of sensor resonant mode 1 and the resonant frequency of resonant mode 2 are selected as constraints in the computer for data fitting to construct a blood glucose concentration prediction model. The measured resonant frequency and resonant depth are then imported into the blood glucose concentration prediction model to calculate the current blood glucose concentration.

[0094] Comparison between predicted and reference blood glucose concentrations obtained from the regression model. Figure 7 As shown in the figure, the trend of change is highly consistent with the curve of change of the predicted blood glucose concentration and the reference blood glucose concentration. The root mean square error and the mean relative error of the regression model are 2.6 and 2.3%, respectively.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention. Those skilled in the art can easily understand that the above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. The present invention is not limited to the above examples. Modifications or equivalent substitutions made within the principles of the technical solutions of the present invention, as long as they meet the requirements of the method of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A small, non-invasive blood glucose detection system based on frequency shift and amplitude constraints, characterized in that, include: Field-programmable gate array (FPGA), digital-to-analog converter (DAC), voltage-controlled oscillator (VCO), dual-frequency complementary open-circuit resonator (CSRR) sensor, detector, analog-to-digital converter (ADC), liquid crystal display (LCD), digital tube, and power supply module. The FPGA controls the DAC to output a sweep voltage signal, which is used as the tuning voltage of the VCO, thereby enabling the VCO to generate a microwave signal corresponding to the tuning voltage. The microwave signal generated by the VCO is input to the microwave sensor. The detector measures the power of the microwave signal output by the sensor and converts the power value into an analog voltage signal, which is then output to the ADC. The voltage signal is converted into a digital signal by the ADC module, read by the FPGA, and stored in the FPGA's random access memory (RAM). After storage, the FPGA increases the DAC output voltage, and the ADC module reads the new analog voltage signal. This process is repeated until the DAC output voltage reaches the maximum value of the VCO input tuning voltage, at which point the system completes the frequency sweep of the sensor's operating frequency band. The transmission coefficient S of the sensor 21 The parameters are as follows: Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN The power of the VCO input sensor is P, and the power of the signal received by the detector is P. OUT With the input voltage V of the ADC module ADC The relational expression is as follows: P OUT =28.6V ADC -33.6 After the frequency sweep is completed, the detector output voltage data stored in RAM is converted into power values ​​through calculation, based on S. 21 The amplitude of the sensor's transmission coefficient is obtained from the parameter formula, and the FGP controls the LCD display of the sensor S. 21 The amplitude curve of the parameter, S 21 The frequency and amplitude at the parameter resonance peak are the sensor's resonant frequency and resonance depth. The FGPA controls the digital tube to display the sensor's resonant frequency and resonance depth according to the key.

2. The small, non-invasive blood glucose detection system based on frequency shift and amplitude constraints as described in claim 1, characterized in that, The dual-frequency complementary open-circuit resonant ring (CSRR) sensor includes an open / combiner microstrip line, a substrate, and complementary open-circuit resonant ring units. Specifically: the open / combiner microstrip line is etched onto a copper layer on the top of the substrate, and consists of two combiner microstrip lines and two open-circuit microstrip lines; two double-ring circular dual-frequency complementary open-circuit resonant ring (CSRR) units and two single-ring circular dual-frequency complementary open-circuit resonant ring (CSRR) units are etched onto a copper layer on the bottom of the substrate. The double-ring CSRR unit consists of two concentric circular grooves with symmetrical openings, and the single-ring CSRR unit consists of a single circular opening groove. When a microwave signal is applied to the microstrip line, if the symmetry line of the CSRR unit is perpendicular to the axis of the microstrip line, the microstrip line provides electric field excitation to the CSRR unit; if the symmetry line of the CSRR unit is parallel to the axis of the microstrip line, the microstrip line provides both electric and magnetic field excitation to the CSRR unit; the symmetry lines of the four CSRR units are perpendicular to the axis of the open / combiner microstrip line.

3. A small, non-invasive blood glucose detection method based on frequency shift and amplitude constraints, characterized in that, The system described in claim 1 is implemented using the following steps: (1) Place your finger on the dual-frequency CSRR sensor, and the detection system applies a microwave signal to the sensor to measure the sensor's transmission coefficient S. 21 The transmission coefficient S 21 Two resonance peaks are displayed. The minimum points of these two resonance peaks, i.e., the transmission zero points, are named Resonance Mode 1 and Resonance Mode 2. The resonance depth of Resonance Mode 1 and the resonance frequency of Resonance Mode 2 are used as constraints for data fitting to construct a blood glucose concentration prediction model. The expression of the prediction model is as follows: Where y is the blood glucose concentration x s1 x f2 These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. w0 is the constant to be fitted, and w s1 w f2 These are the weights corresponding to each independent variable; (2) After the blood glucose concentration prediction model is constructed, place your finger on the microwave sensor, import the measured resonant frequency and resonant depth into the blood glucose concentration prediction model, and calculate the current blood glucose concentration.

4. The small-scale non-invasive blood glucose detection method based on frequency shift and amplitude constraints as described in claim 3, characterized in that, The detailed steps are as follows: (1) After the system is powered on, the ADC and DAC are initialized first. After initialization, the FPGA sets the initial output voltage of the DAC to 0V. After the FPGA completes the setting of the DAC, the FPGA sends a signal that the data transmission is complete to the ADC as the start signal of the ADC. The FPGA controls the ADC to read the output signal of the detector and stores the read data in the RAM of the FPGA. The above operation after the ADC is started is repeated until the frequency sweep is completed. (2) After the frequency sweep is completed, the excitation voltage value can be converted into frequency according to the mathematical model of the detector output voltage amplitude and the sensor output power. The relationship between the VCO tuning voltage and the output frequency is as follows: Among them, f VCO V represents the output voltage of the VCO, measured in GHz. T The tuning voltage of the VCO is expressed in volts (V). (3) Sensor S 21 The parameters are represented as follows: Among them, S 21 P represents the transmission coefficient of the sensor, measured in dB. IN It is the power of the VCO input sensor, P IN Taking a typical VCO output power value of 11 dBm, when the detector's input power is between 0-10 dBm, the power P of the signal received by the detector is... OUT With the input voltage V of the ADC module ADC The relational expression is as follows: P OUT =28.6V ADC -33.6 The S of the sensor at the corresponding frequency point can be calculated using the above formula. 21 Parameters; S for traversing all frequency points 21 The amplitude of the parameters is used to find the excitation voltage and resonance depth corresponding to the resonant point. The content displayed on the digital tube is controlled by the key. After the traversal is completed, the DAC re-outputs the initial sweep voltage signal. (4) Place the index finger of the person to be tested in the gap of the pearl cotton foam board. Start timing after eating. Record the resonant frequency and resonant depth of the sensor every 5 minutes. Use a blood glucose meter to perform an invasive measurement. The blood glucose concentration of the sensor and the resonant characteristics of the sensor measured by the system within 60 minutes after the volunteer eats. (5) The dataset recorded in the measurement is divided into a training dataset and a test dataset for multiple linear regression analysis. The data sampled at minutes 0, 10, 20, 30, 40, 50, and 60 are used as the training dataset, and the data sampled at minutes 5, 15, 25, 35, 45, and 55 are used as the test dataset. (6) Using the resonant frequency and resonant depth of resonant mode 1 and the resonant frequency of resonant mode 2 as features, and blood glucose concentration as the label data, a multiple linear regression equation was fitted. The prediction formula for blood glucose concentration is shown below: Where y is the blood glucose concentration x s1 x f2 These correspond to the resonance depth of sensor resonance mode 1 and the resonance frequency of resonance mode 2, respectively. w0 is the constant to be fitted, and w s1 w f2 These are the weights corresponding to each independent variable.

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