Continuous intraocular pressure monitoring method and device based on glaucoma valve

Abstract

The invention relates to the technical field of intraocular pressure monitoring and analysis, in particular to a continuous intraocular pressure monitoring method and device based on a glaucoma valve. The glaucoma valve comprises a glaucoma valve body and further comprises a pressure sensor installed inside or outside the glaucoma valve body and a transmitting coil arranged outside the glaucoma valve body, and the pressure sensor is electrically connected with the transmitting coil through a signal wire; a pressure electric signal, sensed by the pressure sensor, in the glaucoma valve body is transmitted to the transmitting coil through the signal wire, and the transmitting coil converts the pressure electric signal into a pressure electromagnetic wave signal and sends the pressure electromagnetic wave signal outwards. The continuous intraocular pressure monitoring device receives a pressure electromagnetic wave signal through a receiving coil and acquires an intraocular pressure signal. The coil and the glaucoma valve are combined, intraocular pressure data can be continuously obtained in real time in a wireless mode, and intraocular pressure changes can be monitored in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of intraocular pressure monitoring, and in particular, to a continuous intraocular pressure monitoring method and device based on a glaucoma valve. Background Art

[0002] Glaucoma is a common ophthalmic disease, mainly manifested by increased intraocular pressure, resulting in optic nerve damage, and can cause blindness in severe cases. At present, the treatment methods for glaucoma include drug treatment, surgical treatment, and implantation of glaucoma valves, etc. The goal of these treatment means is to reduce intraocular pressure, relieve glaucoma symptoms, and protect the optic nerve from further damage. The glaucoma valve regulates intraocular pressure by controlling the drainage of aqueous humor, thereby slowing down the progression of the disease.

[0003] However, the existing glaucoma valve devices still face some technical problems during use, especially in real-time monitoring of intraocular pressure, regulating aqueous humor drainage, and evaluating the health status of the device. They cannot provide real-time data feedback and dynamic adjustment functions, and doctors cannot obtain the operating status of the device through non-invasive means, which makes it difficult to detect changes in intraocular pressure, device failures, or the need for drug adjustment in a timely manner, thus increasing the uncertainty during the treatment process.

[0004] With the rapid development of wireless communication technology and medical sensors, more and more intelligent medical devices are being applied to disease management, especially in the field of ophthalmology. Wireless communication technology makes it possible to remotely monitor and transmit real-time data of implanted devices, helping doctors to understand the treatment effect of patients at any time and intervene in a timely manner. However, the existing glaucoma valve devices usually lack the function of real-time intraocular pressure monitoring. On the one hand, they cannot obtain intraocular pressure data in a timely manner through wireless means, resulting in a lack of timely feedback during the treatment process; on the other hand, for glaucoma valves integrated with intraocular pressure sensors, most still have not solved the problems of wireless data transmission stability and power consumption, and it is difficult to provide long-term reliable monitoring in the intraocular environment. Summary of the Invention

[0005] The purpose of the present invention is to provide a continuous intraocular pressure monitoring method and device based on a glaucoma valve, which can accurately collect intraocular pressure data in real time through the mutual inductance of the transceiver coils between the glaucoma valve and the continuous intraocular pressure monitoring device, and perform real-time analysis and processing on the intraocular pressure data based on a pre-trained KAN network model.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: According to one aspect of the present invention, a continuous intraocular pressure monitoring method based on a glaucoma valve is provided, including the following steps: A receiving coil receives the pressure electromagnetic wave signal emitted by the transmitting coil of the glaucoma valve and converts it into a pressure electrical signal; The control unit sends a control signal to the variable-frequency signal generator, which generates a variable-frequency signal according to the control signal. The variable-frequency signal is filtered by a variable-frequency signal filter and amplified by a variable-frequency signal amplifier, and then transmitted to the receiving coil. The control unit adjusts the frequency and phase of the variable-frequency signal through the control signal to achieve the acquisition of the piezoelectric signal data of the receiving coil, and performs filtering and noise reduction processing on the piezoelectric signal by using a signal filtering and noise reduction unit; the filtered and noise-reduced piezoelectric signal is subjected to analog-to-digital conversion by an analog-to-digital conversion unit to obtain a pressure digital signal. The control unit obtains intraocular pressure data based on the pressure digital signal, and analyzes the intraocular pressure data by using a pre-trained KAN network model based on the intraocular pressure data, and gives an analysis result.

[0007] According to an embodiment of the present invention, the filtering and noise reduction processing of the piezoelectric signal by using a signal filtering and noise reduction unit specifically includes: Performing denoising processing on the piezoelectric signal by using wavelet transform to effectively separate the low-frequency part carrying intraocular pressure information from the high-frequency noise part; Performing real-time filtering in a dynamic environment by using Kalman filtering to filter out high-frequency noise and low-frequency interference; Performing smoothing processing on the intraocular pressure signal after Kalman filtering by using mean filtering and weighted moving average methods.

[0008] According to an embodiment of the present invention, the method further includes: collecting optical coherence tomography images, fundus color photos, and fundus vein images, extracting image features by using a convolutional neural network in deep learning, extracting multi-modal features based on intraocular pressure data and image data, and analyzing the multi-modal features by using a pre-trained KAN network model to give an analysis result.

[0009] According to an embodiment of the present invention, the training method of the pre-trained KAN network model includes the following steps: Using a continuous intraocular pressure monitoring device based on a glaucoma valve to continuously collect intraocular pressure data and / or ocular image data of patients; Performing preprocessing on the collected intraocular pressure data and / or image data, including data cleaning, normalization, and image enhancement; Performing feature extraction on the intraocular pressure data and / or image data by using wavelet transform; Selecting features with a relatively high correlation with eye diseases by using correlation analysis and principal component analysis methods to obtain a reduced-dimensional feature vector; Constructing a KAN network including an input layer, a hidden layer, and an output layer, where the input layer receives the reduced-dimensional feature vector, the hidden layer maps the multi-dimensional input to a one-dimensional space by using the Kolmogorov-Arnold theorem, and the output layer performs classification or regression; The dataset is divided into a training set, a validation set, and a test set, and the KAN network is trained to obtain a pre-trained KAN network model.

[0010] According to an embodiment of the present invention, the feature extraction of intraocular pressure data and / or image data using wavelet transform includes: Select a wavelet function suitable for intraocular pressure data and / or image data; Determine the wavelet function decomposition level according to the data length and feature requirements: Extract energy, variance, and mean statistical features from the decomposed wavelet coefficients; Combining intraocular pressure data and image data, a convolutional neural network is used to extract image features to obtain multi-modal features.

[0011] On the other hand, the present invention also provides a continuous intraocular pressure monitoring device based on a glaucoma valve, including: a receiving coil and a signal acquisition module. The receiving coil is used to receive the pressure electromagnetic wave signal emitted by the transmitting coil of the glaucoma valve and convert it into a pressure electrical signal; the signal acquisition module is used to collect the intraocular pressure signal from the pressure electrical signal of the receiving coil; The signal acquisition module includes: a signal filtering and noise reduction unit, an analog-to-digital conversion unit, a control unit, a variable-frequency signal generator, a variable-frequency signal filter, and a variable-frequency signal amplifier; the signal filtering and noise reduction unit is connected to the first end of the receiving coil, and the variable-frequency signal amplifier is connected to the second end of the receiving coil; The control unit sends a control signal to the variable-frequency signal generator, and the variable-frequency signal generator generates a variable-frequency signal according to the control signal. The variable-frequency signal is filtered by the variable-frequency signal filter and amplified by the variable-frequency signal amplifier and then transmitted to the receiving coil; The control unit adjusts the frequency and phase of the variable-frequency signal through the control signal to achieve the acquisition of the pressure electrical signal data of the receiving coil. The signal filtering and noise reduction unit performs filtering and noise reduction processing on the pressure electrical signal; the analog-to-digital conversion unit performs analog-to-digital conversion on the filtered and noise-reduced pressure electrical signal to obtain a pressure digital signal; The control unit obtains intraocular pressure data according to the pressure digital signal, and based on the intraocular pressure data, uses the pre-trained KAN network model to analyze the intraocular pressure data and gives an analysis result.

[0012] According to an embodiment of the present invention, the device further includes an image acquisition unit for collecting optical coherence tomography images, fundus color photos, and fundus vein images; The control unit uses a convolutional neural network in deep learning to extract image features, extracts multi-modal features based on intraocular pressure data and image data, and uses the pre-trained KAN network model to analyze the multi-modal features and gives an analysis result.

[0013] According to an embodiment of the present invention, the signal filtering and noise reduction unit uses wavelet transform to denoise the pressure electrical signal, effectively separating the low-frequency part carrying intraocular pressure information from the high-frequency noise part; uses Kalman filter to perform real-time filtering in a dynamic environment to filter out high-frequency noise and low-frequency interference; and uses mean filtering and weighted moving average method to smooth the intraocular pressure signal after Kalman filtering.

[0014] On the other hand, the present invention also provides a glaucoma valve, including a glaucoma valve body, further comprising: a pressure sensor installed inside or outside the glaucoma valve body, a transmitting coil externally disposed outside the glaucoma valve body, and the pressure sensor is electrically connected to the transmitting coil through a signal wire; The pressure electrical signal inside the glaucoma valve body sensed by the pressure sensor is transmitted to the transmitting coil through the signal wire, and the transmitting coil converts the pressure electrical signal into a pressure electromagnetic wave signal for transmitting to a continuous intraocular pressure monitoring device.

[0015] The pressure sensor is placed on the abdomen of the glaucoma valve body, and a groove is provided on the abdomen of the glaucoma valve body, and the pressure sensor is hermetically embedded in the groove; the pressure sensor, the signal wire, and the transmitting coil include a packaging layer, and the packaging layer is a multi-layer structure packaging layer formed by combining a polydimethylsiloxane layer and a parylene layer; the transmitting coil is a circular, square, elliptical, polygonal or serpentine coil; the pressure sensor is a piezoresistive, capacitive, inductive, LC resonant or microfluidic sensor.

[0016] Beneficial effects: Compared with the prior art, the beneficial effects produced by the present invention are as follows: 1. Real-time intraocular pressure monitoring: By combining the coil with the glaucoma valve, intraocular pressure data can be continuously and real-time obtained in a wireless manner, enabling real-time monitoring of intraocular pressure changes, ensuring that doctors and patients can timely grasp the fluctuations of intraocular pressure, and thus assisting doctors to optimize treatment plans according to intraocular pressure data.

[0017] 2. Wireless data transmission: The present invention adopts wireless communication technology, avoiding the dependence on frequent medical visits and intraocular pressure measurements in the traditional way, realizing non-invasive and remote intraocular pressure monitoring, and greatly improving the comfort and medical convenience of patients.

[0018] 3. Enhanced intelligence and accuracy of treatment: Real-time and continuous intraocular pressure monitoring provides timely and accurate data support for doctors. Based on the accurate analysis results of the artificial intelligence model, the treatment plan can be dynamically adjusted according to intraocular pressure changes, significantly improving the accuracy and effectiveness of treatment.

[0019] 4. Improve the quality of patients' life: By reducing the frequency of regular visits and providing real-time feedback on intraocular pressure information, patients can more conveniently monitor their treatment in daily life, enhancing the convenience of treatment and the quality of life. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a schematic diagram of a glaucoma valve according to an exemplary embodiment of the present invention.

[0021] Figure 2 is a schematic diagram of a continuous intraocular pressure monitoring device of a glaucoma valve according to an exemplary embodiment of the present invention.

[0022] Figure 3 is a schematic diagram of a variable-frequency signal generator according to an exemplary embodiment of the present invention.

[0023] Figure 4 is a schematic diagram of a variable-frequency signal filter according to an exemplary embodiment of the present invention.

[0024] Figure 5 is a schematic diagram of a variable-frequency signal amplifier according to an exemplary embodiment of the present invention.

[0025] Figure 6 is a schematic diagram of an MCU signal processor as a control unit according to an exemplary embodiment of the present invention.

[0026] Figure 7 is a schematic diagram of a signal filtering and noise reduction unit according to an exemplary embodiment of the present invention.

[0027] Figure 8 is a schematic diagram of an analog-to-digital conversion unit according to an exemplary embodiment of the present invention.

[0028] Figure 9 is a schematic diagram of a KAN network architecture according to an exemplary embodiment of the present invention.

[0029] REFERENCE SIGNS: 1 - glaucoma valve, 11 - glaucoma valve body, 12 - pressure sensor, 13 - transmitting coil, 14 - signal wire; 2 - signal acquisition module, 21 - receiving coil, 211 - first end of the receiving coil, 212 - second end of the receiving coil; 22 - signal filtering and noise reduction unit, 23 - analog-to-digital conversion unit, 24 - control unit, 25 - variable-frequency signal generator, 26 - variable-frequency signal filter, 27 - variable-frequency signal amplifier. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] For the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and roles. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and do not limit their sequence. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and the terms "first" and "second" do not necessarily limit being different.

[0031] It should be noted that in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0032] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. The following at least one (item) or its similar expression refers to any combination of these items, including any combination of single (item) or plural items (items). For example, at least one (item) of a, b or c can represent: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b and c can be single or multiple.

[0033] The technical solution of continuous intraocular pressure monitoring based on a glaucoma valve in the present invention adopts a wireless communication technology based on mutual inductance of double coils, combines the coil with the glaucoma valve, and continuously obtains intraocular pressure data wirelessly, solving the technical problem that traditional glaucoma methods cannot obtain intraocular pressure data in real time. By integrating an intraocular pressure sensor in the glaucoma valve and using the mutual inductance technology of double coils to achieve wireless data transmission, it is possible to obtain intraocular pressure data in real time without adding external electronic components and transmit it to an external device for monitoring, thereby realizing efficient and stable wireless intraocular pressure monitoring.

[0034] As Figure 1 shown, a glaucoma valve 1 includes a glaucoma valve body 11, and it further includes: A pressure sensor 12 installed inside or outside the glaucoma valve body 11, and a transmitting coil 13 externally disposed outside the glaucoma valve body 11. The pressure sensor 12 is electrically connected to the transmitting coil 13 through a signal wire 14. The pressure electrical signal inside the glaucoma valve body 11 sensed by the pressure sensor 12 is transmitted to the transmitting coil 13 through the signal wire 14, and the transmitting coil 13 converts the pressure electrical signal into a pressure electromagnetic wave signal and sends it outwards.

[0035] The pressure sensor, signal wire, and transmitting coil are all covered with a packaging layer, which is a multi-layer structure packaging layer formed by combining a polydimethylsiloxane layer and a parylene layer. Considering the relationship with the intraocular fluid flow, a position with relatively stable intraocular fluid flow and less interference to the sensor is selected for placement. To reduce the adverse effects of gas pressure (possibly from the external eye environment pressure) on the sensor, a protective film with certain elasticity and breathability can be wrapped outside the sensor.

[0036] If the transmitting coil is placed outside the eye, when the signal wire passes through the eyeball wall, it is wrapped with a sealed sleeve to prevent intraocular fluid leakage and external bacterial infection, and is arranged along the natural texture of the eyeball wall to ensure a certain flexibility to adapt to eyeball movement.

[0037] To ensure efficient signal transmission, the design of the coil is optimized according to frequency, coil shape, and distance, so that both the transmission distance and transmission efficiency reach the best state.

[0038] The transmitting coil is a single-layer coil in the shape of a circle, square, ellipse, polygon, or snake; or a multi-layer coil in the shape of a circle, square, ellipse, polygon, or snake. The outer diameter of the transmitting coil is in the range of 2 mm to 10 mm, the inner diameter is in the range of 1 mm to 8 mm, the number of coil turns is in the range of 10 to 100 turns, and the diameter of the coil wire is in the range of 0.05 mm to 0.5 mm.

[0039] A circular acquisition coil, which has the characteristic of uniform magnetic field distribution, relatively compact space utilization, is suitable for placement in the narrow space around the eye socket, and can well adapt to the circular contour of the eyeball.

[0040] The square acquisition coil is conducive to cooperation with planar structures such as rectangular circuit boards. Its four-corner structure can enhance the directivity and selectivity of signal reception and perform well in scenarios where specific areas need to be covered; the side length and corners of the square coil can be adjusted to optimize the magnetic field distribution and adapt to the geometric shape of the glaucoma valve. The elliptical coil combines the characteristics of circular and square coils and is suitable for occasions where an elliptical magnetic field distribution is required.

[0041] According to specific design requirements, the coil can be designed into polygonal shapes such as triangles and hexagons to adapt to specific spatial layouts or magnetic field requirements.

[0042] The serpentine design enables the transmitting coil to have tensile properties and withstand a certain degree of tensile deformation. The elliptical acquisition coil combines the advantages of circles and squares. Its major axis and minor axis can be adjusted as needed, enabling it to better fit the physiological shape of the eye and optimize the signal reception range and intensity.

[0043] The number of turns of the acquisition coil is set with reference to the transmitting coil according to the actual situation. Since the number of turns of the transmitting coil is in the range of 10 to 100 turns, the number of turns of the acquisition coil can be adapted accordingly. Considering that the main function of the acquisition coil is to receive the signals emitted by the transmitting coil, the number of turns can fluctuate around this range to achieve a better signal reception effect. Too few turns may result in insufficient signal reception sensitivity and difficulty in effectively capturing the weak signals from the transmitting coil; while too many turns will increase the inductive ability but also increase the resistance of the coil, thereby causing energy loss.

[0044] The wire material can be traditional silver, platinum, copper materials, superconducting materials, or can also be liquid metal or conductive hydrogel (an organic material), which has good biocompatibility and excellent electrical properties, such as a high signal-to-noise ratio and good electrical conductivity, helping to ensure better signal conductivity and enabling more accurate and efficient signal transmission. In terms of materials, the transmitting coil uses highly conductive materials such as copper or copper alloys, which can ensure good electromagnetic signal reception and conduction performance. Copper is widely used in the manufacture of coils due to its excellent electrical conductivity and cost-effectiveness. Silver has better electrical conductivity than copper and is suitable for applications that require higher transmission efficiency, but it is more expensive. Superconducting materials: In certain high-end medical devices, superconducting materials may be used to achieve zero resistance and higher electromagnetic induction efficiency, although this will increase costs and cooling requirements.

[0045] Due to its excellent chemical stability and corrosion resistance, platinum is widely used in the medical field, especially in devices that need to be used for a long time and are exposed to complex biological environments. Platinum materials have good electrical conductivity and good biocompatibility. Therefore, platinum is an ideal choice in medical devices with high safety requirements for the human body. Although platinum is more expensive, it provides stable long-term performance in some precision medical devices.

[0046] Liquid metal: Liquid metal has unique electrical properties, which can improve the flexibility and stretchability of the coil to a certain extent and is suitable for applications that require higher stretchability and adaptability. Liquid metal has excellent conductivity and can maintain fluidity and self-healing under certain conditions, enabling the transmitting coil to adapt to a dynamically changing environment, especially suitable for devices that require bending and stretching. Liquid metal coils have unique advantages under the requirements of miniaturization and high flexibility.

[0047] Conductive hydrogel (organic material): Conductive hydrogel is an innovative material based on organic materials, with good biocompatibility and excellent conductivity, suitable for devices that need to be in long-term contact with the human body. Hydrogel has good elasticity and adaptability and can adjust its shape according to environmental changes, suitable for medical applications with high requirements for flexibility and stretchability. As an organic material, it can also provide good electrical properties while reducing manufacturing costs, especially suitable for medical devices that require high comfort and safety.

[0048] The pressure sensor is a piezoelectric, piezoresistive, capacitive, inductive, LC resonant or microfluidic sensor.

[0049] The pressure sensor is placed on the abdomen of the glaucoma valve body. There is a groove on the abdomen of the glaucoma valve body, and the pressure sensor is hermetically embedded in the groove; alternatively, the pressure sensor is placed on the back of the glaucoma valve body, and the pressure sensor is adhered to the back of the glaucoma valve body by an adhesive. The intraocular pressure sensor is arranged inside the glaucoma valve near the fluid channel, which is the area where the intraocular pressure is most easily and accurately measured. Its inverted pyramid-shaped electrode plate forms good contact with the surrounding tissues and can accurately reflect the pressure change of the intraocular pressure on the electrode plate. At this position, the intraocular pressure sensor is adapted to the structure of the glaucoma valve, and can make full use of the hydrodynamic characteristics of the valve to ensure the efficient acquisition of the intraocular pressure signal without affecting the normal flow of the aqueous humor in the eye; the transmitting coil is placed around the intraocular pressure sensor at the built-in position inside the glaucoma valve or near the intraocular pressure sensor to ensure the shortest signal transmission distance between the two and reduce signal attenuation.

[0050] The intraocular pressure sensor can be placed on the surface of the eyeball near the glaucoma valve and is in contact with the surface of the eyeball through a soft and biocompatible connecting device. The transmitting coil is placed around the orbit close to the eyeball and is designed to fit the shape of the orbit to receive the signal emitted by the intraocular pressure sensor to the greatest extent.

[0051] The shape of the pressure sensor is an inverted pyramid structure, and the top of the pyramid is a trapezoidal structure or an arc-shaped structure. This makes the sensor not easily broken and its electrical structure not easily changed. This structure has a better elastic modulus, showing good stretching characteristics when bent and having stronger adaptability.

[0052] As shown Figure 2 in the figure, the continuous intraocular pressure monitoring device of the glaucoma valve includes: a receiving coil 21, a signal acquisition module 2, The receiving coil 21 is configured to receive the pressure electromagnetic wave signal emitted by the transmitting coil 13 of the glaucoma valve and convert it into a pressure electrical signal; the signal acquisition module 2 is configured to acquire the intraocular pressure signal from the pressure electrical signal of the receiving coil 21.

[0053] The pressure sensor 12 senses the pressure electrical signal inside the glaucoma valve, and this pressure electrical signal is sent to the transmitting coil 13 through the signal wire 14. The transmitting coil 13 converts the pressure electrical signal into a pressure electromagnetic wave signal and sends it outwards; the receiving coil 21 receives the pressure electromagnetic wave signal sent by the transmitting coil 13 and converts it into a pressure electrical signal. The transmitting coil 13 and the receiving coil 21 are mutual inductance coils; the signal acquisition module 2 acquires the pressure electrical signal from the receiving coil 21 to obtain the intraocular pressure signal.

[0054] The signal acquisition module 2 includes: a signal filtering and noise reduction unit 22, an analog-to-digital conversion unit 23, a control unit 24, a variable-frequency signal generator 25, a variable-frequency signal filter 26, and a variable-frequency signal amplifier 27; the signal filtering and noise reduction unit 22 is connected to the first end 211 of the receiving coil, and the variable-frequency signal amplifier 27 is connected to the second end 212 of the receiving coil; The control unit 24 sends a control signal to the variable-frequency signal generator 25. The variable-frequency signal generator 25 generates a variable-frequency signal according to the control signal. The variable-frequency signal is filtered by the variable-frequency signal filter 26 and amplified by the variable-frequency signal amplifier 27 and then transmitted to the receiving coil 21; The control unit 24 adjusts the frequency and phase of the variable-frequency signal through the control signal to achieve data acquisition of the pressure electrical signal of the receiving coil 21. The signal filtering and noise reduction unit 22 performs filtering and noise reduction processing on the pressure electrical signal; the analog-to-digital conversion unit 23 performs analog-to-digital conversion on the pressure electrical signal after filtering and noise reduction processing to obtain a pressure digital signal; the control unit 24 obtains the intraocular pressure signal according to the pressure digital signal.

[0055] The control unit is an MCU signal processor. The MCU signal processor sends a control signal to the variable-frequency signal generator. The control signal adjusts the operating frequency and amplitude of the variable-frequency signal generator to optimize the signal generation and transmission process and ensure that the signal can maintain the best transmission quality in different monitoring environments. For example, when the signal strength is weak, the MCU signal processor can adjust the variable-frequency signal generator to increase the frequency or amplitude of the signal, thereby enhancing the signal penetration and stability to cope with possible noise interference or signal attenuation problems.

[0056] To monitor the intraocular pressure status in the glaucoma valve in real time, after detecting information such as pressure changes in the glaucoma valve, the control unit is an MCU signal processor, and the MCU signal processor reports to the host computer through WIFI, Bluetooth or network, and the host computer controls the display to display data.

[0057] As Figure 3 shown, the crystal oscillator in the variable frequency signal generator 25 is used to provide a stable clock signal, laying the foundation for generating signals with precise frequencies. The peripheral resistors and capacitors ensure the stability of the system by adjusting the operating frequency of the crystal oscillator, precisely control the frequency and provide a high-quality clock source. Next, the DDS chip (direct digital frequency synthesizer) generates precise frequency signals through digital signal processing technology and can flexibly adjust the output frequency. Finally, the preliminary filtering components (including inductors, resistors, and capacitors) in the output part are used to remove high-frequency noise and unnecessary harmonics, smooth the signal waveform, ensure the purity and stability of the signal, thereby improving the signal quality and ensuring the accuracy of the signal during subsequent processing and transmission.

[0058] The main function of the variable frequency signal generator is to generate signals with variable frequencies. In different application scenarios and monitoring requirements, the intraocular pressure monitoring device needs to generate signals with different frequencies to meet the system requirements.

[0059] For example, in the initial stage, to explore the optimal signal transmission frequency, or in the ocular physiological states of different patients, signals with different frequencies may have different penetration capabilities and signal transmission effects. At this time, the variable frequency signal generator can output signals with corresponding frequencies according to specific situations. Its frequency range can be designed according to actual needs and may be adjusted from relatively low frequencies (such as dozens of hertz) to relatively high frequencies (such as several megahertz to dozens of megahertz) to adapt to various complex ocular environments and different monitoring conditions.

[0060] In terms of the working principle, the variable frequency signal generator can generate signals with the required frequencies based on digital synthesis technology, precisely control the signal frequency using digital logic and algorithms, and achieve flexible adjustment of the signal frequency by frequency division, frequency multiplication or frequency synthesis of the clock signal. In terms of collaborative work with other components, the variable frequency signal generator works closely with the MCU signal processor. The MCU signal processor can send control instructions to the variable frequency signal generator according to the current monitoring situation to adjust the frequency of its output signal. When it is detected that the signal transmission quality is poor, such as severe signal attenuation or large interference, the MCU signal processor can control the variable frequency signal generator to change the frequency to find a more suitable signal frequency for transmission. At the same time, the signals it generates will be sent to corresponding circuits, such as the variable frequency signal filter and the variable frequency signal amplifier, for further processing.

[0061] In terms of performance, the variable-frequency signal generator has the ability of fast frequency switching. When it is necessary to quickly adjust the frequency to cope with different monitoring environments, it can quickly respond to the instructions of the MCU signal processor, complete the frequency switching in a short time, and avoid affecting the continuity and real-time nature of intraocular pressure monitoring due to frequency adjustment delay. For the waveform of the signal, it can generate various waveforms, such as sine wave, square wave or triangular wave, etc. Different waveforms may have different advantages in different signal transmission and processing links. For example, the sine wave has better spectral characteristics in signal transmission, and the square wave is more convenient in some digital circuit processing. According to the specific system requirements, different waveforms can be flexibly selected, and the signal transmission and processing effects can be optimized by adjusting the frequency.

[0062] As Figure 4 shown, the variable-frequency signal filter optimizes the signal quality through the cooperation of multiple components to ensure the stability and accuracy of the signal. The resistors and capacitors in the input part jointly set the input impedance and frequency response of the filter, determine the cut-off frequency of the filter, and thus effectively filter out the low-frequency or high-frequency noise in the signal. The feedback resistor is used to adjust the gain and feedback ratio of the integrated operational amplifier, control the amplification factor of the signal, and help determine the transfer function of the circuit, so as to achieve an accurate filtering effect. The integrated operational amplifier amplifies the signal and performs the filtering function under the control of the feedback resistor, providing stable gain and efficient noise suppression. Through the coordinated work of these components, the variable-frequency signal filtering circuit can effectively optimize the signal, ensure the purity and stability of the output signal, and lay a foundation for subsequent signal processing and transmission.

[0063] The main function of the variable-frequency signal filter is to filter the signal generated by the variable-frequency signal generator to filter out various interference signals mixed in the signal transmission process. These interference signals may originate from environmental noise, electromagnetic interference of other electronic devices or harmonics generated by the signal generator itself. It can select useful signals according to different filtering principles. In this design, a low-pass variable-frequency signal filter is adopted. When the useful signal in the system is in the low-frequency band and is seriously affected by high-frequency interference, its cut-off frequency is set at a position slightly higher than the highest frequency of the useful signal, which can effectively filter out high-frequency interference and make the signal purer. It is closely connected to the variable-frequency signal generator, receives and filters the signal, and then transmits the processed signal to the variable-frequency signal amplifier.

[0064] As Figure 5As shown, the variable-frequency signal amplifier effectively enhances the amplitude of the signal through the cooperation of multiple components and ensures the stable transmission of the signal. The resistor and capacitor in the input part work together. The resistor sets the input impedance of the circuit, and the capacitor, together with the resistor, determines the frequency response, removes the DC component or filters out the noise in a specific frequency range, and optimizes the signal quality. The integrated operational amplifier is used to amplify the signal, and its gain is controlled by an external circuit, ensuring the stability and high-precision amplification of the signal during processing and avoiding signal attenuation or distortion. The inductor and capacitor in the output part form the output filtering part, effectively filtering out high-frequency noise, smoothing the signal waveform, improving the signal quality, and ensuring that the signal can be clearly and stably transmitted to the subsequent processing stage. The coordinated work of these components ensures the amplification, optimization, and stable transmission of the signal.

[0065] The main function of the variable-frequency signal amplifier is to amplify the signal processed by the variable-frequency signal filter. Since the signal may be attenuated due to various factors during signal transmission and processing, such as losses in the transmission line and partial loss of energy after filtering, in order to ensure the accurate and effective progress of subsequent signal processing and analysis, it is necessary to increase the amplitude of the signal to an appropriate level. When the amplitude of the initially collected signal is small and insufficient to be accurately recognized and processed by the subsequent signal processing circuit or monitoring device, the variable-frequency signal amplifier can linearly or non-linearly amplify the amplitude of the signal and enhance the signal strength.

[0066] As Figure 6 shown, as the MCU signal processor serving as the control unit, the internal circuit realizes the processing, storage, and display of the signal through the cooperation of multiple components.

[0067] The MCU signal processor plays a core control and data processing role in the continuous intraocular pressure monitoring device. First of all, the MCU signal processor is responsible for receiving the digital signal from the analog-to-digital conversion circuit. It can perform preliminary processing on the collected intraocular pressure data, including simple statistical analysis of the data, such as calculating the average value, maximum value, minimum value, etc. of the intraocular pressure, to obtain more representative intraocular pressure data. At the same time, it can also analyze the trend of the intraocular pressure data to judge whether the intraocular pressure is rising, falling, or tending to be stable, providing more valuable information for subsequent medical decisions.

[0068] In terms of control functions, the MCU signal processor can control other components in the system. It can control the operating frequency and status of the variable-frequency signal generator, adjust the frequency and amplitude of the signal according to the actual situation, so as to optimize the signal generation and transmission process. For example, when the signal strength is detected to be weak, the MCU can control the signal generator to increase the signal frequency or amplitude to ensure that the intraocular pressure signal can be better collected and transmitted. It can also control different branches in the signal acquisition unit, such as the signal filtering and noise reduction circuit and the variable-frequency signal branch, and adjust the filtering parameters and signal amplification factor according to the environment and data conditions to adapt to different monitoring environments and individual patient differences.

[0069] The MCU signal processor can quickly adjust the frequency of the signal generator using the adaptive frequency selection method to find the resonant frequency of the coil. This method ensures that the target frequency is locked in the shortest time through real-time monitoring and adjustment, improving the measurement efficiency. By continuously adjusting and optimizing the frequency, the resonant frequency of the coil can be accurately found, thereby improving the accuracy of intraocular pressure measurement. This method performs well in a dynamic environment, can adapt to changes in coil parameters, and maintain the stability of measurement. The resonant frequency of the intraocular pressure coil changes with the intraocular pressure. The adaptive frequency selection method can track these changes in real time to ensure accurate measurement under different intraocular pressure conditions and provide continuous and reliable data. Through adaptive adjustment, the adaptive frequency selection method can effectively reduce the interference of environmental noise and improve the signal-to-noise ratio of the signal.

[0070] The MCU signal processor is also responsible for communicating with external devices. It can send the processed data to an external display device through an interface, such as the SPI communication protocol, so that medical staff can intuitively view the intraocular pressure data; it can also store the data in an external storage device, such as an SD card, for subsequent data analysis and research. At the same time, it can receive instructions from external devices, such as the doctor's adjustment instructions for monitoring parameters, and adjust the operating parameters of the system according to these instructions to achieve remote control and configuration of the system.

[0071] Specifically, the crystal oscillator (capacitor, resistor) is used to provide a stable clock signal, providing precise timing control for the MCU to ensure the normal operation of the entire system. The ground filtering capacitor is used to remove power supply noise, smooth the voltage supply, reduce the impact of power supply fluctuations on system performance, and ensure a stable power supply input.

[0072] The SD card is used for data storage, providing the function of storing and subsequent analysis of the processed signals to ensure the persistent preservation of data. The filtering circuit composed of resistors and capacitors is used for filtering the output signal, eliminating high-frequency noise and optimizing the signal quality to ensure the stability and purity of the output signal. Finally, the display screen is used to display the processing results in real time, intuitively presenting the signal data processed by the MCU for easy observation and operation by the user.

[0073] As Figure 7 shown, the function of the signal filtering and noise reduction unit is to remove the noise and unnecessary frequency components in the input signal, ensuring the purity and stability of the signal. It plays a crucial role in the continuous intraocular pressure monitoring device. It is mainly responsible for processing the original signal received by the acquisition coil to extract the useful intraocular pressure signal and eliminate noise interference. In this design, an RC low-pass filter is used, which is composed of passive components such as resistors, capacitors, and inductors. The resistance and capacitance values can be adjusted according to the formula f = 1 / 2πRC to achieve the required cut-off frequency. It has the advantages of simplicity and good real-time performance and can directly process analog signals.

[0074] This signal filtering and noise reduction unit usually includes a second-order RC low-pass filter circuit composed of resistors and capacitors, which is a common filter configuration. The combination of resistors and capacitors determines the cut-off frequency of the circuit. By adjusting the values of these components, the working range of the filter can be set, thereby effectively filtering out high-frequency or low-frequency interference signals. The second-order RC low-pass filter circuit provides better frequency selectivity and higher filtering effect compared to the first-order circuit, and can more accurately remove noise in signal processing to ensure the retention of the effective signal.

[0075] As Figure 8 shown, the main function of the analog-to-digital conversion unit is to convert the analog signal into a digital signal for subsequent digital processing. The basic principle is to convert the continuous analog signal into discrete digital values through sampling and quantization operations on the analog signal. In terms of performance parameters, the resolution determines the ability to distinguish subtle changes in the intraocular pressure signal. For intraocular pressure monitoring, according to the signal amplitude range and accuracy requirements, this design selects a 12-bit resolution, which can divide the input range into 4096 discrete levels and uses a successive approximation ADC. The successive approximation ADC has a fast conversion speed and is suitable for medium-resolution and high-sampling frequency scenarios. The input analog signal is gradually approximated and the digital code is obtained by comparing it with the internal digital voltage through a comparator. It is composed of a comparator, a DAC, and a successive approximation register, etc. This circuit closely cooperates with the signal filtering and noise reduction circuit, and the pure analog signal after filtering processing will be input into it to provide input for the subsequent microcontroller or digital signal processor.

[0076] Specifically, the ADC conversion chip is the core component of this circuit and is used to convert the input analog signal into a 12-bit digital signal. The 12-bit resolution ensures the accurate capture of subtle changes in the analog signal, improving the precision and accuracy of the data. The analog signal input section is responsible for receiving the analog signal from the previous-stage circuit and transmitting it to the ADC chip for conversion. The ground filtering capacitor is used to filter out the noise in the power supply or signal input section, ensuring the quality of the analog signal, making it unaffected by external electromagnetic interference, and ensuring that the converted digital signal is purer and more stable. Through the collaborative work of these components, the analog-to-digital conversion unit can achieve the conversion of high-precision and low-noise analog signals to digital signals, providing accurate data support for subsequent digital signal processing.

[0077] After the pressure sensor in the valve body of the glaucoma valve detects information such as pressure changes in the glaucoma valve, it uses the principle of electromagnetic induction through the mutual inductance of the double coils to transmit the data to the external monitoring system. For example, the MCU signal processor reports to the host computer through WIFI, Bluetooth or the network, and the host computer controls the display to display the data. After the pressure sensor detects information such as pressure changes in the glaucoma valve, it converts this information into corresponding electrical signals. The transmitting coil uses the principle of electromagnetic induction to convert the electrical signal transmitted by the pressure sensor into an electromagnetic wave and transmit it. The receiving coil senses the signal in the electromagnetic field generated by the transmitting coil and converts it into a pressure electrical signal. After the external signal acquisition module receives the pressure electrical signal transmitted by the receiving coil, it analyzes it to obtain the real intraocular pressure data, realizing the real-time monitoring of the intraocular pressure size state in the glaucoma valve.

[0078] Based on the above device, the continuous intraocular pressure monitoring method based on the glaucoma valve includes the following steps: The receiving coil receives the pressure electromagnetic wave signal transmitted by the transmitting coil of the glaucoma valve and converts it into a pressure electrical signal; The control unit sends a control signal to the variable-frequency signal generator, and the variable-frequency signal generator generates a variable-frequency signal according to the control signal. The variable-frequency signal is filtered by the variable-frequency signal filter and amplified by the variable-frequency signal amplifier and then transmitted to the receiving coil; The control unit adjusts the frequency and phase of the variable-frequency signal through the control signal to achieve the data acquisition of the pressure electrical signal of the receiving coil, and performs filtering and noise reduction processing on the pressure electrical signal using the signal filtering and noise reduction unit; performs analog-to-digital conversion on the pressure electrical signal after filtering and noise reduction processing to obtain a pressure digital signal; the control unit obtains the intraocular pressure signal according to the pressure digital signal.

[0079] In order to improve the accuracy and stability of the intraocular pressure data, the present invention adopts a series of effective algorithms and methods in aspects such as signal analysis, filtering and optimization. The filtering and noise reduction processing of the pressure electrical signal using the signal filtering and noise reduction unit specifically includes: The wavelet transform is used to denoise the piezoresistive signal, effectively separating the low-frequency part carrying intraocular pressure information from the high-frequency noise part; Kalman filtering is used for real-time filtering in a dynamic environment to filter out high-frequency noise and low-frequency interference; The mean filtering and weighted moving average methods are used to smooth the intraocular pressure signal after Kalman filtering.

[0080] 1. Signal analysis and denoising: Since the intraocular pressure signal may be disturbed by external environmental noise, the wavelet transform (Wavelet Transform) is used to denoise the signal. The wavelet transform can effectively separate the low-frequency part (representing intraocular pressure information) from the high-frequency noise part in the signal.

[0081] Among them, W represents the wavelet transform, Threshold is the threshold selected according to the noise level of the signal, and the high-frequency components less than the threshold are removed.

[0082] 2. Filtering algorithm: To further eliminate the low-frequency interference and high-frequency noise in the signal, the Kalman filter (Kalman Filter) method is adopted. Kalman filtering can achieve real-time filtering of the signal in a dynamic environment, with good self-adaptability and strong robustness. Kalman filter formula: Among them, is the current estimated value, is the measured value, is the system matrix, is the Kalman gain, which controls the weights of the prediction error and the measurement error. In practical applications, the output signal of the filter circuit will be sent into a microcontroller or a digital signal processor and further processed through the Kalman filter algorithm. The microcontroller can run the Kalman filter algorithm to perform real-time estimation and update on the signal output by the filter circuit to obtain a more accurate intraocular pressure measurement value.

[0083] 3. Signal optimization and smoothing processing: For the collected intraocular pressure signal, the mean filtering and weighted moving average methods are used for optimization processing to reduce the sudden noise and improve the signal smoothness. Weighted moving average algorithm: , where wi is the weighting coefficient, which is usually set according to the importance of the signal or the accuracy of historical data. Through weighted averaging, the intraocular pressure data can be smoothed and its stability can be improved.

[0084] 4. Signal Feature Extraction and Trend Prediction: For the feature extraction of continuous intraocular pressure data, Fourier Transform and time-domain analysis methods are used to extract the periodic features and trends of intraocular pressure changes.

[0085] Fourier Transform formula: , where represents the frequency-domain signal, is the time-domain signal. Fourier Transform helps to extract the frequency components in the signal for further analysis of the periodic fluctuations of intraocular pressure.

[0086] The data obtained in real time by the continuous intraocular pressure monitoring device can be combined with an artificial intelligence (AI) model to achieve the classification of auxiliary eye diseases.

[0087] First, data collection and preprocessing are carried out. An intraocular pressure measurement device based on the glaucoma valve is used to continuously collect the intraocular pressure data of patients. At the same time, other eye image data of patients, such as optical coherence tomography (OCT) images, fundus color photos, etc., are collected. The collected intraocular pressure data and image data are preprocessed, including operations such as data cleaning, normalization, and image enhancement, to improve the data quality and consistency.

[0088] Then, feature extraction and selection are carried out. Wavelet transform is used to extract features from the intraocular pressure data and / or image data. Wavelet transform can effectively decompose signals and images, extract features at different scales and frequencies, and helps to capture subtle changes and abnormal patterns in the data. Through methods such as correlation analysis and principal component analysis (PCA), features with higher correlation with eye diseases are selected to reduce the data dimension and improve the efficiency and accuracy of the model.

[0089] Example 1: Analysis of intraocular pressure data based on Kolmogorov - Arnold Networks (KAN network).

[0090] Analysis of intraocular pressure data based on the KAN network to improve the accuracy of eye disease diagnosis.

[0091] First, data collection and preprocessing. Use a continuous intraocular pressure monitoring device based on a glaucoma valve to continuously collect intraocular pressure data of patients. These data are usually time-series data with time on the horizontal axis and intraocular pressure values on the vertical axis. Further, other ophthalmic examination data, such as OCT (optical coherence tomography), fundus color photographs, etc., can be combined to obtain more comprehensive ophthalmic information. During data preprocessing, outliers and noise in the data are removed. For example, moving average filtering, wavelet denoising, adaptive filtering, or deep learning denoising models are used to remove noise in the intraocular pressure data. The intraocular pressure data is normalized to a certain range (such as between 0 and 1) for subsequent processing and analysis. The dataset is divided into a training set, a validation set, and a test set with proportions of 60%, 20%, and 20%. The diversity of training data is increased through data augmentation techniques (such as adding noise, random transformation, etc.).

[0092] Then, perform feature extraction using wavelet transform. Select an appropriate wavelet function according to the characteristics of the intraocular pressure data, such as Daubechies (dbN), Symlet (symN), etc. Determine the decomposition level of the wavelet function: Determine the decomposition level according to the data length and feature requirements, usually 3 to 5 levels. Extract features: Extract statistical features such as energy, variance, and mean from the decomposed wavelet coefficients. Combining intraocular pressure data and other ophthalmic examination data (such as OCT images, fundus color photographs, etc.), convolutional neural networks (CNNs) in deep learning can be used to extract image features to obtain more modality features.

[0093] For example, the formula for wavelet transform is: , where j is the scale parameter, k is the translation parameter, ψ ( t ) is the wavelet function.

[0094] Calculate the energy of wavelet coefficients at each scale: , where is the energy at the i-th scale, is the j-th wavelet coefficient at this scale, is the number of coefficients at this scale.

[0095] Calculate the variance of wavelet coefficients at each scale: , where is the mean of wavelet coefficients at this scale. The mean of wavelet coefficients at each scale: .

[0096] After that, feature selection and dimensionality reduction are performed. Analyze the correlation between the extracted features and eye diseases, and select the features with higher correlation with the diseases. Use principal component analysis (PCA) to perform PCA dimensionality reduction on the extracted features, retain the main feature components, reduce the data dimension, and improve the model efficiency. Specifically, when implementing, retain 95% of the variance to reduce the data dimension.

[0097] Calculate the covariance matrix of the data: , where is the data sample, is the mean of the data, and N is the number of samples. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and eigenvectors. Select the eigenvectors corresponding to the largest several eigenvalues to form a projection matrix. Project the data into the new low-dimensional space.

[0098] After that, construct and train the KAN network model. As Figure 9 shown, the structure of the KAN network includes an input layer, a hidden layer, and an output layer. The input layer receives the dimensionality-reduced feature vectors, the hidden layer uses the Kolmogorov-Arnold theorem to map the multi-dimensional input to a one-dimensional space, and the output layer performs classification or regression, such as a fully connected layer using the ReLU activation function. Output layer: Perform classification or regression, such as an output layer using the Sigmoid activation function.

[0099] After randomly initializing the network parameters, perform model training. Use binary_crossentropy as the loss function and adam as the optimizer. Set the number of training epochs and the batch size, and use the validation set for validation.

[0100] After training the KAN network model for intraocular pressure data analysis, apply it to the actual diagnosis of eye diseases to classify and analyze the newly collected real-time data. Verify and feedback the diagnosis results of the model through clinical verification and the professional judgment of doctors.

[0101] Through the above steps, an intelligent diagnosis of eye diseases can be achieved using the model based on Kolmogorov-Arnold Networks, improving the accuracy and efficiency of diagnosis.

[0102] Potential applications of the present invention: Based on the real-time obtained intraocular pressure data, there are various potential applications that can significantly improve the treatment effect of glaucoma and the quality of life of patients. First of all, doctors can dynamically adjust the treatment plan according to the intraocular pressure fluctuations to achieve personalized treatment and precise adjustment of drug dosage. Real-time monitoring can also provide a warning function to help detect abnormal intraocular pressure at an early stage, remind patients to seek medical treatment in time, and thus reduce the risk of optic nerve damage. In addition, patients can view the intraocular pressure data in real time through a mobile application for self-management, enhancing treatment compliance. Long-term data also supports clinical research, helps evaluate the treatment effect and optimize the treatment strategy, and provides a research basis for the pathogenesis and treatment methods of glaucoma. Combining with telemedicine technology, the intraocular pressure data can be remotely monitored, reducing the burden on patients to visit the doctor and improving the treatment efficiency. Finally, the system can also provide personalized health management suggestions based on the intraocular pressure data to help patients adjust their lifestyle, maintain a healthy intraocular pressure level, and prevent the progression of glaucoma. Through these applications, the real-time intraocular pressure data provides important support for the accuracy, effectiveness of glaucoma treatment and the improvement of the quality of life of patients.

[0103] In addition, according to an exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program may also be provided. The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to execute the continuous intraocular pressure monitoring method based on the glaucoma valve according to the exemplary embodiment of the present invention. The computer-readable recording medium is any data storage device capable of storing data readable by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, compact disc read-only memory, magnetic tape, floppy disk, optical data storage device, and carrier waves (such as data transmission through the Internet via a wired or wireless transmission path).

[0104] In addition, according to an exemplary embodiment of the present invention, a computing device may also be provided. The computing device includes a processor and a memory. The memory is used to store a computer program. The computer program, when executed by the processor, causes the processor to execute the computer program of the continuous intraocular pressure monitoring method based on the glaucoma valve according to the exemplary embodiment of the present invention.

[0105] Although the present invention has been described herein in connection with various embodiments, however, in the process of implementing the claimed invention, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the drawings, the disclosure content, and the like. In the specification, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality of cases. A single processor or other unit can implement several functions listed in the specification. Certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0106] Although the present invention has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the invention. Accordingly, the present specification and the drawings are merely exemplary illustrations of the invention and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A continuous intraocular pressure monitoring method based on a glaucoma valve, characterized in that, It includes the following steps: The receiving coil receives the pressure electromagnetic wave signal emitted by the transmitting coil of the glaucoma valve and converts it into a pressure electrical signal; The control unit sends a control signal to the frequency conversion signal generator. The frequency conversion signal generator generates a frequency conversion signal according to the control signal. The frequency conversion signal is filtered by the frequency conversion signal filter and amplified by the frequency conversion signal amplifier and then transmitted to the receiving coil; The control unit adjusts the frequency and phase of the frequency conversion signal through the control signal to realize the acquisition of the pressure electrical signal data of the receiving coil, and performs filtering and noise reduction processing on the pressure electrical signal by using the signal filtering and noise reduction unit; The pressure electrical signal after filtering and noise reduction processing is subjected to analog-to-digital conversion by the analog-to-digital conversion unit to obtain a pressure digital signal; The control unit obtains the intraocular pressure data according to the pressure digital signal. Based on the intraocular pressure data, the pre-trained KAN network model is used to analyze the intraocular pressure data and give an analysis result.

2. The continuous intraocular pressure monitoring method of the glaucoma valve according to claim 1, characterized in that The filtering and noise reduction processing of the pressure electrical signal by using the signal filtering and noise reduction unit specifically includes: Wavelet transform is used to denoise the pressure electrical signal, effectively separating the low-frequency part carrying intraocular pressure information and the high-frequency noise part; Kalman filter is used for real-time filtering in a dynamic environment to filter out high-frequency noise and low-frequency interference; Mean filtering and weighted moving average method are used to smooth the intraocular pressure signal after Kalman filtering.

3. The continuous intraocular pressure monitoring method of the glaucoma valve according to claim 1, characterized in that The method further includes: collecting optical coherence tomography images, fundus color photos, and fundus vein images, extracting image features by using a convolutional neural network in deep learning, extracting multi-modal features based on intraocular pressure data and image data, and using the pre-trained KAN network model to analyze the multi-modal features and give an analysis result.

4. The continuous intraocular pressure monitoring method of the glaucoma valve according to claim 1, characterized in that The training method of the pre-trained KAN network model includes the following steps: Use the continuous intraocular pressure monitoring device based on the glaucoma valve to continuously collect the intraocular pressure data and / or eye image data of patients; Perform preprocessing on the collected intraocular pressure data and / or image data, including data cleaning, normalization, and image enhancement; Use wavelet transform to extract features from the intraocular pressure data and / or image data; Select features with higher correlation with eye diseases through correlation analysis and principal component analysis methods to obtain a feature vector after dimensionality reduction; Construct a KAN network including an input layer, a hidden layer, and an output layer. The input layer receives the feature vector after dimensionality reduction. The hidden layer uses the Kolmogorov-Arnold theorem to map the multi-dimensional input to a one-dimensional space, and the output layer performs classification or regression; Divide the data set into a training set, a validation set, and a test set, and train the KAN network to obtain a pre-trained KAN network model.

5. The continuous intraocular pressure monitoring method of the glaucoma valve according to claim 4, characterized in that The extracting features from the intraocular pressure data and / or image data by using wavelet transform includes: Select a wavelet function suitable for intraocular pressure data and / or image data; Determine the wavelet function decomposition level according to the data length and feature requirements: Extract energy, variance, and mean statistical features from the decomposed wavelet coefficients; Combine the intraocular pressure data and image data, and use a convolutional neural network to extract image features to obtain multi-modal features.

6. A glaucoma valve-based continuous intraocular pressure monitoring device for implementing the method according to any one of claims 1-5, characterized in that, Including: A receiving coil and a signal acquisition module. The receiving coil is used to receive the pressure electromagnetic wave signal emitted by the transmitting coil of the glaucoma valve and convert it into a pressure electrical signal; The signal acquisition module is used to collect the intraocular pressure signal from the pressure electrical signal of the receiving coil; The signal acquisition module includes: a signal filtering and noise reduction unit, an analog-to-digital conversion unit, a control unit, a variable-frequency signal generator, a variable-frequency signal filter, and a variable-frequency signal amplifier; the signal filtering and noise reduction unit is connected to the first end of the receiving coil, and the variable-frequency signal amplifier is connected to the second end of the receiving coil; The control unit sends a control signal to the variable-frequency signal generator, and the variable-frequency signal generator generates a variable-frequency signal according to the control signal. The variable-frequency signal is filtered by the variable-frequency signal filter and amplified by the variable-frequency signal amplifier and then transmitted to the receiving coil; The control unit adjusts the frequency and phase of the variable-frequency signal through the control signal to realize the acquisition of the pressure electrical signal data of the receiving coil. The signal filtering and noise reduction unit performs filtering and noise reduction processing on the pressure electrical signal; the analog-to-digital conversion unit performs analog-to-digital conversion on the filtered and noise-reduced pressure electrical signal to obtain a pressure digital signal; The control unit obtains the intraocular pressure data according to the pressure digital signal, and based on the intraocular pressure data, uses a pre-trained KAN network model to analyze the intraocular pressure data and gives an analysis result.

7. The continuous intraocular pressure monitoring device based on the glaucoma valve according to claim 6, characterized in that The device further includes an image acquisition unit for collecting optical coherence tomography images, fundus color photos, and fundus vein images; The control unit uses a convolutional neural network in deep learning to extract image features, extracts multi-modal features based on intraocular pressure data and image data, and uses a pre-trained KAN network model to analyze the multi-modal features and gives an analysis result.

8. The continuous intraocular pressure monitoring device based on the glaucoma valve according to claim 6, characterized in that The signal filtering and noise reduction unit uses wavelet transform to denoise the pressure electrical signal, effectively separating the low-frequency part carrying intraocular pressure information from the high-frequency noise part; uses Kalman filtering to perform real-time filtering in a dynamic environment to filter out high-frequency noise and low-frequency interference; Use mean filtering and weighted moving average methods to smooth the intraocular pressure signal after Kalman filtering.

9. A glaucoma valve, comprising a glaucoma valve body, characterized in that, It also includes: A pressure sensor installed inside or outside the glaucoma valve body, and a transmitting coil placed outside the glaucoma valve body. The pressure sensor is electrically connected to the transmitting coil through a signal wire; The pressure electrical signal inside the glaucoma valve body sensed by the pressure sensor is transmitted to the transmitting coil through the signal wire, and the transmitting coil converts the pressure electrical signal into a pressure electromagnetic wave signal for transmitting to the continuous intraocular pressure monitoring device.

10. The glaucoma valve according to claim 9, characterized in that The pressure sensor is placed on the abdomen of the glaucoma valve body, and a groove is provided on the abdomen of the glaucoma valve body, and the pressure sensor is hermetically embedded in the groove; the pressure sensor, the signal wire, and the transmitting coil include a packaging layer, and the packaging layer is a multi-layer structure packaging layer formed by combining a polydimethylsiloxane layer and a parylene layer; the transmitting coil is a circular, square, elliptical, polygonal or serpentine coil; the pressure sensor is a piezoresistive, capacitive, inductive, LC resonance type or microfluidic sensor.

Images