Implantable gamma-aminobutyric acid detection system and dynamic real-time calibration method

Abstract

The invention relates to an implantable gamma-aminobutyric acid detection system, which belongs to the technical field of neurochemical detection and comprises an implantable microelectrode array device, a signal detection circuit module, a first wireless data transceiving module, a second wireless data transceiving module and a detection terminal. The invention also provides a dynamic real-time calibration method. The method comprises the following steps: implanting the implanted microelectrode array device into an animal body; measuring two paths of electrochemical impedance data between the working electrode and the reference electrode and between the calibration electrode and the reference electrode by using a signal detection circuit module; calculating an estimated value of the concentration of gamma-aminobutyric acid by adopting an improved Randles circuit model; estimating the measurement error of the gamma-aminobutyric acid concentration by using an artificial neural network; and calibrating the measurement error to obtain a gamma-aminobutyric acid concentration detection value.

Description

Technical Field

[0001] The invention belongs to the technical field of neurochemical detection, and relates to an implantable gamma-aminobutyric acid detection system and a dynamic real-time calibration method. Background Art

[0002] γ-Aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system. It has the function of regulating the information transmission process in the cerebral cortex, thereby affecting neural activities such as learning, memory, and attention. In vivo, real-time, and continuous analysis of the neurotransmitter γ-aminobutyric acid in animals may provide important information for neuroscience research related to learning and memory, and can provide new monitoring and control methods for some neurological diseases. In recent years, micromachining technology in the integrated circuit industry has been used to prepare microelectrode arrays for recording neurochemical information. Some microelectrode arrays can use fast voltammetry to detect the concentration of neurotransmitters at multiple sites separated by hundreds of microns in vertical distance. When implantable microelectrode array devices work in vivo for a long time, their sensitivity and other indicators will gradually change due to the coating of proteins, cells, and tissues. If the measured values ​​are not calibrated, they cannot reflect the true situation of the γ-aminobutyric acid concentration in the body. Therefore, there are currently the following problems in the detection of γ-aminobutyric acid:

[0003] Insufficient sensitivity and long-term stability: When implantable microelectrode array devices work in vivo for a long time, their surfaces are easily coated by proteins, cells and tissues, resulting in a significant decrease in the sensitivity of the electrodes and distortion of the measurement signals. This change not only affects the accurate detection of γ-aminobutyric acid concentration, but also limits the long-term stability of the sensor, making it difficult to continuously provide reliable data.

[0004] Environmental sensitivity of recognition elements: Traditional methods rely on bioactive molecules such as antibodies or enzymes as recognition elements to detect GABA. However, these bioactive molecules are extremely sensitive to environmental factors such as temperature and pH, and are easily inactivated in the complex and changing biological environment, thus affecting the performance and reliability of the sensor. In addition, the high cost of preparing and preserving bioactive molecules also limits their widespread application.

[0005] Effective use of electrochemical impedance spectroscopy data: Although electrochemical impedance spectroscopy technology provides a powerful tool for the study of electrode characteristics, in practical applications, how to accurately extract information related to γ-aminobutyric acid concentration from complex electrochemical impedance spectroscopy data and establish a reliable measurement model based on it is still an urgent problem to be solved. Existing methods often have problems such as insufficient accuracy or high computational complexity when processing electrochemical impedance spectroscopy data.

[0006] Lack of real-time dynamic calibration mechanism: During long-term in vivo detection of implantable microelectrode array devices, the measurement performance of the electrodes will change due to changes in the biological environment (such as inflammation, tissue repair, etc.), resulting in increased measurement errors. The existing technology lacks an effective real-time dynamic calibration mechanism to correct these errors in a timely manner, thus affecting the accuracy and reliability of the measurement results. Summary of the invention

[0007] In view of this, the object of the present invention is to provide a measurement system capable of detecting the concentration of γ-aminobutyric acid in an animal in real time in vivo and a method for real-time dynamic calibration of the measured value of the γ-aminobutyric acid concentration.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] In one aspect, the present invention provides an implantable γ-aminobutyric acid detection system, comprising an implantable microelectrode array device, a signal detection circuit module, a first wireless data transceiver module, a second wireless data transceiver module, and a detection terminal;

[0010] The implantable microelectrode array device comprises a working electrode, a calibration electrode and a reference electrode; the working electrode is used to detect the electrochemical impedance parameters before and after the target molecule binds to the electrode; the calibration electrode is used to detect the change of the background electrochemical impedance parameters caused by non-target molecules; the reference electrode provides a reference point when an excitation voltage signal is applied to the working electrode or the calibration electrode;

[0011] The input end of the signal detection circuit module is connected to the working electrode, the calibration electrode and the reference electrode. The signal detection circuit module is used to simultaneously generate two excitation electrical signals, one of which is applied to the working electrode and the reference electrode, and the other is applied to the calibration electrode and the reference electrode. The signal detection circuit module is also used to measure the electrochemical impedance spectrum between the working electrode and the reference electrode, and between the calibration electrode and the reference electrode in real time.

[0012] The output end of the signal detection circuit module is connected to the first wireless data transceiver module; the first wireless data transceiver module is wirelessly connected to the second wireless data transceiver module; the second wireless data transceiver module is connected to the detection terminal;

[0013] The detection terminal is used to: (1) adjust the frequency scanning range and amplitude of the excitation electrical signal of the signal detection circuit module; (2) receive the electrochemical impedance spectrum transmitted by the signal detection circuit module, use the improved Randles circuit model to estimate the γ-aminobutyric acid concentration, then use the artificial neural network model to estimate the measurement error, and finally calibrate the measurement error to obtain the γ-aminobutyric acid concentration detection value; (3) display the γ-aminobutyric acid concentration detection value.

[0014] Furthermore, the implantable microelectrode array device also includes a substrate, a working electrode pin, a calibration electrode pin and a reference electrode pin; the working electrode, the calibration electrode, the reference electrode and the working electrode pin, the calibration electrode pin and the reference electrode pin are all arranged on the substrate; the working electrode is connected to the working electrode pin through a lead, the calibration electrode is connected to the calibration electrode pin through a lead, and the reference electrode is connected to the reference electrode pin through a lead; the working electrode pin, the calibration electrode pin and the reference electrode pin are connected to the input end of the signal detection circuit module through a wire.

[0015] Furthermore, the surface of the working electrode is modified by a molecular imprinting membrane of γ-aminobutyric acid, while the surface of the calibration electrode is not modified by a molecular imprinting membrane of γ-aminobutyric acid.

[0016] Furthermore, the preparation process of the implantable microelectrode array device is as follows:

[0017] A1: First, a layer of photoresist is spin-coated on the surface of the insulator silicon wafer, and the patterns of the working electrode, calibration electrode, reference electrode, lead wire, working electrode pin, calibration electrode pin and reference electrode pin are formed by photolithography;

[0018] A2: Use ultraviolet light exposure to cure the photoresist where there are no electrodes, leads or pins;

[0019] A3: Use a developer to dissolve the uncured photoresist, exposing the location where the conductive metal needs to be deposited;

[0020] A4: Deposit a layer of chromium on the silicon wafer by electron beam evaporation deposition, and deposit a layer of metal platinum on the chromium layer; remove the photoresist on the silicon wafer with acetone;

[0021] A5: After the Pt electrode is made on the silicon substrate, the microelectrode and the pin part are covered with photoresist by secondary photolithography, and a layer of silicon nitride Si3N4 is deposited on the Pt by low-pressure chemical vapor deposition LPCVD as an insulating layer;

[0022] A6: Using the third photolithography development, the microelectrode array substrate shape is formed by deep etching;

[0023] A7: Remove the bottom silicon by wet etching, so that the thin silicon dioxide layer outside the microelectrode array device falls off to form a microelectrode array prototype;

[0024] Form an Ag / AgCl film on the reference electrode surface as follows:

[0025] B1: The implantable microelectrode array device is cleaned with deionized water, then ultrasonically cleaned in a hydrochloric acid solution, then cleaned with deionized water, and finally dried in an oven for later use;

[0026] B2: Prepare the electrolyte solution used in the silver plating process;

[0027] B3: Silver plating of the reference electrode on the implantable microelectrode array device: using a three-electrode system, the chuck of the electrochemical workstation is connected to the reference electrode, platinum wire electrode, and Ag / AgCl electrode on the implantable microelectrode array device respectively; the end of the implantable microelectrode array device with the microelectrode is immersed in the prepared electrolyte solution; the electrodeposition is performed using the chronopotentiometry; after the silver particles are deposited on the surface of the reference electrode, the FeCl3 solution is dripped onto the surface of the reference electrode with a pipette gun to form an Ag / AgCl film on the surface of the reference electrode;

[0028] Gold nanoparticles are formed on the surface of the working electrode and the calibration electrode as follows:

[0029] C1: Place one end of the microelectrode array device with the microelectrode in the HAuCl4 solution;

[0030] C2: Connect one of the chucks of the electrochemical workstation to the working electrode and the calibration electrode at the same time, connect the second chuck to the platinum wire electrode, and connect the third chuck to the Ag / AgCl electrode;

[0031] C3: Set the potential and duration;

[0032] C4: Clean the microelectrode array device with ultrapure water;

[0033] The surface of the working electrode is modified with a molecular imprinting membrane of γ-aminobutyric acid, and the steps are as follows:

[0034] D1: Clean the microelectrode array device with ultrapure water and then ultrasonically clean it in ethanol-water solution;

[0035] D2: Using a three-electrode system, one of the chucks of the electrochemical workstation is connected to the working electrode on the implantable microelectrode array device, the second chuck is connected to the platinum wire electrode, and the third chuck is connected to the Ag / AgCl electrode;

[0036] D3: The molecular imprinting membrane of γ-aminobutyric acid was prepared by electropolymerizing scopoletin monomers; the solution used in the electropolymerization process contained 0.5 mM scopoletin, 0.05 mM thiol-modified γ-aminobutyric acid, and 100 mM NaCl; the end of the microelectrode array device with the microelectrode was immersed in the solution for incubation; then, the electropolymerization was performed using a potential pulse; after the polymerization, the microelectrode array device was washed with ultrapure water to remove the scopoletin monomers and thiol-modified γ-aminobutyric acid that did not participate in the reaction;

[0037] D4: One end of the microelectrode array device with the microelectrode is immersed in a 100 mM PBS solution, and a voltage pulse is applied to the working electrode to oxidize the thiol-modified γ-aminobutyric acid and cause it to fall off the working electrode, thereby generating a hole on the polymer membrane for the γ-aminobutyric acid to specifically bind;

[0038] D5: Finally, clean the microelectrode array device with ultrapure water;

[0039] The calibration electrode is modified in a similar process to the working electrode, except that the solution used for electropolymerization of scopolamine does not contain template molecules, thiol-modified γ-aminobutyric acid, and the polymer film on the surface of the calibration electrode does not contain holes for γ-aminobutyric acid to bind, and does not constitute a molecular imprinting film of γ-aminobutyric acid.

[0040] Furthermore, the sizes of the working electrode, calibration electrode and reference electrode are comparable to those of a single cell; the front end width of the implantable microelectrode array device substrate is 100 μm, the rear end width is 20 mm, and the working electrode pin, calibration electrode pin and reference electrode pin are 3×6 mm in size.

[0041] In another aspect, the present invention provides an implantable γ-aminobutyric acid dynamic real-time calibration method, comprising the following steps:

[0042] S1: implanting the implantable microelectrode array device into an animal body, and connecting the electrode pins of the working electrode, the calibration electrode and the reference electrode to the input end of the signal detection circuit module;

[0043] S2: using the signal detection circuit module to simultaneously generate two excitation electrical signals, and measuring two electrochemical impedance data between the working electrode and the reference electrode, and between the calibration electrode and the reference electrode;

[0044] S3: The improved Randles circuit model is used to fit the electrochemical impedance spectroscopy data to obtain the circuit element parameters, thereby calculating the estimated value of γ-aminobutyric acid concentration;

[0045] S4: using an artificial neural network to process the circuit element parameters obtained by the fitting, and outputting the measurement error of the γ-aminobutyric acid concentration;

[0046] S5: Calibrate the measurement error to obtain the detection value of the γ-aminobutyric acid concentration.

[0047] Further, the improved Randles circuit model includes the resistance R of the electrolyte solution s , the constant phase angle element Z related to the interfacial capacitance between the electrode and the solution CPE , R represents the charge transfer resistance ct and Z, which represents the Warburg diffusion impedance W , R ct With Z W After being connected in series, circuit component 1 is formed. Circuit component 1 and Z CPE The circuit component 2 is connected in parallel, and the circuit component 2 is connected with R s The whole circuit model is constructed in series; Z CPE The frequency variation is represented by the following equation:

[0048]

[0049] Where T and n are constants, ω is the angular frequency, and j represents the imaginary unit;

[0050] Z W The frequency changes are as follows:

[0051]

[0052] According to the AC impedance values ​​measured at a series of different frequencies and the fitting algorithm, R s , R ct ,T,n,Y W parameter.

[0053] Furthermore, the improved Randles circuit model is used to fit the electrochemical impedance data to obtain circuit element parameters, thereby calculating the estimated value of the γ-aminobutyric acid concentration, which specifically includes the following steps:

[0054] preparing artificial cerebrospinal fluid, adding gamma-aminobutyric acid to the artificial cerebrospinal fluid so that the concentration of gamma-aminobutyric acid is C;

[0055] The electrode end of the microelectrode array device is immersed in artificial cerebrospinal fluid, and the electrochemical impedance spectrum between the working electrode and the reference electrode of the microelectrode array device is measured;

[0056] The improved Randles circuit model is used to fit the electrochemical impedance spectroscopy data between the working electrode and the reference electrode to obtain the resistance Rs of the electrolyte solution and the constant phase angle element Z CPE Parameters T and n, charge transfer resistance R ct and Warburg diffusion impedance Z W Parameter Y W;

[0057] Changing the concentration of γ-aminobutyric acid in artificial cerebrospinal fluid, C, the corresponding Rs, T, n, R ct and Y W Also changes, so as to obtain multiple sets of data (1C, 1Rs, 1T, 1n, 1R ct , 1Y W ), (2C, 2Rs, 2T, 2n, 2R ct , 2Y W ), (3C, 3Rs, 3T, 3n, 3R ct , 3Y W ), ..., ( m C. m Rs. m T. m n. m R ct , m Y W );

[0058] θ0, θ1, θ2, θ3, θ4 and θ5 are obtained by the least square method; θ1, θ2, θ3, θ4 and θ5 are obtained by solving the following equations:

[0059]

[0060] in, X1=Rs,X2=T,X3=n,X4=R ct ,X5=Y W ; 3X1 is 3R s ,

[0061] make Then AΘ=B,Θ=A -1 B, A -1 is the inverse matrix of A;

[0062]

[0063] Thus, θ0, θ1, θ2, θ3, θ4 and θ5 are obtained;

[0064] R s , R ct ,T,n,Y W is the independent variable, and the estimated concentration of γ-aminobutyric acid is C e A linear regression model was constructed for the dependent variable, and the estimated value of γ-aminobutyric acid concentration C was calculated. e =θ0+θ1Rs+θ2T+θ3n+θ4R ct +θ5Y W .

[0065] Further, the measurement error estimation steps of γ-aminobutyric acid concentration are as follows:

[0066] The cerebrospinal fluid of experimental rats was extracted, and γ-aminobutyric acid was added to it. The concentration of γ-aminobutyric acid was measured by high performance liquid chromatography. t ;

[0067] In the presence of a specific concentration of C t After adding bovine serum albumin (BSA) and dopamine into the cerebrospinal fluid of rats with γ-aminobutyric acid, the electrode end of the microelectrode array device was immersed in it, and the electrochemical impedance spectrum between the working electrode and the reference electrode of the microelectrode array device was measured. The improved Randles circuit model was used to fit it, and the circuit element parameters Rs1, T1, n1, R ct1 and Y W1 ;

[0068] The electrochemical impedance spectrum between the calibration electrode and the reference electrode of the microelectrode array device was measured and fitted using the improved Randles circuit model to obtain the circuit element parameters Rs2, T2, n2, R ct2 and Y W2 ;

[0069] The obtained parameters θ0, θ1, θ2, θ3, θ4 and θ5 are calculated according to C e =θ0+θ1Rs1+θ2T1+θ3n1+θ4R ct1 +θ5Y W1 The estimated value of γ-aminobutyric acid concentration C was calculated e ;

[0070] According to the formula ΔC=C e -C t Calculate the γ-aminobutyric acid concentration error ΔC;

[0071] GABA was added to the cerebrospinal fluid of rats to change the concentration of GABA therein, and the concentration of GABA was measured by high performance liquid chromatography as a standard value;

[0072] Then add bovine serum albumin BSA and dopamine to the rat's cerebrospinal fluid; use the above steps to obtain a new set of ΔC, Rs2, T2, n2, R ct2 , Y W2 ;

[0073] Repeat the above operation to obtain a series of different r groups of data ( i ΔC, i Rs2, i T2, i n2, i R ct2 ,i Y W2 ), i = 1, 2, ..., r; Rs2, T2, n2, R ct2 and Y W2 As the input data of the neural network, the corresponding ΔC is the output of the neural network;

[0074] use( i ΔC, i Rs2, i T2, i n2, i R ct2 , i Y W2 ), i = 1, 2, ..., r data groups are used to train the artificial neural network model and determine the model parameters.

[0075] Further, the measurement error is calibrated to obtain the γ-aminobutyric acid concentration detection value, which specifically includes the following steps:

[0076] After the microelectrode array device is implanted into the animal body, the electrode pins are connected to the input end of the signal detection circuit module;

[0077] According to the two-way electrochemical impedance data between the working electrode and the reference electrode and between the calibration electrode and the reference electrode measured by the signal detection circuit module, the circuit element parameters Rs3, T3, n3, R ct3 , Y W3 , and Rs4, T4, n4, R ct4 and Y W4 ;

[0078] According to the parameters θ0, θ1, θ2, θ3, θ4 and θ5, according to C e '=θ0+θ1Rs3+θ2T3+θ3n3+θ4R ct3 +θ5Y W3 The estimated value of γ-aminobutyric acid concentration C was calculated e ';

[0079] Using the trained artificial neural network, Rs4, T4, n4, R ct4 and Y W4 As input data, the output value ΔC' is obtained;

[0080] According to formula C c =C e '-ΔC' calculates the calibrated γ-aminobutyric acid concentration C c .

[0081] The beneficial effects of the present invention are:

[0082] The present invention proposes an implantable γ-aminobutyric acid detection system, which uses a molecular imprinting membrane of γ-aminobutyric acid as an element for specific recognition of γ-aminobutyric acid, overcoming the shortcomings of biologically active molecules such as antibodies and enzymes being easily affected by environmental factors such as temperature and pH value, easily inactivated, and having a short service life when used as recognition elements. The measurement system proposed by the present invention measures the concentration of γ-aminobutyric acid in the body through the change in electrochemical impedance parameters caused by the binding of γ-aminobutyric acid to the working electrode surface, solving the problem that γ-aminobutyric acid is not electrically active. The measurement system is real-time, specific, and effective in a long-term in vivo detection state. This solution is suitable for in vivo real-time monitoring of the concentration of all chemical molecules that are not electrically active in the body, and its typical application can be an implantable γ-aminobutyric acid detection system, but is not limited to such applications.

[0083] The present invention establishes a method for real-time dynamic calibration of the measurement value of an implantable γ-aminobutyric acid detection system. By measuring the electrochemical impedance spectrum data of the calibration electrode, fitting the parameters of an improved Randles circuit model, estimating the parameters of a linear regression model, and applying an artificial neural network model, the accuracy of the γ-aminobutyric acid concentration measurement result is improved, and the measurement error caused by the coating of proteins, cells and tissues when the implantable device is in the body for a long time is effectively reduced.

[0084] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0086] Figure 1 This is a schematic diagram of the structure of the implantable γ-aminobutyric acid detection system of the present invention;

[0087] Figure 2 It is a schematic diagram of the structure of the implantable microelectrode array device of the present invention;

[0088] Figure 3 An improved Randles circuit model used in the present invention;

[0089] Figure 4 It is a structural diagram of the artificial neural network used in the present invention;

[0090] Figure 5The present invention is a flow chart of a method for calibrating the concentration of gamma-aminobutyric acid when the detection system of the present invention performs real-time in vivo detection on animals.

[0091] Figure numerals: 1 - reference electrode, 2 - working electrode, 3 - calibration electrode, 4 - lead, 5 - substrate, 6 - calibration electrode pin, 7 - reference electrode pin, 8 - working electrode pin. DETAILED DESCRIPTION

[0092] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0093] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0094] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0095] An implantable γ-aminobutyric acid detection system, such as Figure 1 As shown, it includes an implantable microelectrode array device, a signal detection circuit module, a wireless data transceiver module A, a wireless data transceiver module B, a PC terminal, and data display and analysis software running on the PC terminal.

[0096] The structure of the implantable microelectrode array device is as follows Figure 2As shown. A working electrode 2, a calibration electrode 3 and a reference electrode 1 are installed on the substrate 5 of the implantable microelectrode array device. The working electrode 2 is used to detect the electrochemical impedance parameters before and after the target molecule binds to the electrode. The calibration electrode 3 is used to detect the changes in the background electrochemical impedance parameters caused by non-target molecules. The reference electrode 1 provides a reference point when an excitation voltage signal is applied to the working electrode or the calibration electrode. The working electrode 2, the calibration electrode 3 and the reference electrode 1 are respectively connected to the working electrode pin 8, the calibration electrode pin 6 and the reference electrode pin 7 through three leads 4. The working electrode pin 8, the calibration electrode pin 6 and the reference electrode pin 7 are connected to the input end of the signal detection circuit module through wires, thereby introducing the electrical signal from the implantable microelectrode array device into the signal detection circuit module.

[0097] The implantable microelectrode array device is prepared using micromachining technology in the integrated circuit industry. The processing flow is as follows: The implantable microelectrode array device is processed on a 4-inch insulator silicon wafer. First, a layer of photoresist is spin-coated on the surface of the silicon wafer, and the patterns of the working electrode, calibration electrode and reference electrode, leads, working electrode pins, calibration electrode pins and reference electrode pins are formed by photolithography. After ultraviolet light exposure, the parts of the photoresist without electrodes, leads or pins are cured. Through the treatment of the developer, the uncured photoresist is dissolved, revealing the locations where the conductive metal needs to be deposited (microelectrodes, leads, pins). A 100nm thick chromium is deposited on the silicon wafer by electron beam evaporation deposition, and a layer of metal platinum is deposited on the chromium layer. The photoresist on the silicon wafer is removed with acetone. After the Pt electrode is made on the silicon substrate, the microelectrode and pin parts are covered with photoresist by secondary photolithography, and a layer of silicon nitride (Si3N4) is deposited on the Pt by low pressure chemical vapor deposition (LPCVD) as an insulating layer. The third photolithography development is used to form the microelectrode array substrate shape through deep etching; the bottom silicon is removed by wet etching, so that the thin layer of silicon dioxide outside the microelectrode array device falls off to form a microelectrode array prototype. The size of the electrode on the implantable microelectrode array device is comparable to the size of a single cell (about 10μm in diameter) to ensure a high spatial resolution. The front end of the implantable microelectrode array device is about 100μm wide and the rear end is about 20mm wide. The working electrode pin, calibration electrode pin and reference electrode pin are about 3×6mm in size.

[0098] An Ag / AgCl film is formed on the surface of the reference electrode as follows: the implantable microelectrode array device is cleaned with deionized water, then ultrasonically cleaned in a hydrochloric acid solution, then cleaned with deionized water, and finally dried in an oven for use. The electrolyte solution used in the silver plating process is prepared, and the solution contains 40g / L AgNO3, 77g / L ammonium acetate, 32mL / L ammonia, 50g / L KOH, 90g / L nicotinic acid, and 75g / L K2CO3. The reference electrode on the implantable microelectrode array device is silver plated using a CHI660E electrochemical workstation (Shanghai Chenhua Instrument Co., Ltd.). A three-electrode system is used, the green chuck of the electrochemical workstation is connected to the reference electrode on the implantable microelectrode array device, the red chuck of the electrochemical workstation is connected to the platinum wire electrode, and the white chuck of the electrochemical workstation is connected to the Ag / AgCl electrode. The end of the implantable microelectrode array device with the microelectrode is immersed in the above-prepared electrolyte solution. Electrodeposition was performed using chronopotentiometry with a current of 2 × 10 -6 A, time 30 minutes. After the silver particles are deposited on the reference electrode surface, FeCl3 solution is added to the reference electrode surface with a pipette to form an Ag / AgCl film on the reference electrode surface. In order to improve the detection sensitivity, a layer of platinum black is deposited on the working electrode and the calibration electrode surface by electrochemical method. The electrolyte solution used in the electrodeposition process is prepared by proportion of lead acetate and chloroplatinic acid (H2PtCl4).

[0099] Further, gold nanoparticles are formed on the surfaces of the working electrode and the calibration electrode. The method is as follows: place one end of the microelectrode array device with the microelectrode in a 0.05 mg / mL HAuCl4 solution, connect the green chuck of the electrochemical workstation to the working electrode and the calibration electrode on the implantable microelectrode array device at the same time, connect the red chuck of the electrochemical workstation to the platinum wire electrode, and connect the white chuck of the electrochemical workstation to the Ag / AgCl electrode. Set the potential to -200 mV and the duration to 2 minutes. Wash the microelectrode array device with ultrapure water.

[0100] The further modification process for the working electrode is as follows:

[0101] The surface of the working electrode on the microelectrode array was modified with a molecular imprinting membrane of γ-aminobutyric acid, while the calibration electrode on the microelectrode array was not modified with a molecular imprinting membrane of γ-aminobutyric acid. The molecular imprinting membrane modification process of the working electrode was as follows: First, the microelectrode array device was cleaned with ultrapure water, and then ultrasonically cleaned in a 1:1 ethanol aqueous solution for 5 minutes. Using a three-electrode system, the green chuck of the CHI660E electrochemical workstation (Shanghai Chenhua Instrument Co., Ltd.) was connected to the working electrode on the implanted microelectrode array device, the red chuck of the electrochemical workstation was connected to the platinum wire electrode, and the white chuck of the electrochemical workstation was connected to the Ag / AgCl electrode. The molecular imprinting membrane of γ-aminobutyric acid was prepared by electropolymerization of scopoletin monomer. The solution used in the electropolymerization process contained 0.5mM scopoletin, 0.05mM thiol-modified γ-aminobutyric acid (a derivative by introducing thiol groups on GABA molecules), and 100mM NaCl. The end of the microelectrode array device with the microelectrode was immersed in the above solution and incubated at 25°C for 5 minutes. Then, the electropolymerization was carried out using a potential pulse, in which the 0V potential lasted for 15 seconds, the 0.7V potential lasted for 5 seconds, and the number of pulses was about 20. After polymerization, the microelectrode array device was washed with ultrapure water to remove the scopoletin monomer and thiol-modified γ-aminobutyric acid that did not participate in the reaction. In order to generate holes on the polymer film for γ-aminobutyric acid to specifically bind, the end of the microelectrode array device with the microelectrode was immersed in a 100mM PBS (pH7.4) solution, and a voltage pulse of 1.4V was applied to the working electrode for 30 seconds, so that the thiol-modified γ-aminobutyric acid was oxidized and fell off the working electrode. Finally, the microelectrode array device was washed with ultrapure water. The modification process of the working electrode on the microelectrode array device is completed.

[0102] The further modification process of the calibration electrode is similar to that of the working electrode, except that the solution used for electropolymerization of scopoletin does not contain the template molecule, thiol-modified γ-aminobutyric acid. Therefore, the polymer film on the surface of the calibration electrode does not contain holes for γ-aminobutyric acid to bind, and does not constitute a molecular imprinting film of γ-aminobutyric acid.

[0103] The input end of the signal detection circuit module is connected to the implantable microelectrode array device. The signal detection circuit module can generate two excitation electrical signals at the same time, which are applied to two pairs of electrodes: the working electrode and the reference electrode, and the calibration electrode and the reference electrode. The excitation electrical signal is an AC voltage signal, and its frequency scanning range and amplitude can be adjusted by the data display and analysis software running on the PC terminal. The maximum frequency scanning range is 1Hz-100kHz. The signal detection circuit module can measure the electrochemical impedance spectrum between the working electrode and the reference electrode, and between the calibration electrode and the reference electrode in real time. 15 impedance data points are sampled every 10 times the frequency, and each data point contains the real part and the imaginary part of the impedance data. The signal detection circuit module can be designed based on the AD5933 impedance measurement chip, and the measurement process and data transmission process are controlled by the STM32 microcontroller.

[0104] The output end of the signal detection circuit module is connected to the wireless data transceiver module A, and the wireless data transceiver module B is connected to the PC terminal. The wireless data transceiver module A and the wireless data transceiver module B can use different communication methods, such as Blutooth, Zig-Bee or Wi-Fi, and the Blutooth communication module, Zig-Bee communication module or Wi-Fi communication module is selected to form the data transceiver module A and the wireless data transceiver module B accordingly.

[0105] The software running on the PC terminal can calculate, display and analyze the measured data and display the state parameters of the current measurement system. The PC terminal software integrates an algorithm for fitting the parameters of the improved Randles circuit model for electrochemical impedance spectroscopy. The improved Randles circuit model used in the present invention is as follows: Figure 3 As shown in the figure, R s represents the resistance of the electrolyte solution, Z CPE represents the constant phase angle element, which is related to the interfacial capacitance between the electrode and the solution, R ct represents the charge transfer resistance, Z W Represents the Warburg diffusion resistance. R ct With Z W After being connected in series, circuit component 1 is formed. Circuit component 1 and Z CPE The circuit component 2 is connected in parallel, and the circuit component 2 is connected with R s The whole circuit model is constructed in series.

[0106] Z CPE The frequency variation can be represented by the following equation:

[0107]

[0108] T and n are constants, ω is the angular frequency, and j represents the imaginary unit. WThe frequency changes are as follows:

[0109]

[0110] Based on the AC impedance values ​​measured at a series of different frequencies and the fitting algorithm, the software on the PC terminal can obtain R s , R ct ,T,n,Y W parameter.

[0111] The parameter fitting process of the improved Randles circuit model for electrochemical impedance spectroscopy can also be achieved through mature professional software such as Zsimdemo software.

[0112] The estimated concentration of γ-aminobutyric acid was obtained by the following method: s , R ct ,T,n,Y W is the independent variable, and the estimated concentration of γ-aminobutyric acid is C e Construct a linear regression model for the dependent variable C = θ0 + θ1Rs + θ2T + θ3n + θ4R ct +θ5Y W , determine the model parameters. Prepare artificial cerebrospinal fluid, which contains 124mM NaCl, 3mM KCl, 26mM NaHCO3, 1.2mM NaH2PO4·2H2O, 2mM MgSO4·7H2O, 2mM CaCl2, and 10mM glucose monohydrate. Add γ-aminobutyric acid to the artificial cerebrospinal fluid to make the concentration of γ-aminobutyric acid C, immerse the electrode end of the microelectrode array device in it, and measure the electrochemical impedance spectrum between the working electrode and the reference electrode of the microelectrode array device. Use the improved Randles circuit model to fit the electrochemical impedance spectrum data between the working electrode and the reference electrode to obtain the resistance Rs of the electrolyte solution and the constant phase angle element Z CPE Parameters T and n, charge transfer resistance R ct and Warburg diffusion impedance Z W Parameter Y W Changing the concentration of γ-aminobutyric acid in artificial cerebrospinal fluid, C, the corresponding Rs, T, n, R ct and Y W Also changes, so as to obtain multiple sets of data (1C, 1Rs, 1T, 1n, 1R ct , 1Y W ), (2C, 2Rs, 2T, 2n, 2R ct , 2Y W ), (3C, 3Rs, 3T, 3n, 3R ct , 3Y W ), ..., ( m C. mRs. m T. m n. m R ct , m Y W ). θ0, θ1, θ2, θ3, θ4 and θ5 are obtained by the least square method. θ1, θ2, θ3, θ4 and θ5 can be obtained by solving the following system of equations:

[0113]

[0114] in, X1=Rs,X2=T,X3=n,X4=R ct ,X5=Y W , for example, 3X1 is 3R s ,

[0115] Then AΘ=B,Θ=A -1 B, A -1 is the inverse matrix of A.

[0116]

[0117] In this way, θ0, θ1, θ2, θ3, θ4 and θ5 can be obtained.

[0118] The method for estimating the measurement error of γ-aminobutyric acid concentration is as follows: extract the cerebrospinal fluid of experimental rats, add γ-aminobutyric acid to it, and measure the concentration of γ-aminobutyric acid by high performance liquid chromatography as C t In the presence of a specific concentration of C t After adding bovine serum albumin (BSA), dopamine and other interfering substances into the cerebrospinal fluid of rats with γ-aminobutyric acid, the electrode end of the microelectrode array device was immersed in it, and the electrochemical impedance spectrum between the working electrode and the reference electrode of the microelectrode array device was measured. The improved Randles circuit model was used to fit it, and the circuit element parameters Rs1, T1, n1, R ct1 and Y W1 ; The electrochemical impedance spectrum between the calibration electrode and the reference electrode of the microelectrode array device was measured, and the improved Randles circuit model was used to fit it to obtain the circuit element parameters Rs2, T2, n2, R ct2 and Y W2 According to the parameters θ0, θ1, θ2, θ3, θ4 and θ5 obtained by the above method, according to C e =θ0+θ1Rs1+θ2T1+θ3n1+θ4R ct1 +θ5Y W1 The estimated value of γ-aminobutyric acid concentration C was calculated eAccording to the formula ΔC=C e -C t Calculate the γ-aminobutyric acid concentration error ΔC. Add γ-aminobutyric acid to the rat's cerebrospinal fluid to change the concentration of γ-aminobutyric acid, and use high performance liquid chromatography to measure the concentration of γ-aminobutyric acid as the standard value. Add bovine serum albumin (BSA), dopamine and other interfering substances to the rat's cerebrospinal fluid. According to the above method, a new set of ΔC, Rs2, T2, n2, R ct2 , Y W2 Repeat the above operation to obtain a series of r different groups of data ( i ΔC, i Rs2, i T2, i n2, i R ct2 , i Y W2 ), i = 1, 2, ..., r. With Rs2, T2, n2, R ct2 and Y W2 As the input data of the neural network, the corresponding ΔC is used as the output of the neural network. i ΔC, i Rs2, i T2, i n2, i R ct2 , i Y W2 ), i = 1, 2, ..., r data groups are used to train the artificial neural network model and determine the model parameters.

[0119] For example, a multi-layer perceptron neural network model is established, whose structure is as follows Figure 4 shown. Figure 4 The neural network model in the example contains an input layer, two hidden layers, and an output layer. The number of neurons in each layer is 5, 15, 15, and 1, respectively. The number of neurons in the hidden layer can be adjusted appropriately based on empirical values. The activation function of the neurons in the first hidden layer is the log-sigmoid function, the activation function of the neurons in the second hidden layer is the hyperbolic tangent function, and the activation function of the neurons in the output layer is the linear function. The mean square error is selected as the loss function. The input vector contains 5 elements, namely Rs2, T2, n2, R ct2 and Y W2 , the output is ΔC. In order to facilitate the training of the neural network, the data set ( i ΔC, i Rs2, i T2, i n2, i R ct2 ,i Y W2 ) i Rs2, i T2, i n2, i R ct2 , i Y W2 All are normalized to the interval [-1,1]. The Levenberg–Marquardt algorithm is used to train the above neural network and determine the network parameters, namely the connection weights between neurons and the bias of each neuron.

[0120] After the microelectrode array device is implanted in the animal body, the electrode pin is connected to the input end of the signal detection circuit module. According to the two-way electrochemical impedance data between the working electrode and the reference electrode and between the calibration electrode and the reference electrode measured by the signal detection circuit module, the circuit element parameters Rs3, T3, n3, R ct3 , Y W3 , and Rs4, T4, n4, R ct4 and Y W4 According to the parameters θ0, θ1, θ2, θ3, θ4 and θ5 obtained by the above method, according to C e ’ =θ0+θ1Rs3+θ2T3+θ3n3+θ4R ct3 +θ5Y W3 The estimated value of γ-aminobutyric acid concentration C was calculated e ’ The trained artificial neural network obtained by the above method is based on Rs4, T4, n4, R ct4 and Y W4 As the input data of the neural network, the output value of the neural network ΔC is obtained ’ According to formula C c =C e ’ -ΔC ’ Calculate the calibrated γ-aminobutyric acid concentration C c . Figure 5 The figure shows a flow chart of the calibration method of the γ-aminobutyric acid concentration when the detection system of the present invention performs real-time in vivo detection on animals.

[0121] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0122] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0123] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0124] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0125] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0126] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0127] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0128] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0129] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0130] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0131] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. An implantable γ-aminobutyric acid detection system, characterized in that: It includes an implantable microelectrode array device, a signal detection circuit module, a first wireless data transceiver module, a second wireless data transceiver module, and a detection terminal; The implantable microelectrode array device includes a working electrode, a calibration electrode and a reference electrode; The working electrode is used to detect the electrochemical impedance parameters before and after the target molecule binds to the electrode; the calibration electrode is used to detect the changes in the background electrochemical impedance parameters caused by non-target molecules; the reference electrode provides a reference point when an excitation voltage signal is applied to the working electrode or the calibration electrode; The input end of the signal detection circuit module is connected to the working electrode, the calibration electrode and the reference electrode. The signal detection circuit module is used to simultaneously generate two excitation electrical signals, one of which is applied to the working electrode and the reference electrode, and the other is applied to the calibration electrode and the reference electrode. The signal detection circuit module is also used to measure the electrochemical impedance spectrum between the working electrode and the reference electrode, and between the calibration electrode and the reference electrode in real time. The output end of the signal detection circuit module is connected to the first wireless data transceiver module; the first wireless data transceiver module is wirelessly connected to the second wireless data transceiver module; the second wireless data transceiver module is connected to the detection terminal; The detection terminal is used to: (1) adjust the frequency scanning range and amplitude of the excitation electrical signal of the signal detection circuit module; (2) receive the electrochemical impedance spectrum transmitted by the signal detection circuit module, use the improved Randles circuit model to estimate the γ-aminobutyric acid concentration, then use the artificial neural network model to estimate the measurement error, and finally calibrate the measurement error to obtain the γ-aminobutyric acid concentration detection value; (3) display the γ-aminobutyric acid concentration detection value.

2. The implantable γ-aminobutyric acid detection system according to claim 1, characterized in that: The implantable microelectrode array device also includes a substrate, a working electrode pin, a calibration electrode pin and a reference electrode pin; the working electrode, the calibration electrode, the reference electrode and the working electrode pin, the calibration electrode pin and the reference electrode pin are all arranged on the substrate; the working electrode is connected to the working electrode pin via a lead, the calibration electrode is connected to the calibration electrode pin via a lead, and the reference electrode is connected to the reference electrode pin via a lead; the working electrode pin, the calibration electrode pin and the reference electrode pin are connected to the input end of the signal detection circuit module via a wire.

3. The implantable γ-aminobutyric acid detection system according to claim 1, characterized in that: The surface of the working electrode is modified by a molecular imprinting membrane of γ-aminobutyric acid, while the surface of the calibration electrode is not modified by a molecular imprinting membrane of γ-aminobutyric acid.

4. The implantable γ-aminobutyric acid detection system according to claim 1, characterized in that: The preparation process of the implantable microelectrode array device is as follows: A1: First, a layer of photoresist is spin-coated on the surface of the insulator silicon wafer, and the patterns of the working electrode, calibration electrode, reference electrode, lead wire, working electrode pin, calibration electrode pin and reference electrode pin are formed by photolithography; A2: Use ultraviolet light exposure to cure the photoresist where there are no electrodes, leads or pins; A3: Use a developer to dissolve the uncured photoresist, exposing the location where the conductive metal needs to be deposited; A4: Deposit a layer of chromium on the silicon wafer by electron beam evaporation deposition, and deposit a layer of metal platinum on the chromium layer; remove the photoresist on the silicon wafer with acetone; A5: After the Pt electrode is made on the silicon substrate, the microelectrode and the pin part are covered with photoresist by secondary photolithography, and a layer of silicon nitride Si3N4 is deposited on the Pt by low-pressure chemical vapor deposition LPCVD as an insulating layer; A6: Using the third photolithography development, the microelectrode array substrate is formed by deep etching; A7: Remove the bottom silicon by wet etching, so that the thin silicon dioxide layer outside the microelectrode array device falls off to form a microelectrode array prototype; Form an Ag / AgCl film on the reference electrode surface as follows: B1: The implantable microelectrode array device is cleaned with deionized water, then ultrasonically cleaned in a hydrochloric acid solution, then cleaned with deionized water, and finally dried in an oven for later use; B2: Prepare the electrolyte solution used in the silver plating process; B3: Silver plating of the reference electrode on the implantable microelectrode array device: using a three-electrode system, the chuck of the electrochemical workstation is connected to the reference electrode, the platinum wire electrode, and the Ag / AgCl electrode on the implantable microelectrode array device respectively; the end of the implantable microelectrode array device with the microelectrode is immersed in the prepared electrolyte solution; Electrodeposition was performed using a chronopotentiometry method; after silver particles were deposited on the reference electrode surface, a FeCl3 solution was dripped onto the reference electrode surface using a pipette gun to form an Ag / AgCl film on the reference electrode surface; Gold nanoparticles are formed on the surface of the working electrode and the calibration electrode as follows: C1: Place one end of the microelectrode array device with the microelectrode in the HAuCl4 solution; C2: Connect one of the chucks of the electrochemical workstation to the working electrode and the calibration electrode at the same time, connect the second chuck to the platinum wire electrode, and connect the third chuck to the Ag / AgCl electrode; C3: Set the potential and duration; C4: Clean the microelectrode array device with ultrapure water; The surface of the working electrode is modified with a molecular imprinting membrane of γ-aminobutyric acid, and the steps are as follows: D1: Clean the microelectrode array device with ultrapure water and then ultrasonically clean it in ethanol-water solution; D2: Using a three-electrode system, one of the chucks of the electrochemical workstation is connected to the working electrode on the implantable microelectrode array device, the second chuck is connected to the platinum wire electrode, and the third chuck is connected to the Ag / AgCl electrode; D3: The molecular imprinting membrane of γ-aminobutyric acid was prepared by electropolymerizing scopoletin monomers; the solution used in the electropolymerization process contained 0.5 mM scopoletin, 0.05 mM thiol-modified γ-aminobutyric acid, and 100 mM NaCl; the end of the microelectrode array device with the microelectrode was immersed in the solution for incubation; then, the electropolymerization was performed using a potential pulse; after the polymerization, the microelectrode array device was washed with ultrapure water to remove the scopoletin monomers and thiol-modified γ-aminobutyric acid that did not participate in the reaction; D4: One end of the microelectrode array device with the microelectrode is immersed in a 100 mM PBS solution, and a voltage pulse is applied to the working electrode to oxidize the thiol-modified γ-aminobutyric acid and cause it to fall off the working electrode, thereby generating a hole on the polymer membrane for the γ-aminobutyric acid to specifically bind; D5: Finally, clean the microelectrode array device with ultrapure water; The calibration electrode is modified in a similar process to the working electrode, except that the solution used for electropolymerization of scopolamine does not contain template molecules, thiol-modified γ-aminobutyric acid, and the polymer film on the surface of the calibration electrode does not contain holes for γ-aminobutyric acid to bind, and does not constitute a molecular imprinting film of γ-aminobutyric acid.

5. The implantable γ-aminobutyric acid detection system according to claim 1, characterized in that: The sizes of the working electrode, calibration electrode and reference electrode are comparable to those of a single cell; the front end width of the implantable microelectrode array device substrate is 100 μm, the rear end width is 20 mm, and the working electrode pin, calibration electrode pin and reference electrode pin have a size of 3×6 mm.

6. A dynamic real-time calibration method for implantable γ-aminobutyric acid, characterized in that: The following steps are involved: S1: implanting the implantable microelectrode array device into an animal body, and connecting the electrode pins of the working electrode, the calibration electrode and the reference electrode to the input end of the signal detection circuit module; S2: using the signal detection circuit module to simultaneously generate two excitation electrical signals, and measuring two electrochemical impedance data between the working electrode and the reference electrode, and between the calibration electrode and the reference electrode; S3: The improved Randles circuit model is used to fit the electrochemical impedance spectroscopy data to obtain the circuit element parameters, thereby calculating the estimated value of γ-aminobutyric acid concentration; S4: using an artificial neural network to process the circuit element parameters obtained by the fitting, and outputting the measurement error of the γ-aminobutyric acid concentration; S5: Calibrate the measurement error to obtain the detection value of the γ-aminobutyric acid concentration.

7. The implantable γ-aminobutyric acid dynamic real-time calibration method according to claim 6, characterized in that: The modified Randles circuit model includes the resistance R of the electrolyte solution s , the constant phase angle element Z related to the interfacial capacitance between the electrode and the solution CPE , R represents the charge transfer resistance ct and Z, which represents the Warburg diffusion impedance W , R ct With Z W After being connected in series, circuit component 1 is formed. Circuit component 1 and Z CPE The circuit component 2 is connected in parallel, and the circuit component 2 is connected with R s The whole circuit model is constructed in series; Z CPE The frequency variation is represented by the following equation: Where T and n are constants, ω is the angular frequency, and j represents the imaginary unit; Z W The frequency changes are as follows: According to the AC impedance values ​​measured at a series of different frequencies and the fitting algorithm, R s , R ct ,T,n,Y W parameter.

8. The implantable γ-aminobutyric acid dynamic real-time calibration method according to claim 7, characterized in that: The improved Randles circuit model is used to fit the electrochemical impedance data to obtain circuit element parameters, thereby calculating the estimated value of the γ-aminobutyric acid concentration, which specifically includes the following steps: preparing artificial cerebrospinal fluid, adding γ-aminobutyric acid to the artificial cerebrospinal fluid so that the concentration of γ-aminobutyric acid is C; The electrode end of the microelectrode array device is immersed in artificial cerebrospinal fluid, and the electrochemical impedance spectrum between the working electrode and the reference electrode of the microelectrode array device is measured; The improved Randles circuit model is used to fit the electrochemical impedance spectroscopy data between the working electrode and the reference electrode to obtain the resistance Rs of the electrolyte solution and the constant phase angle element Z CPE Parameters T and n, charge transfer resistance R ct and Warburg diffusion impedance Z W Parameter Y W ; Changing the concentration of γ-aminobutyric acid in artificial cerebrospinal fluid, C, the corresponding Rs, T, n, R ct and Y W Also changes, so as to obtain multiple sets of data (1C, 1Rs, 1T, 1n, 1R ct , 1Y W ), (2C, 2Rs, 2T, 2n, 2R ct , 2Y W ), (3C, 3Rs, 3T, 3n, 3R ct , 3Y W ), ..., ( m C. m Rs. m T. m n. m R ct , m Y W ); θ0, θ1, θ2, θ3, θ4 and θ5 are obtained by the least square method; θ1, θ2, θ3, θ4 and θ5 are obtained by solving the following equations: in, X1=Rs,X2=T,X3=n,X4=R ct ,X5=Y W ; 3X1 is 3R s , make Then AΘ=B,Θ=A -1 B, A -1 is the inverse matrix of A; Thus, θ0, θ1, θ2, θ3, θ4 and θ5 are obtained; R s , R ct ,T,n,Y W is the independent variable, and the estimated concentration of γ-aminobutyric acid is C e A linear regression model was constructed for the dependent variable, and the estimated value of γ-aminobutyric acid concentration C was calculated. e =θ0+θ1Rs+θ2T+θ3n+θ4R ct +θ5Y W .

9. The implantable γ-aminobutyric acid dynamic real-time calibration method according to claim 8, characterized in that: The measurement error estimation steps of γ-aminobutyric acid concentration are as follows: The cerebrospinal fluid of experimental rats was extracted, and γ-aminobutyric acid was added to it. The concentration of γ-aminobutyric acid was measured by high performance liquid chromatography. t ; In the presence of a specific concentration of C t After adding bovine serum albumin (BSA) and dopamine into the cerebrospinal fluid of rats with γ-aminobutyric acid, the electrode end of the microelectrode array device was immersed in it, and the electrochemical impedance spectrum between the working electrode and the reference electrode of the microelectrode array device was measured. The improved Randles circuit model was used to fit it, and the circuit element parameters Rs1, T1, n1, R ct1 and Y W1 ; The electrochemical impedance spectrum between the calibration electrode and the reference electrode of the microelectrode array device was measured and fitted using the improved Randles circuit model to obtain the circuit element parameters Rs2, T2, n2, R ct2 and Y W2 ; The obtained parameters θ0, θ1, θ2, θ3, θ4 and θ5 are calculated according to C e =θ0+θ1Rs1+θ2T1+θ3n1+θ4R ct1 +θ5Y W1 The estimated value of γ-aminobutyric acid concentration C was calculated e ; According to the formula ΔC=C e -C t Calculate the γ-aminobutyric acid concentration error ΔC; GABA was added to the cerebrospinal fluid of rats to change the concentration of GABA therein, and the concentration of GABA was measured by high performance liquid chromatography as a standard value; Then add bovine serum albumin BSA and dopamine to the rat's cerebrospinal fluid; use the above steps to obtain a new set of ΔC, Rs2, T2, n2, R ct2 , Y W2 ; Repeat the above operation to obtain a series of different r groups of data ( i ΔC, i Rs2, i T2, i n2, i R ct2 , i Y W2 ), i = 1, 2, ..., r; Rs2, T2, n2, R ct2 and Y W2 As the input data of the neural network, the corresponding ΔC is the output of the neural network; use( i ΔC, i Rs2, i T2, i n2, i R ct2 , i Y W2 ), i = 1, 2, ..., r data groups are used to train the artificial neural network model and determine the model parameters.

10. The implantable γ-aminobutyric acid dynamic real-time calibration method according to claim 9, characterized in that: The calibration of the measurement error to obtain the γ-aminobutyric acid concentration detection value specifically includes the following steps: After the microelectrode array device is implanted into the animal body, the electrode pins are connected to the input end of the signal detection circuit module; According to the two-way electrochemical impedance data between the working electrode and the reference electrode and between the calibration electrode and the reference electrode measured by the signal detection circuit module, the circuit element parameters Rs3, T3, n3, R ct3 , Y W3 , and Rs4, T4, n4, R ct4 and Y W4 ; According to the parameters θ0, θ1, θ2, θ3, θ4 and θ5, according to C e '=θ0+θ1Rs3+θ2T3+θ3n3+θ4R ct3 +θ5Y W3 The estimated value of γ-aminobutyric acid concentration C was calculated e '; Using the trained artificial neural network, Rs4, T4, n4, R ct4 and Y W4 As input data, the output value ΔC' is obtained; According to formula C c =C e '-ΔC' calculates the calibrated γ-aminobutyric acid concentration C c .

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