Knee joint nerve treatment auxiliary positioning surface-mounted electrode system and method

Abstract

The invention relates to the field of medical instruments, and discloses a knee joint nerve treatment auxiliary positioning surface-mounted electrode system and method, the system comprises a signal acquisition module, a nerve positioning module, an electrode adjustment module, an energy control module, a treatment feedback module and a treatment evaluation module, and the invention also discloses a knee joint nerve treatment auxiliary positioning surface-mounted electrode method. Comprising the following steps: initializing a system, starting a knee joint nerve treatment system, installing a surface-mounted electrode, and arranging an energy control module, a signal acquisition module, a nerve positioning module, a treatment feedback module and an electrode adjusting module to prepare to start treatment. By introducing real-time physiological feedback, dynamic adjustment of electrode positions and radio frequency energy and the treatment evaluation module, more accurate, safer and more personalized knee joint treatment is realized, the stability of the treatment effect and the curative effect are improved, and side effects are reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and specifically to a knee joint nerve treatment auxiliary positioning patch electrode system and method. Background Art

[0002] In modern society, knee joint diseases have become important health problems affecting people's quality of life. Especially with the increase of age or excessive exercise, the degeneration and injury of the knee joint gradually worsen. Although traditional treatment methods can relieve symptoms, they often fail to achieve lasting effects; while radiofrequency treatment has gradually become a popular treatment method because it can effectively relieve pain and promote tissue repair. For radiofrequency treatment of the knee joint, accurate electrode positioning and energy control are crucial to ensure the treatment effect while avoiding unnecessary side effects.

[0003] Currently, radiofrequency treatment technology mainly relies on fixed-point electrode stimulation and usually treats based on preset treatment parameters. These treatment methods stimulate nerves through electrodes and adjust temperature, aiming to relieve pain and inflammation around the knee joint. The radiofrequency treatment in the prior art can provide certain curative effects and achieve relatively basic treatment parameter adjustment during the treatment process. Through different current intensities and treatment durations, the prior art can perform a certain degree of adjustment on different parts of the knee joint to cope with different types of pain or injuries. However, most of these technologies lack personalized customization and real-time feedback mechanisms and rely more on doctors' experience and traditional treatment standards.

[0004] However, the deficiencies of the prior art are the lack of adaptability to individual patient differences and the inability to dynamically adjust according to real-time physiological feedback during the treatment process; traditional methods usually use fixed treatment parameters and cannot flexibly cope with the deviation of electrode positions or tissue reactions during the treatment process. In addition, the prior art does not establish an effective real-time feedback system, and doctors cannot obtain key signals immediately during the treatment process, often making it difficult to adjust the treatment plan in a timely manner. This lack of a feedback mechanism is likely to lead to fluctuations in the treatment effect and even bring unnecessary side effects. More importantly, the prior art does not have a comprehensive and systematic mechanism for evaluating the treatment effect and cannot provide accurate reports and optimization suggestions for doctors after the treatment. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a knee joint nerve treatment auxiliary positioning patch electrode system and method, which solve the problems of lack of personalized adjustment and real-time feedback in the prior art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A knee joint nerve treatment auxiliary positioning patch electrode system, comprising: A signal acquisition module, which is used to collect multi-channel signals around the knee joint in real time, and the multi-channel signals include impedance data and electrical pulse feedback signals; A nerve localization module, which is connected to the signal acquisition module, determines the position of the target nerve according to the data collected by the signal acquisition module, and generates nerve localization data; An electrode adjustment module, which is connected to the nerve localization module, and is used to adjust the position of the electrode according to the localization data provided by the nerve localization module; An energy control module, which is respectively connected to the nerve localization module and the electrode adjustment module, and controls the energy output of the radiofrequency generator according to the nerve localization and electrode position data; A treatment feedback module, which is respectively connected to the energy control module and the electrode adjustment module, is used to monitor the physiological feedback signals during the treatment process in real time, and adjusts the electrode position and energy output according to the feedback to form a closed-loop control; A treatment evaluation module, which is connected to the treatment feedback module, is used to generate a treatment report after the treatment, evaluate the treatment effect, and provide suggestions on whether to continue the treatment and optimization.

[0007] Preferably, the signal acquisition module includes: A multi-channel electrode array, which is used to detect the physiological signals of multiple nerve regions simultaneously to ensure the comprehensiveness and accuracy of the signals; A signal preprocessing unit, which is used to filter, denoise and enhance the collected signals; A data transmission interface, which is used to transmit the processed signal data to the nerve localization module for further analysis and calculation.

[0008] Preferably, the nerve localization module includes: An anatomical model database, which stores the standard anatomical structure information of the nerves around the knee joint to provide a localization reference; A dynamic calibration unit, which is used to dynamically adjust the anatomical model parameters according to the signal characteristics of individual patients to make the nerve localization result more accurate; A nerve signal matching algorithm, which is used to analyze the data provided by the signal acquisition module and compare it with the information in the anatomical model database to accurately locate the target nerve.

[0009] Preferably, the electrode adjustment module includes: An electrode support unit, which is used to fix the surface-mounted electrode and can be adaptively adjusted according to the anatomical characteristics of the patient; A positioning fine-tuning unit, which is used to accurately adjust the position of the electrode after the nerve localization is completed; A feedback response unit, which is connected to the treatment feedback module and dynamically adjusts the electrode position according to the treatment feedback information.

[0010] Preferably, the energy control module includes: An energy calculation unit that calculates appropriate radio frequency energy parameters based on nerve localization data, target nerve characteristics, and tissue impedance; An energy regulation unit that dynamically controls the output power, action time, and frequency of radio frequency energy to meet individualized treatment requirements; A safety protection unit that monitors the output of radio frequency energy in real time and automatically adjusts or terminates the energy output in case of abnormalities.

[0011] Preferably, the treatment feedback module includes: A physiological signal monitoring unit that monitors the physiological responses of the nerve during the treatment in real time, and the physiological responses include muscle contraction, temperature change, and impedance change; An adaptive regulation unit that automatically adjusts the energy output parameters based on the monitored feedback data to optimize the treatment effect; An intelligent learning unit that records the feedback data during multiple treatment processes and optimizes the subsequent treatment plan through machine learning methods.

[0012] Preferably, the treatment evaluation module includes: A treatment effect analysis unit that evaluates the patient's response to radio frequency treatment based on the treatment feedback data; A personalized report generation unit that generates a personalized treatment report, provides a curative effect analysis, and gives optimization suggestions for the subsequent treatment plan; A clinical database comparison unit that compares the patient's treatment data with the historical data in the clinical database to evaluate the treatment success rate and adjust the future treatment strategy.

[0013] Preferably, the feedback response unit includes: A signal analysis unit that receives the real-time physiological signals provided by the treatment feedback module and analyzes muscle responses, electrophysiological changes, and local temperature fluctuations to evaluate the nerve response; A position calibration unit that adjusts the angle and contact pressure of the electrode support unit based on the analyzed feedback data to optimize the electrode fit and the nerve action area; A dynamic adaptation unit that adaptively adjusts the electrode micro-displacement using the patient's individual anatomical characteristics and real-time treatment feedback data to keep the treatment area always at the optimal target position.

[0014] Preferably, the energy calculation unit includes: A biological characteristic analysis unit that collects tissue impedance, blood flow characteristics, and physiological response data of the target nerve to provide a basis for personalized energy calculation; A dose regulation unit calculates an appropriate RF energy intensity based on the analysis result and adjusts the pulse width and frequency to match the nerve treatment requirements; A real-time correction unit dynamically fine-tunes the energy output using the feedback information obtained during the treatment process.

[0015] The present invention also provides a method for assisting in positioning surface electrodes for knee joint nerve treatment, including the following steps: System initialization: Start the knee joint nerve treatment system, install the surface electrodes, and set the energy control module, signal acquisition module, nerve positioning module, treatment feedback module, and electrode adjustment module to prepare for starting the treatment; Signal acquisition: Use the multi-channel electrode array in the signal acquisition module to simultaneously acquire the physiological signals around the knee joint, including impedance data and electrical pulse feedback signals, and perform filtering, denoising, and enhancement processing through the signal preprocessing unit; Nerve positioning: According to the data provided by the signal acquisition module, the nerve positioning module analyzes and compares the standard nerve positions in the anatomical model database, and combines the signal characteristics of the patient's individual to dynamically calibrate and determine the position of the target nerve; Electrode adjustment: According to the positioning data provided by the nerve positioning module, the electrode adjustment module adjusts the position of the surface electrode, and ensures that the electrode is accurately aligned with the target nerve through the electrode support unit and the positioning fine-tuning unit; Energy control: According to the nerve positioning data and the electrode position data, the energy control module calculates the appropriate RF energy parameters and controls the output power, frequency, and duration of the RF generator; Monitoring and feedback adjustment during the treatment process: During the treatment process, the treatment feedback module monitors the physiological responses of the target nerve in real time, obtains muscle response and temperature change information using the physiological signal monitoring unit, and adjusts the electrode position and RF energy output according to the feedback data; Treatment evaluation and report generation: After the treatment is completed, the treatment evaluation module analyzes the treatment feedback data and generates a personalized treatment report to evaluate the treatment effect of the patient and provide suggestions on whether to continue the treatment and optimization; Treatment optimization and end: According to the analysis result of the treatment effect, judge whether it is necessary to adjust the treatment plan and continue the treatment.

[0016] The present invention provides a knee joint nerve treatment assisting positioning surface electrode system and method. It has the following beneficial effects: 1. The present invention adopts a nerve treatment assistance system based on multi-parameter feedback. By real-time monitoring of tissue impedance, nerve response, and temperature change, dynamically adjusting the RF energy and electrode position, it achieves an accurate and personalized treatment effect. Compared with the simple fixed-value RF treatment method in the prior art, the present invention can effectively cope with individual differences, avoid overheating or over-stimulation, and significantly improve the safety and effect of the treatment.

[0017] 2. Through the close cooperation of the treatment feedback module with the energy control module and the nerve localization module, the present invention forms a closed-loop control mechanism. This mechanism can automatically adjust the treatment plan according to real-time physiological feedback, ensuring the stability of treatment. Compared with traditional treatment methods, the present invention avoids the lag of manual intervention, reduces human errors during the treatment process, and realizes more intelligent and precise treatment.

[0018] 3. The present invention uses a treatment evaluation module to generate personalized treatment reports and provide suggestions on whether to continue treatment and optimize the plan. This comprehensive evaluation method can help doctors better judge the progress and effect of treatment. Compared with the existing technology that only relies on rough observation during the treatment process, it significantly improves the transparency and adjustability of the treatment effect, and reduces the blindness of treatment.

[0019] 4. By combining advanced algorithms such as fuzzy control and machine learning, the present invention can not only adjust treatment parameters according to real-time data, but also learn from historical treatment data and adaptively optimize the treatment plan. Compared with traditional single adjustment strategies, the present invention can more intelligently cope with complex treatment environments and patient differences, significantly improving the treatment effect and patient satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic diagram of the system construction of the present invention; Figure 2 is a framework diagram of the signal acquisition module of the present invention; Figure 3 is a framework diagram of the nerve localization module of the present invention; Figure 4 is a framework diagram of the electrode adjustment module of the present invention; Figure 5 is a framework diagram of the energy control module of the present invention; Figure 6 is a framework diagram of the treatment feedback module of the present invention; Figure 7 is a framework diagram of the treatment evaluation module of the present invention; Figure 8 is a schematic diagram of the method flow of the present invention; Figure 9 is a schematic diagram of the surface-mounted electrode of the present invention; Figure 10 is a schematic diagram of the surface-mounted electrode with a puncture needle of the present invention; Figure 11 is a schematic diagram of the radiofrequency electrode inserted into the puncture needle of the present invention; Figure 12 is a schematic diagram of the mobile positioning hole of the present invention; Figure 13Schematic diagram of the adhesion positioning layer of the present invention; Figure 14 Schematic diagram of the superior medial geniculate nerve of the present invention.

[0021] Among them, 1. Surface-mounted electrode; 2. Moving positioning hole; 3. Motor surface layer; 4. Electrical layer; 5. Adhesion positioning layer; 6. Superior lateral geniculate nerve; 7. Superior medial geniculate nerve; 8. Inferior medial geniculate nerve. Detailed implementation manners

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to the attached Figure 1 - attached Figure 7 , the embodiment of the present invention provides a surface-mounted electrode system for knee joint nerve treatment auxiliary positioning, including: A signal acquisition module for real-time acquisition of multi-channel signals around the knee joint, and the multi-channel signals include impedance data and electrical pulse feedback signals; The signal acquisition module is used to real-time acquire multi-channel physiological signals around the knee joint, and provide data support for subsequent nerve positioning, electrode adjustment, and energy control. To ensure the accuracy and comprehensiveness of the data, the signal acquisition module can simultaneously obtain various different types of signal information, and optimize the data quality through signal processing means. Generally, the signals collected by this module include tissue impedance data and electrical pulse feedback signals, and these data are used to characterize the physiological state of nerve tissue and its response to external stimuli.

[0024] In this embodiment, the signal acquisition module uses a multi-channel electrode array to obtain data. The multi-channel electrode array is composed of multiple independent electrode units, and the electrode units can act on different nerve regions respectively to improve the spatial resolution of the signals. Specifically, each electrode unit includes a signal input terminal and a reference electrode. The input terminal is used to detect the change of the electrical signal of the local tissue, and the reference electrode is used to provide a reference potential for differential amplification to improve the stability of the signal. In some embodiments, the spacing between the electrode units can be adjusted according to the anatomical structure of the patient to optimize the signal acquisition effect.

[0025] In a possible implementation, the signal acquisition module further includes a signal preprocessing unit. The signal preprocessing unit is used to filter, denoise, amplify, etc. the acquired original signal to enhance the signal quality. Generally, the acquisition of impedance data requires measurement using a low-frequency alternating current signal, and the common frequency range is between 1 kHz and 100 kHz. On this basis, more abundant tissue characteristic information can be obtained by measuring the impedance change at different frequencies. For example, in some embodiments, the impedance can be calculated by the following formula: ; wherein, is the applied alternating voltage, is the measured response current, is the tissue impedance.

[0026] As an option, the signal preprocessing unit can use a band-pass filter to remove high-frequency noise and combine an adaptive noise suppression algorithm to reduce interference. Specifically, methods such as Kalman filtering or wavelet transform can be used to optimize the acquired signal, so that the effective signal part is enhanced while the interference component is effectively suppressed.

[0027] In another possible implementation, the signal acquisition module further has a data transmission interface for transmitting the processed signal data to the nerve localization module. The data transmission can be carried out in a wired or wireless manner. In the wired transmission mode, a low-noise shielded cable can be used to reduce electromagnetic interference. In the wireless mode, Bluetooth Low Energy (BLE) or Wi-Fi is usually used for data transmission to ensure the real-time performance and stability of the signal.

[0028] In some embodiments, the signal acquisition module can also combine an electrical pulse feedback signal to evaluate the excitability and functional state of the nerve. The electrical pulse feedback signal refers to the response signal generated by nerve tissue after an external electrical stimulus is applied. Generally, the intensity, duration, and frequency of this signal can reflect the health status of the nerve. For example, the conduction ability of the nerve can be evaluated by measuring the compound action potential (CAP) of the nerve, and its calculation formula is as follows: ; wherein, is the nerve potential signal, and are respectively the start and end times of integration, dimensional represents the cumulative action amount of the nerve potential signal, is a small time increment.

[0029] Specifically, a high-impedance input amplifier can be used to collect the electrical pulse feedback signal to reduce signal attenuation. At the same time, the collected signal can be subjected to feature extraction, such as calculating peak amplitude, rise time, duration, and spectral characteristics, etc., to provide richer diagnostic information.

[0030] In a possible implementation, to further improve the stability of signal collection, the signal collection module can adopt an active servo circuit to adjust the contact impedance between the electrode and the skin in real time, so that the quality of signal collection remains in an optimal state. For example, an automatic gain control (AGC) algorithm based on negative feedback can be used to keep the signal amplitude within a suitable dynamic range all the time.

[0031] In some embodiments, the signal collection module can also work in cooperation with the treatment feedback module to achieve closed-loop control. Specifically, during the treatment process, the system can monitor the impedance change of the tissue in real time and adjust the output of the radio frequency energy to optimize the treatment effect. For example, if an increase in tissue impedance is detected, it may indicate an increase in tissue temperature or a decrease in moisture. At this time, the radio frequency energy can be appropriately reduced to prevent tissue damage.

[0032] The nerve localization module is connected to the signal collection module. According to the data collected by the signal collection module, it determines the position of the target nerve and generates nerve localization data. As one of the core components of the system, the nerve localization module works closely with the signal collection module. Its main function is to analyze and determine the position of the target nerve around the knee joint based on the collected multi-channel physiological signals and generate nerve localization data for electrode adjustment and energy control. Generally, the accuracy of nerve localization directly determines the effectiveness of subsequent treatment. Therefore, this module needs to have high-precision computing capabilities and be able to adapt to the individual anatomical differences of different patients. To achieve this goal, the present invention combines an anatomical model database, a dynamic calibration unit, and a nerve signal matching algorithm to achieve precise identification of the target nerve based on multi-channel signal collection.

[0033] In this embodiment, the nerve localization module uses the standard nerve structure information provided by the anatomical model database as a preliminary reference and combines the real-time data transmitted by the signal collection module for multi-level comparison and analysis. The anatomical model database stores a large amount of knee joint nerve anatomical features based on clinical data, including nerve distribution areas, nerve diameters, relative positions of nerves and adjacent tissues, conductivity parameters, and impedance characteristics, etc. In specific applications, the nerve localization module first extracts the anatomical model that matches the patient's basic information from the database and makes a preliminary nerve region division accordingly.

[0034] In some embodiments, the dynamic calibration unit of the nerve localization module is used to adjust the information in the anatomical model database according to the specific physiological parameters of an individual patient. Specifically, this unit can correct the anatomical model based on the physiological signal characteristics of the patient's knee joint, such as the measured basic tissue impedance, nerve excitation threshold, tissue conductivity distribution, etc., to improve the matching accuracy. For example, during the impedance measurement process, the following calculation formula can be used to characterize the conductive properties of different tissues: ; where, is the tissue impedance, is the DC resistance of the tissue, is the inductive reactance, is the signal angular frequency, is the dielectric relaxation time constant of the tissue, is the imaginary unit.

[0035] As an option, the dynamic calibration unit can combine the patient's historical treatment data, compare the current signal with the previous data, and perform adaptive correction. This model optimization method based on personalized data can effectively improve the accuracy of nerve localization and reduce the influence brought by individual differences.

[0036] In a possible implementation, the nerve signal matching algorithm of the nerve localization module uses a multi-parameter analysis method to extract the characteristics of the collected impedance data and electrical pulse feedback signals, and compare them with the standard signals in the database to determine the position of the target nerve. Specifically, this algorithm can be based on time-domain, frequency-domain, and time-frequency-domain analyses to extract parameters such as nerve discharge frequency, action potential waveform characteristics, impedance change rate, etc., and calculate the similarity with the standard data. The similarity calculation can use the following formula: ; where, is the deviation degree of nerve matching, is the measured signal characteristic value, is the standard signal characteristic value in the database, is the weight factor, is the number of characteristic parameters.

[0037] Generally, after the matching algorithm finishes the calculation, it will obtain an optimal position prediction value of the target nerve and send this data to the electrode adjustment module to guide the position adjustment of the surface-mounted electrodes. In some embodiments, the nerve localization module can also generate a nerve localization confidence interval, that is, give the possible position range of the target nerve, and continuously optimize the localization accuracy through a subsequent dynamic feedback mechanism.

[0038] In another possible implementation, to improve the robustness of nerve localization, the nerve localization module can combine the multiple repeated measurement method, that is, at different time points and under various electrical stimulation intensities, the nerve signals are repeatedly collected, and statistical methods are used for comprehensive calculation. For example, the weighted average method can be used to calculate the final nerve position: ; where is the finally determined nerve position, is the position value obtained from the th measurement, is the confidence weight of this measurement, is the number of measurements.

[0039] In some embodiments, the nerve localization module can also interact with the treatment feedback module to adjust the nerve localization result according to the real-time feedback during the treatment process. For example, if the system detects abnormal nerve responses after electrode stimulation, it may indicate that the nerve position is not accurately matched. At this time, the repositioning mechanism can be automatically triggered to recalculate the position of the target nerve and guide the electrode adjustment.

[0040] As an option, the nerve localization module can use machine learning algorithms to optimize the nerve localization accuracy. By training models based on artificial neural networks (ANN), support vector machines (SVM), or random forests (RF), this module can adaptively adjust the nerve matching parameters after multiple measurements to improve the calculation efficiency and accuracy. For example, an ANN model can be used for nerve position prediction, and its mathematical expression is as follows: ; where is the nerve localization result, is the input signal feature vector, is the weight matrix, is the bias term, is the activation function.

[0041] The electrode adjustment module, connected to the nerve localization module, is used to adjust the position of the electrode according to the localization data provided by the nerve localization module; Please refer to Appendix Figure 9 - Appendix Figure 14 , the energy control module, respectively connected to the nerve localization module and the electrode adjustment module, controls the energy output of the radio frequency generator according to the nerve localization and electrode position data; The energy control module realizes the precise transmission and adjustment of radio frequency energy through the coordinated work with the surface-mounted electrode 1, the moving positioning hole 2, the motor surface layer 3, the electrical layer 4, and the adhesion positioning layer 5.

[0042] Surface-mounted electrode 1: The energy control module is connected to the electrical layer 4 of the surface-mounted electrode. By adjusting the current and voltage on the electrode, the transmission of radiofrequency energy is made more accurate and effective, ensuring effective coupling between the electrode and the target nerve; Moving positioning hole 2: By adjusting the position of the moving positioning hole 2 in real time, the energy control module can accurately control the contact point of the electrode, thus ensuring that the radiofrequency energy is focused on the target nerve area and avoiding energy leakage or over-concentration; Motor surface layer 3: The function of the motor surface layer 3 is to ensure precise control of the electrode position through mechanical adjustment. The interaction between the energy control module and the motor surface layer 3 enables the radiofrequency energy to be automatically adjusted to the optimal power output to cope with any minor changes in the electrode position during the treatment process.

[0043] Electrical layer 4: The electrical layer 4 provides electrical contact between the electrode and the tissue. The energy control module controls the distribution of radiofrequency energy by adjusting the current intensity in the electrical layer 4, thus ensuring the best match between the electrode and the target nerve; Adhesion positioning layer 5: The adhesion positioning layer 5 is responsible for ensuring the fixation and stability of the electrode on the skin surface, avoiding the displacement of the electrode position during the treatment process. The energy control module adjusts the adhesion positioning layer 5 to keep the electrode in the best contact position at all times to achieve the best radiofrequency treatment effect; Lateral superior geniculate nerve 6: The lateral superior geniculate nerve 6 is responsible for transmitting sensory information in the knee joint area, such as pain, temperature, and proprioception; during radiofrequency treatment, accurately locating this nerve is crucial to ensure that the radiofrequency energy can act precisely on this nerve and relieve the pain caused by knee joint inflammation or degeneration; the energy control module adjusts the output of the radiofrequency energy to avoid over-concentration or leakage of energy, ensuring that the energy is transmitted to this nerve, helping to relieve pain and promote tissue repair.

[0044] Medial superior geniculate nerve 7: The medial superior geniculate nerve 7 is involved in the sensory and motor functions of the knee joint and affects the stability and movement of the knee joint. By precisely applying radiofrequency energy to this nerve, symptoms such as knee joint stiffness and limited mobility caused by nerve dysfunction can be effectively relieved. The goal of radiofrequency treatment is to reduce pain or dysfunction caused by overactivation of the nerve by adjusting the excitability of the nerve. The energy control system continuously adjusts the radiofrequency power during the treatment process to ensure that the energy acts precisely on the target nerve.

[0045] Inferomedial geniculate nerve 8: Inferomedial geniculate nerve 8 is closely related to the sensory transmission and reflex activities of the knee joint; by performing radiofrequency treatment on this nerve, chronic pain caused by nerve inflammation or injury can be alleviated, and the motor function of the knee joint can be improved. During the treatment process, the energy control module precisely adjusts the output of radiofrequency energy according to the real-time data provided by the nerve localization module, ensuring the maximization of the treatment effect while avoiding thermal damage to other tissues.

[0046] The energy control module is a key component in the radiofrequency treatment process. Its main function is to dynamically adjust the energy output of the radiofrequency generator based on the target nerve position and electrode arrangement data provided by the nerve localization module and the electrode adjustment module, ensuring that the radiofrequency signal can precisely act on the target nerve area. Generally, the control of energy requires comprehensive consideration of the impedance characteristics of nerve tissue, the transmission efficiency of radiofrequency energy, the treatment time, and safety parameters, thereby achieving refined energy management. The energy control module of the present invention adopts an energy calculation unit, an energy adjustment unit, and a safety protection unit, ensuring that the risk of thermal damage to tissues is minimized while meeting the treatment requirements.

[0047] In this embodiment, the energy calculation unit of the energy control module is used to determine the optimal radiofrequency energy parameters. Generally, the effectiveness of radiofrequency treatment depends on the relative position between the electrode and the target nerve, the impedance characteristics of the tissue, and the energy distribution pattern. To optimize the setting of radiofrequency energy, the energy calculation unit can calculate the radiofrequency power using the following formula: ; where is the radiofrequency power, is the measured response current, is the impedance of the target tissue. The impedance is calculated from the data provided by the nerve localization module and can be expressed by the following formula: ; where is the applied alternating voltage, is the measured response current, is the impedance of the target tissue.

[0048] As an option, the energy calculation unit can further optimize the energy transmission strategy according to the heat capacity and thermal conductivity characteristics of the tissue to prevent the tissue temperature from rising excessively. The change in tissue temperature can be represented by the following heat conduction equation: ; where is the tissue temperature, is the time, is the thermal diffusivity of the tissue, is the radio frequency heating power density, is the tissue density, is the specific heat capacity, is the Laplacian operator of temperature.

[0049] In a possible implementation, the energy calculation unit dynamically calculates the radio frequency dose of the treatment area and adjusts the radio frequency according to the physiological state of the target nerve. The radio frequency optimization can be determined by the following formula: ; wherein, is the inductance in the circuit, is the capacitance. This calculation ensures that the radio frequency signal is within the optimal biological effect range, is the frequency of the circuit, representing the oscillation frequency of the circuit.

[0050] In some embodiments, the energy adjustment unit of the energy control module is used to dynamically control the power output, signal frequency and action time of the radio frequency generator. Specifically, when the position of the target nerve provided by the nerve localization module deviates, or the electrode adjustment module feeds back the change of the electrode position, the energy adjustment unit automatically adjusts the radio frequency signal to match the latest treatment requirements. For example, if it is detected that the distance between the electrode and the nerve increases, the system can automatically increase the radio frequency power to ensure that sufficient energy acts on the nerve tissue.

[0051] In another possible implementation, the energy adjustment unit adopts an adaptive adjustment strategy and adjusts the radio frequency energy output according to the physiological signal feedback during the treatment process. For example, when the tissue temperature gradually rises, the system can prevent overheating by reducing the radio frequency power or shortening the pulse width. The pulse width calculation can be represented by the following formula: ; wherein, is the frequency of the radio frequency modulation signal, is the dielectric relaxation time constant of the tissue.

[0052] As an option, the energy adjustment unit can adopt a closed-loop control strategy and automatically adjust the radio frequency parameters by real-time monitoring the impedance change of the tissue. For example, if it is detected that the tissue impedance suddenly rises during the treatment, which may mean tissue dehydration or overheating, the system can trigger the automatic power reduction mode to protect the tissue from damage.

[0053] In this embodiment, the safety protection unit is used to monitor the safety of the radio frequency energy output and take protective measures when an abnormal situation is detected. Generally, the safety protection unit can monitor the following parameters in real time: The change trend of tissue impedance; The temperature of the target tissue; Contact state between the electrode and the tissue; Physiological signal feedback during the treatment, such as muscle response and pain threshold changes.

[0054] In some embodiments, the safety protection unit can determine whether to reduce the radio frequency energy based on tissue temperature and impedance data. For example, when the system detects that the tissue temperature reaches the set threshold the energy output is automatically reduced: ; wherein, is the adjusted radio frequency power, is the original set power, is the real-time tissue temperature, is the target temperature, is the maximum temperature, representing the maximum safe temperature during the treatment.

[0055] As an option, the safety protection unit can provide an anomaly detection mechanism. When it detects that the tissue impedance exceeds the normal range or the energy output is abnormal, it automatically interrupts the treatment and triggers a warning. For example, when the tissue impedance change exceeds the preset range the system can trigger the following protection measures: ; wherein, is the resistance change amount, representing the difference between the current resistance and the reference resistance, is the current resistance value, representing the resistance of the current tissue, is the reference resistance value, representing the resistance of the tissue in the initial or normal state.

[0056] If > automatic stop the radio frequency output, and prompt the user to check the electrode position or tissue status.

[0057] In another possible implementation, the safety protection unit can combine intelligent data analysis, learn the treatment data patterns of different patients, and optimize the radio frequency energy output strategy accordingly. For example, the system can store multiple treatment history data and predict the optimal energy parameters through a machine learning model, thereby further improving the personalized treatment effect.

[0058] The treatment feedback module is respectively connected to the energy control module and the electrode adjustment module, and is used to monitor the physiological feedback signals during the treatment in real time, and adjust the electrode position and energy output according to the feedback to form a closed-loop control; The treatment feedback module undertakes the functions of real-time monitoring, data analysis, and adaptive adjustment throughout the treatment process. Its main role is to monitor physiological feedback data based on the signals provided by the electrode adjustment module and the energy control module, and optimize the adjustment of the electrode position and radiofrequency energy during the treatment process. Generally, the treatment feedback module needs to be able to identify key parameters such as tissue impedance changes, electromyogram (EMG) fluctuations, nerve response delays, tissue temperature gradients, etc., and form a closed-loop control accordingly to ensure the safety and accuracy of the treatment. The treatment feedback module of the present invention includes a physiological signal acquisition unit, a feedback analysis unit, and an adaptive adjustment unit, which can perform multi-level regulation during the treatment process to ensure the stability and controllability of radiofrequency treatment.

[0059] In this embodiment, the physiological signal acquisition unit of the treatment feedback module is responsible for real-time monitoring of the physiological feedback signals in the target area, including impedance changes, action potential characteristics, local tissue temperature, and nerve discharges after radiofrequency stimulation, etc. Generally, the physiological signal acquisition unit needs to have high time resolution and high spatial resolution to ensure the accuracy of the feedback data. For example, during the radiofrequency action process, the physiological signal acquisition unit can measure the dynamic impedance changes in the following ways: ; where represents the impedance change amount, is the latest measured tissue impedance, is the initial impedance baseline value.

[0060] As an option, the physiological signal acquisition unit can adopt multi-frequency impedance measurement technology. By applying alternating current signals of different frequencies, the complex impedance characteristics of the tissue can be obtained, and the impedance spectrum curve can be calculated. Specifically, the impedance characteristics at different frequencies can be expressed as: ; where is the complex impedance, is the real part (resistance component), is the imaginary part (reactance component).

[0061] In a possible implementation manner, the feedback analysis unit of the treatment feedback module is used to analyze the tissue state based on the collected physiological signal data, and determine whether it is necessary to adjust the electrode position or radiofrequency energy. Specifically, the feedback analysis unit can adopt time series analysis methods to calculate the dynamic trend of nerve responses. For example, during the radiofrequency stimulation process, if it is detected that the peak amplitude of the nerve action potential decreases, it can indicate a decrease in nerve excitability, and the system can automatically increase the radiofrequency power or adjust the electrode position to optimize the treatment effect.

[0062] In some embodiments, the feedback analysis unit uses non-linear data modeling to calculate the dynamic change trend of neural responses. For example, a polynomial fitting model is used to estimate the change trend of neural responses: ; where, represents the amplitude of the neural action potential, is the time, , , ,…, are the fitting parameters. Through this method, the system can predict the future trend of the neural state and adjust the radio frequency parameters in advance, is the highest power of the polynomial, representing the fitting order of the neural signal.

[0063] In this embodiment, the adaptive adjustment unit of the treatment feedback module is used to adjust the electrode position and radio frequency energy output when an abnormal physiological signal is detected. Generally, when the feedback analysis unit detects that the impedance change exceeds the normal range or the neural response signal is abnormal, the adaptive adjustment unit will send an instruction to the electrode adjustment module to adjust the electrode position, or send an instruction to the energy control module to change the radio frequency power and modulation parameters. For example, in the case where the distance between the electrode and the target nerve increases, the radio frequency power can be optimized in the following way: ; where, is the adjusted radio frequency power, is the original set power, is the electrode offset, is the set optimal electrode-nerve distance.

[0064] In another possible implementation, the adaptive adjustment unit can combine a feedback control algorithm to achieve closed-loop adjustment. For example, a fuzzy control method can be used to divide the physiological signal feedback data into different state levels and set corresponding adjustment strategies. Specifically, fuzzy control rules can be set according to the impedance change rate, neural response amplitude, and tissue temperature change: If the impedance change is small and the neural response is stable, the current radio frequency power is maintained.

[0065] If the impedance change is large but the neural response is still within the normal range, the radio frequency power is appropriately reduced.

[0066] If the neural response weakens and the impedance rises, the electrode position is adjusted and the radio frequency power is reduced.

[0067] If the tissue temperature exceeds the set threshold, the radio frequency output is stopped and a warning is issued.

[0068] As an option, the adaptive adjustment unit can combine historical data analysis and optimize electrode adjustment and energy control strategies through machine learning algorithms. For example, support vector machines (SVM) can be used to classify different treatment states and automatically recommend the best parameter adjustment strategies. The decision function of the support vector machine can be expressed as: ; where is the classification function, is the weight vector, is the input physiological signal feature vector, is the bias term. This method can improve the intelligent adjustment ability of the system and make the treatment feedback more accurate.

[0069] In some embodiments, the treatment feedback module can also be linked with a safety protection mechanism to immediately abort radiofrequency treatment when a high-risk signal is detected. For example, if it is detected that the tissue temperature exceeds the set threshold , the system will automatically stop the radiofrequency output and issue an alarm signal to avoid tissue thermal damage.

[0070] The treatment evaluation module, which is connected to the treatment feedback module, is used to generate a treatment report after the treatment, evaluate the treatment effect, and provide suggestions on whether to continue the treatment and optimization; The treatment evaluation module is used to comprehensively evaluate the treatment process after the treatment and generate a detailed treatment report. This module can not only evaluate the treatment effect, but also give suggestions on whether to continue the treatment and how to optimize the treatment plan based on the physiological signal data provided by the treatment feedback module. Generally, the treatment evaluation module deeply analyzes key parameters such as physiological feedback data, nerve responses, tissue temperature changes, and impedance characteristics during the treatment process to evaluate the effectiveness and safety of the treatment. On this basis, the evaluation module can provide personalized treatment optimization suggestions to help doctors decide whether to continue the treatment and adjust the treatment plan. The core components of the treatment evaluation module include an effect evaluation unit, a report generation unit, and a suggestion generation unit, and through a close connection with the treatment feedback module, it ensures the real-time and accuracy of the evaluation process.

[0071] In this embodiment, the effect evaluation unit of the treatment evaluation module analyzes various physiological signals during the treatment process based on the feedback data provided by the treatment feedback module to evaluate the treatment effect. For example, the effect evaluation unit calculates a comprehensive score of the treatment effect based on the improvement of nerve responses, changes in tissue temperature, and stability of impedance. Generally, the evaluation of the treatment effect depends on the combination of multiple indicators, and the weighted average method can be used in the evaluation process to generate the final treatment effect score based on the contribution degree of each physiological signal. For example, the treatment effect can be evaluated according to the following criteria: Degree of improvement in nerve response: Evaluate the degree of nerve recovery based on data such as the change amplitude of action potentials and nerve conduction velocity.

[0072] Stability of tissue temperature: Analyze the risk of overheating or insufficient cooling based on the temperature change during the treatment process.

[0073] Smoothness of impedance change: Significant fluctuations in impedance may indicate poor contact between the electrode and the tissue or abnormalities during the treatment process.

[0074] As an option, the effect evaluation unit can also adopt a multi-dimensional data fusion algorithm to weight and fuse feedback signals from different sources, so as to more accurately evaluate the treatment effect. For example, principal component analysis (PCA) can be used to extract the principal components in physiological signals, reduce redundant data, and improve the evaluation efficiency.

[0075] Specifically, the scoring formula for the treatment effect can be calculated by weighting different physiological signals measured during the treatment process. For example, if the contribution weights of nerve response, tissue temperature, and impedance are , , , then the comprehensive effect score can be expressed as: ; where , , represent the scores of nerve response, tissue temperature, and impedance respectively, , , are the respective weight coefficients, is the comprehensive evaluation score, representing the comprehensive score of the treatment effect.

[0076] In a possible implementation, the report generation unit of the treatment evaluation module generates a detailed treatment report based on the treatment score provided by the effect evaluation unit. The report contains key data of the treatment process, evaluation results, and visualization charts to help doctors understand the progress and effect of the treatment. For example, the report can show the nerve response map before and after treatment, the tissue temperature change curve, and the impedance change trend chart. Doctors can judge whether the treatment has achieved the expected effect based on these data. The report will also give suggestions on whether to continue the treatment according to the evaluation results and provide further optimization plans.

[0077] As an option, the report generation unit can generate personalized treatment recommendations based on historical treatment data, recommending the best treatment plan and possible optimization measures. For example, when the treatment feedback module detects abnormal tissue temperature, the report will alert the doctor to the risk of excessive temperature and recommend adjusting the radiofrequency power or electrode position. In addition, the report can also give recovery period suggestions after treatment and subsequent monitoring plans.

[0078] In this embodiment, the recommendation generation unit of the treatment evaluation module proposes a recommendation on whether to continue the treatment based on the treatment effect score of the effect evaluation unit. Specifically, the recommendation generation unit determines whether the treatment has achieved the expected effect according to the comprehensive score and treatment progress. If the treatment effect is poor, the recommendation generation unit may recommend extending the treatment time, adjusting the treatment intensity, or changing the electrode position. If the evaluation result shows that the treatment effect is good, the recommendation generation unit can recommend ending the treatment and suggest continuing to observe the patient's recovery.

[0079] As an option, the recommendation generation unit can also give targeted optimization suggestions based on the feedback signals during the treatment process. For example, if it is detected that the nerve response has decreased, the recommendation generation unit can recommend increasing the radiofrequency power, or adjusting the optimal position of the electrode relative to the target nerve according to the change in the electrode position. In addition, the recommendation generation unit can also combine the patient's historical treatment data to propose a personalized treatment adjustment plan.

[0080] In some embodiments, the treatment evaluation module can combine machine learning algorithms to optimize the treatment effect evaluation and recommendation generation process through learning and modeling of historical treatment data. For example, support vector machine (SVM) is used to classify the treatment effect, and more accurate optimization suggestions are generated based on the patient's response and treatment history records. Through the machine learning model, the treatment evaluation module can automatically adjust the evaluation criteria and recommendation content according to the different characteristics of each patient.

[0081] The method for auxiliary positioning patch electrodes for knee joint nerve treatment described below can be referred to in correspondence with the system for auxiliary positioning patch electrodes for knee joint nerve treatment described above.

[0082] Please refer to the appendix Figure 8 , the present invention also provides a method for auxiliary positioning patch electrodes for knee joint nerve treatment, including the following steps: S1. System initialization: Start the knee joint nerve treatment system, install the patch electrodes, and set the energy control module, signal acquisition module, nerve positioning module, treatment feedback module, and electrode adjustment module to prepare for starting the treatment; S2. Signal Acquisition: The multi-channel electrode array in the signal acquisition module is used to simultaneously acquire the physiological signals around the knee joint, including impedance data and electrical pulse feedback signals, and perform filtering, denoising, and enhancement processing through the signal preprocessing unit; S3. Nerve Localization: According to the data provided by the signal acquisition module, the nerve localization module analyzes and compares the standard nerve positions in the anatomical model database, and combines the signal characteristics of the patient's individual to dynamically calibrate and determine the position of the target nerve; S4. Electrode Adjustment: According to the positioning data provided by the nerve localization module, the electrode adjustment module adjusts the position of the surface-mounted electrodes, and ensures that the electrodes are accurately aligned with the target nerve through the electrode support unit and the positioning fine-tuning unit; S5. Energy Control: According to the nerve localization data and the electrode position data, the energy control module calculates the appropriate radio frequency energy parameters, and controls the output power, frequency, and duration of the radio frequency generator; S6. Treatment Process Monitoring and Feedback Adjustment: During the treatment process, the treatment feedback module monitors the physiological responses of the target nerve in real time, obtains muscle response and temperature change information using the physiological signal monitoring unit, and adjusts the electrode position and radio frequency energy output according to the feedback data; S7. Treatment Evaluation and Report Generation: After the treatment is completed, the treatment evaluation module analyzes the treatment feedback data, generates a personalized treatment report, evaluates the treatment effect of the patient, and provides suggestions on whether to continue the treatment and optimization; S8. Treatment Optimization and End: According to the analysis results of the treatment effect, it is judged whether it is necessary to adjust the treatment plan and continue the treatment.

[0083] The method of this embodiment can be used to implement the above system embodiment, and its principle and technical effects are similar, so they will not be elaborated here.

[0084] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A surface-mount electrode system for auxiliary positioning of knee joint nerve treatment, characterized in that: include: A signal acquisition module, used for real-time acquisition of multi-channel signals around the knee joint, wherein the multi-channel signals include impedance data and electrical pulse feedback signals; The nerve localization module is connected to the signal acquisition module, determines the location of the target nerve according to the data collected by the signal acquisition module, and generates nerve localization data; The electrode adjustment module is connected to the nerve positioning module and is used to adjust the position of the electrode according to the positioning data provided by the nerve positioning module; The energy control module is connected to the nerve positioning module and the electrode adjustment module respectively, and controls the energy output of the radio frequency generator according to the nerve positioning and electrode position data; The treatment feedback module is connected to the energy control module and the electrode adjustment module respectively, and is used to monitor the physiological feedback signal during the treatment process in real time, and adjust the electrode position and energy output according to the feedback to form a closed-loop control; The treatment evaluation module is connected to the treatment feedback module and is used to generate a treatment report after the treatment is completed, evaluate the treatment effect, and provide suggestions on whether to continue treatment and optimization.

2. The knee joint nerve treatment auxiliary positioning surface electrode system according to claim 1, characterized in that: The signal acquisition module comprises: Multi-channel electrode array, used to detect physiological signals from multiple neural areas simultaneously, ensuring the comprehensiveness and accuracy of the signals; A signal preprocessing unit, used for filtering, denoising and signal enhancement processing of the collected signals; The data transmission interface is used to transmit the processed signal data to the nerve localization module for further analysis and calculation.

3. The surface-mounted electrode system for auxiliary positioning of knee joint nerve treatment according to claim 1, characterized in that: The neural localization module comprises: Anatomical model database, which stores standard anatomical structure information of nerves around the knee joint to provide positioning reference; Dynamic calibration unit, used to dynamically adjust the anatomical model parameters according to the signal characteristics of individual patients to make the nerve localization results more accurate; The neural signal matching algorithm is used to analyze the data provided by the signal acquisition module and compare it with the information in the anatomical model database to accurately locate the target nerve.

4. The knee joint nerve treatment auxiliary positioning surface electrode system according to claim 1, characterized in that: The electrode adjustment module comprises: The electrode support unit is used to fix the surface-mount electrode and can be adjusted according to the patient's anatomical characteristics; Positioning fine-tuning unit, used to accurately adjust the position of the electrode after nerve positioning is completed; The feedback response unit is connected to the treatment feedback module and dynamically adjusts the electrode position according to the treatment feedback information.

5. The knee joint nerve treatment auxiliary positioning surface electrode system according to claim 1, characterized in that: The energy control module comprises: An energy calculation unit calculates appropriate radiofrequency energy parameters based on nerve positioning data, target nerve characteristics and tissue impedance; Energy regulation unit, used to dynamically control the output power, action time and frequency of radio frequency energy to meet individual treatment needs; The safety protection unit is used to monitor the output of RF energy in real time and automatically adjust or terminate the energy output under abnormal circumstances.

6. The knee joint nerve treatment auxiliary positioning surface electrode system according to claim 1, characterized in that: The treatment feedback module includes: A physiological signal monitoring unit, used to monitor in real time the physiological response of nerves during treatment, the physiological response including muscle contraction, temperature change and impedance change; Adaptive regulation unit, based on the monitored feedback data, automatically adjusts the energy output parameters to optimize the treatment effect; Intelligent learning unit, used to record feedback data from multiple treatment processes and optimize subsequent treatment plans through machine learning methods.

7. The knee joint nerve treatment auxiliary positioning surface electrode system according to claim 1, characterized in that: The treatment assessment module includes: A treatment effect analysis unit, used to evaluate the patient's response to radiofrequency treatment based on treatment feedback data; Personalized report generation unit, used to generate personalized treatment reports, provide efficacy analysis, and make optimization suggestions for subsequent treatment plans; The clinical database comparison unit is used to compare patient treatment data with historical data in the clinical database to evaluate treatment success and adjust future treatment strategies.

8. The surface-mounted electrode system for auxiliary positioning of knee joint nerve treatment according to claim 4, characterized in that: The feedback response unit comprises: A signal analysis unit, which is used to receive real-time physiological signals provided by the treatment feedback module and analyze muscle reactions, electrophysiological changes and local temperature fluctuations to evaluate nerve response; A position calibration unit adjusts the angle and contact pressure of the electrode support unit based on the analyzed feedback data to optimize the electrode fit and nerve action area; The dynamic adaptation unit uses the patient's individual anatomical characteristics and real-time treatment feedback data to adaptively adjust the electrode micro-displacement so that the treatment area always remains in the optimal target position.

9. The surface-mounted electrode system for auxiliary positioning of knee joint nerve treatment according to claim 5, characterized in that: The energy calculation unit comprises: A biological characteristic analysis unit is used to collect tissue impedance, blood flow characteristics and physiological response data of the target nerve to provide a basis for personalized energy calculation; The dose control unit calculates the appropriate RF energy intensity based on the analysis results and adjusts the pulse width and frequency to match the neurological treatment needs; The real-time correction unit uses feedback information obtained during the treatment process to dynamically fine-tune the energy output.

10. A surface-mounted electrode method for auxiliary positioning of a knee joint nerve treatment, according to the surface-mounted electrode system for auxiliary positioning of a knee joint nerve treatment according to any one of claims 1 to 9, characterized in that: The following steps are involved: System initialization: Start the knee nerve treatment system, install the surface-mount electrodes, and set up the energy control module, signal acquisition module, nerve positioning module, treatment feedback module and electrode adjustment module to prepare for treatment; Signal acquisition: The multi-channel electrode array in the signal acquisition module is used to simultaneously collect physiological signals around the knee joint, including impedance data and electrical pulse feedback signals, and the signal preprocessing unit performs filtering, denoising and enhancement processing; Nerve localization: Based on the data provided by the signal acquisition module, the nerve localization module analyzes and compares the standard nerve positions in the anatomical model database, combines the patient's individual signal characteristics, and dynamically calibrates and determines the position of the target nerve; Electrode adjustment: Based on the positioning data provided by the nerve positioning module, the electrode adjustment module adjusts the position of the surface-mounted electrode and ensures that the electrode is accurately aligned with the target nerve through the electrode support unit and the positioning fine-tuning unit; Energy control: Based on the nerve positioning data and electrode position data, the energy control module calculates the appropriate RF energy parameters and controls the output power, frequency and duration of the RF generator; Treatment process monitoring and feedback adjustment: During the treatment process, the treatment feedback module monitors the physiological response of the target nerve in real time, uses the physiological signal monitoring unit to obtain muscle response and temperature change information, and adjusts the electrode position and radiofrequency energy output according to the feedback data; Treatment evaluation and report generation: After the treatment is completed, the treatment evaluation module analyzes the treatment feedback data and generates a personalized treatment report to evaluate the patient's treatment effect and provide suggestions on whether to continue treatment and optimization; Treatment optimization and termination: Based on the results of the treatment effect analysis, determine whether the treatment plan needs to be adjusted and whether treatment needs to continue.

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