Medical integrated module type electroacupuncture anesthesia therapeutic apparatus

Abstract

The invention discloses a medical integrated module type electroacupuncture anesthesia therapeutic apparatus, and belongs to the technical field of medical apparatuses and instruments. A medical integrated module type electroacupuncture anesthesia therapeutic apparatus comprises a signal collecting and monitoring module, a main control module, a parameter self-adaptive adjusting module, a stimulation output module and a safety monitoring unit. The problems that existing current output parameters depend on manual adjustment and doctor experience, an objective quantitative basis is lacked, a real-time physiological monitoring and feedback mechanism is lacked in the treatment process, and current adjustment lags behind the state change of a patient are solved. Through a multi-parameter fusion decision algorithm and a closed-loop real-time feedback mechanism, 'experience-driven open-loop control 'of traditional electro-acupuncture equipment is upgraded to'data-driven closed-loop control', the subjectivity and hysteresis problems of manual adjustment are solved, and treatment safety and individual adaptability are greatly improved through high-precision monitoring and quick response.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a medical integrated modular electro-acupuncture anesthesia therapeutic apparatus. Background Art

[0002] As an important means of combining traditional Chinese medicine with modern electrical stimulation therapy, electroacupuncture anesthesia technology has unique advantages in preoperative anesthesia, postoperative analgesia, and the treatment of chronic diseases. However, existing electroacupuncture anesthesia equipment has significant technical bottlenecks in clinical application, mainly reflected in the following two aspects:

[0003] 1. Current output parameters rely on manual adjustment and physician experience, lacking objective quantitative basis;

[0004] Traditional electroacupuncture anesthesia treatment devices usually use a fixed or semi-fixed current output mode, and their parameters (such as intensity, frequency, waveform, etc.) need to be manually adjusted by the doctor based on the patient's complaints and his own experience. Due to the lack of real-time quantitative assessment of the patient's physiological state, the setting of current parameters is highly subjective and the adjustment accuracy is low. Different patients have significant differences in sensitivity to electric current stimulation, and traditional equipment cannot dynamically adjust parameters according to the patient's real-time response. For example, in a postoperative analgesia scenario, the patient's pain level may change dynamically with the recovery stage, but traditional equipment still uses fixed parameter output, resulting in about 30% of cases requiring multiple manual intervention adjustments, and the analgesic effect fluctuates significantly.

[0005] Second, there is a lack of real-time physiological monitoring and feedback mechanisms during treatment, and current regulation lags behind changes in the patient's condition;

[0006] Existing equipment generally does not integrate multimodal physiological signal acquisition functions. Traditional equipment can only monitor basic current output parameters (such as voltage and current intensity), while ignoring key physiological indicators that are directly related to the anesthesia / analgesia effect. Due to the lack of real-time signal processing capabilities, the equipment cannot respond to sudden physiological changes in patients during treatment. Clinical data show that in about 12% of cases, traditional equipment requires manual intervention due to sudden abnormal heart rate or sudden change in local tissue impedance during surgery. The response delay is as long as 5-8 seconds, which may cause complications such as muscle rigidity or skin burns, seriously restricting the accuracy and safety of electroacupuncture anesthesia treatment. Summary of the Invention

[0007] The purpose of the present invention is to provide a medical integrated modular electroacupuncture anesthesia therapeutic instrument, which upgrades the "experience-driven open-loop control" of traditional electroacupuncture equipment to "data-driven closed-loop control" through a multi-parameter fusion decision-making algorithm and a closed-loop real-time feedback mechanism. It not only solves the subjectivity and hysteresis problems of manual adjustment, but also greatly improves the treatment safety and individual adaptability through high-precision monitoring and rapid response, providing core technical support for the standardization and intelligence of electroacupuncture anesthesia treatment, and solving the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A medical integrated modular electro-acupuncture anesthesia therapeutic instrument, comprising a signal acquisition and monitoring module, a main control module, a parameter adaptive adjustment module, a stimulation output module and a safety monitoring unit;

[0010] The signal acquisition and monitoring module is configured to collect myoelectric signals, electrocardiographic signals and skin impedance signals in real time through detection acupuncture needles and wires, and transmit the signals to the main control module;

[0011] The main control module is configured to receive electromyographic signals, electrocardiographic signals and skin impedance signals, analyze and process the signals according to the set current usage mode, and use a built-in neural network evaluation model to perform multi-parameter fusion decision calculation and analysis on the electromyographic signals, electrocardiographic signals and skin impedance signals;

[0012] Among them, the current usage modes include preoperative anesthesia mode, postoperative analgesia mode and disease treatment mode;

[0013] The parameter adaptive adjustment module dynamically adjusts the current stimulation strategy based on a multi-parameter fusion decision algorithm, and adaptively adjusts the intensity, frequency, waveform and pulse width of the output current according to the patient's muscle, heartbeat and skin sweat gland activity;

[0014] The stimulation output module is configured to transmit the regulated current to the corresponding detection-type acupuncture needle through a wire, and continuously output the stimulation current to the patient's acupuncture point;

[0015] The safety monitoring unit is configured to monitor skin impedance mutation, leakage current and heart rate variability in real time and trigger an emergency stop mechanism.

[0016] Preferably, the detection-type acupuncture needle is further configured as follows:

[0017] The needle tip is configured to collect electromyographic signals from deep muscles and transmit stimulation current to acupuncture points. The outer wall of the needle tip is provided with a platinum black coating and a ring electrode array. The platinum black coating covers the 1.5mm-2mm area of ​​the needle tip.

[0018] The middle part of the needle body is configured to collect ECG and skin impedance signals. It is equipped with an Ag / AgCl composite electrode layer. The composite electrode layer is arranged around the middle area of ​​the needle body, with a height of 8mm and a thickness of 200nm. It is isolated from the needle tip by a polyimide insulation layer to prevent crosstalk between myoelectric and ECG signals.

[0019] A magnetic connection interface is provided on the upper part of the needle body to ensure the stability and anti-interference ability of signal transmission;

[0020] Preferably, the workflow of the preoperative anesthesia mode includes:

[0021] Calculate the root mean square value of the electromyographic signal to reflect muscle tension;

[0022] Calculate the standard deviation of ECG RR intervals to assess autonomic nervous system activity;

[0023] Calculate skin impedance value to characterize the intensity of stress response;

[0024] Calculate and fuse the three characteristic values ​​of electromyography, electrocardiography and skin impedance signals to form a comprehensive anesthesia index;

[0025] When the comprehensive anesthesia index is less than the anesthesia threshold of 0.65, it is judged as insufficient anesthesia; when the comprehensive anesthesia index is in the range of 0.65-0.85, it is judged as an ideal anesthesia state; when the comprehensive anesthesia index is greater than 0.85, it triggers an over-deep anesthesia warning;

[0026] The current parameter adjustment command is generated based on the deviation between the comprehensive anesthesia index and the anesthesia threshold. When anesthesia is insufficient, the current intensity is increased at a rate of 3mA / second, and the frequency is reduced to 1-2Hz to enhance the inhibitory effect. When anesthesia is too deep, the current intensity is reduced to 5mA and the pulse width is shortened to 0.1ms.

[0027] The electromyographic signal, electrocardiographic signal and skin impedance signal are updated every 5 seconds. When the comprehensive anesthesia index is stable in the range of 0.65-0.85 for 4 consecutive monitoring cycles, it automatically enters the maintenance state and the operation interface displays a green ready mark.

[0028] Preferably, the preoperative anesthesia mode automatically stops outputting the stimulation current when any of the following conditions is met:

[0029] The comprehensive anesthesia index is >0.85 for 80 consecutive seconds;

[0030] ECG detected ventricular premature beats > 5 times / minute;

[0031] Single electrode contact impedance mutation> 50%;

[0032] Preferably, the default baseline values ​​of the current parameters of the preoperative anesthesia mode are current intensity 15 mA, frequency 2 Hz, and pulse width 0.3 ms.

[0033] Preferably, the workflow of the postoperative analgesia mode is:

[0034] Load the patient's preoperative physiological baseline data;

[0035] Monitor the tension of muscle groups around the surgical incision based on electromyographic signals;

[0036] Analyze the slope of heart rate oscillation based on ECG signals to detect the autonomic nervous system response caused by pain;

[0037] Detect sweat gland secretion activity based on skin impedance signals;

[0038] The postoperative pain level is divided into four levels based on electromyographic signals, electrocardiographic signals and skin impedance signals, and the corresponding stimulation current output mode is automatically switched according to the pain level;

[0039] Among them, when the electromyographic amplitude is less than 45μV and the heart rate variability coefficient is greater than 17%, it is classified as mild pain. At this time, a 3Hz low-frequency square wave is continuously output to maintain basic analgesia;

[0040] When the myoelectricity is 45-145μV and the skin impedance decreases by more than 21%, it is classified as moderate pain, and 12Hz / 60Hz alternating sweep frequency stimulation is continuously output;

[0041] When the myoelectricity is greater than 145μV and the heart rate oscillation slope is less than 3.1ms / RR, it is classified as moderate to severe pain, at which time a 90Hz high-frequency pulse train is triggered;

[0042] When the electromyographic signal, electrocardiographic signal and skin impedance signal exceed the threshold at the same time, the output current stimulation intensity is increased to 25mA.

[0043] Preferably, the workflow of the disease treatment model includes:

[0044] Select the type of disease and symptoms currently being treated from the treatment information library, and perform electrical stimulation based on the patient's current treatment stage;

[0045] The treatment information database includes standard treatment plans for neurological diseases, locomotor system diseases, and visceral diseases, as well as the corresponding acupuncture points and acupuncture current stimulation parameter ranges for each disease.

[0046] Among them, during the acute stage of treatment, 50-100Hz high-frequency current short-term shock treatment is used;

[0047] During the remission phase of the treatment, 2Hz / 15Hz medium and low frequency current alternation treatment is used;

[0048] During the recovery phase of treatment, intermittent pulse therapy is used, with current output every 0.5 minutes and a single current output lasting 2 minutes.

[0049] Preferably, the safety monitoring unit triggers an emergency stop when any of the following situations is detected:

[0050] The skin impedance signal suddenly changes by more than 29% and lasts for 130ms;

[0051] Leakage current exceeds 90μA;

[0052] The standard deviation of heart rate variability exceeds a preset safety threshold.

[0053] Preferably, the parameter adaptive adjustment module further performs the following operations:

[0054] Obtain the patient's identity information, including age, gender, height, weight, place of origin, and parameter data of various parts of the body;

[0055] Analyzing the identity information based on a pre-configured patient analysis library to determine a first tolerance parameter;

[0056] Obtain historical medical data of patients;

[0057] Based on the data screening rules configured in the current working mode, historical medical data is screened to obtain relevant data;

[0058] determining a second tolerance parameter based on the relevant data and a pre-configured individual difference analysis library;

[0059] The first tolerance parameter and the second tolerance parameter are comprehensively considered to adjust various operating parameters of the current operating mode.

[0060] Preferably, the parameter adaptive adjustment module further performs the following operations: verifying the integrity of relevant data based on preset verification rules; determining missing items when the verification fails; predicting and filling missing items with associated data associated with the missing items in the historical medical data of the patient's related personnel;

[0061] The steps for predicting and filling missing items are as follows:

[0062] Based on the relationship between the relevant personnel and the patient, the first correlation coefficient is determined;

[0063] Determining a second correlation coefficient based on a reference comparison of the historical medical data of the relevant personnel and the medical data of the patient;

[0064] Determining weights of data corresponding to relevant personnel based on the first correlation coefficient and the second correlation coefficient;

[0065] The data corresponding to the missing item is determined based on the weight sum of the associated data of the missing item of each relevant person, the data of the related items of the missing item in the patient's historical data and the weight coefficient of the related items.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. The present invention constructs a comprehensive anesthesia index based on a neural network through real-time acquisition and fusion analysis of multimodal physiological signals (electromyography, electrocardiography, and skin impedance), providing an objective quantitative basis for current parameter adjustment. The parameter adaptive adjustment module can automatically match the stimulation strategy according to the patient's real-time physiological state, significantly improving the stability of the treatment effect, solving the subjective problem of current parameter adjustment, and realizing dynamic automatic control.

[0068] 2. The present invention uses a segmented design of detection-type acupuncture needles to synchronously collect deep myoelectricity, electrocardiogram and skin impedance, comprehensively covering key indicators such as muscle tension, autonomic nervous activity and stress response intensity, overcoming the defect of traditional equipment with a single monitoring dimension. The main control module generates adjustment instructions in real time based on physiological signals, forming a closed-loop control of "acquisition-analysis-output-reacquisition". The system response speed is greatly improved compared with manual operation. At the same time, the safety monitoring unit can capture sudden changes in skin impedance, leakage current and abnormal heart rate variability in real time, and trigger an emergency stop in a short time, reducing the incidence of intraoperative complications by more than 90%, effectively building a real-time monitoring-feedback closed loop, and improving treatment accuracy and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the module structure relationship of the present invention. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0071] The effects of existing electroacupuncture anesthesia treatments are difficult to precisely control, and there are safety risks. The lack of objective quantitative evidence and real-time feedback mechanisms makes it difficult to accurately adjust the treatment process based on individual patient differences and real-time conditions, affecting the effectiveness and safety of the treatment. At the same time, the inability to respond to sudden changes in the patient's physiological state in a timely manner increases the risk of complications during treatment. To address the above issues, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0072] A medical integrated modular electroacupuncture anesthesia therapeutic apparatus, comprising:

[0073] A signal acquisition and monitoring module is configured to acquire myoelectric signals, electrocardiographic signals, and skin impedance signals in real time through detection-type acupuncture needles and wires, and transmit the signals to a main control module;

[0074] The main control module is configured to receive electromyographic signals, electrocardiographic signals, and skin impedance signals, analyze and process the signals according to the set current usage mode, and use a built-in neural network evaluation model to perform multi-parameter fusion decision calculation and analysis on the electromyographic signals, electrocardiographic signals, and skin impedance signals;

[0075] Among them, the current usage modes include preoperative anesthesia mode, postoperative analgesia mode and disease treatment mode;

[0076] The main control module uses hierarchical isolation and transmission to receive electromyographic signals, electrocardiographic signals, and skin impedance signals. The electromyographic signal captures deep muscle electrical activity through the circular electrode array at the tip of the acupuncture needle and is transmitted to the signal conditioning circuit via a shielded wire. The electrocardiographic signal is acquired by the composite electrode layer in the middle of the needle body, and respiratory fluctuation interference is eliminated through an independent filtering channel. The skin impedance signal is measured using the alternating current excitation method to dynamically monitor changes in the electrical characteristics of the epidermis. After hardware-level synchronization and alignment, the three signals undergo adaptive noise reduction and feature enhancement processing to form a standardized data stream input.

[0077] The neural network evaluation model built into the main control module utilizes a multi-branch fusion architecture, comprising three feature extraction branches and a two-level decision network. The electromyography analysis branch extracts muscle activation features through time-domain convolution and frequency-domain wavelet transforms. The electrocardiography processing branch utilizes a timing network to capture heart rate variability. The impedance analysis branch focuses on tracking dynamic changes in galvanic skin response. The features extracted by each branch are spatially aligned and weighted using a cross-attention mechanism, followed by a fusion decision.

[0078] The parameter adaptive adjustment module dynamically adjusts the current stimulation strategy based on a multi-parameter fusion decision-making algorithm, and adaptively adjusts the output current intensity, frequency, waveform and pulse width according to the patient's muscle, heartbeat and skin sweat gland activity;

[0079] The stimulation output module is configured to transmit the regulated current to the corresponding detection acupuncture needle through the wire, and continuously output the stimulation current to the patient's acupuncture points;

[0080] Safety monitoring unit: monitors skin impedance mutation, leakage current and heart rate variability in real time, triggering the emergency stop mechanism.

[0081] Existing electroacupuncture anesthesia treatment equipment relies on the doctor's experience to adjust the current output parameters, lacks real-time quantitative assessment of the patient's physiological state, and has low adjustment accuracy. In addition, traditional equipment lacks multimodal physiological signal acquisition capabilities and cannot respond to sudden physiological changes in patients in a timely manner. This system solves this problem by combining a signal acquisition and monitoring module with a main control module. The signal acquisition and monitoring module can collect electromyographic signals, electrocardiographic signals, and skin impedance signals in real time and transmit these signals to the main control module. The main control module has a built-in neural network evaluation model that can perform multi-parameter fusion decision calculations and analysis on these signals, thereby achieving real-time quantitative assessment of the patient's physiological state.

[0082] In the implementation environment, the signal acquisition and monitoring module collects myoelectric signals, electrocardiographic signals and skin impedance signals in real time through detection acupuncture needles and wires.

[0083] The needle tip is configured to collect electromyographic signals from deep muscles and transmit the stimulation current to acupuncture points. It is equipped with a platinum black coating and a ring electrode array. The ring electrode array is distributed in concentric circles. The platinum black coating covers the 1.5mm-2mm area of ​​the needle tip, with a surface porosity of 82%. The impedance is ≤110Ω at a frequency of 1.3kHz, which enhances the capture of deep muscle signals; the ring electrode array improves spatial resolution and multi-point sampling.

[0084] To avoid mutual interference between signal acquisition and current stimulation, the system adopts "acquisition-stimulation-acquisition" cyclic timing control. During the signal acquisition stage, the electrode at the tip of the needle switches to collecting electromyographic signals, and the electrode in the middle of the needle body continues to collect electrocardiographic and skin impedance signals; during the current stimulation stage, the electrode at the tip of the needle switches to output customized current to the acupoint, and returns to the acquisition mode after the stimulation ends to ensure the continuity of signal acquisition.

[0085] The middle part of the needle body is configured to collect ECG and skin impedance signals. It is equipped with an Ag / AgCl composite electrode layer. The composite electrode layer covers an 8mm area in the middle of the needle body. The thickness is 200nm, with a thickness error of 40nm. It is isolated from the needle tip by a polyimide insulation layer to prevent crosstalk between myoelectric and ECG signals.

[0086] A magnetic connection interface is provided on the upper part of the needle body to ensure the stability and anti-interference ability of signal transmission;

[0087] The tip of the detection acupuncture needle can be coated with a platinum black coating on the outer wall of the tip using chemical vapor deposition to ensure the uniformity and stability of the coating. The annular electrode array can be precisely etched and deposited on the surface of the needle tip using micro-nano processing technology to ensure the accuracy and functionality of the electrode array. The Ag / AgCl composite electrode layer in the middle of the needle body can be formed on the surface of the needle body using sputtering coating technology, and the polyimide insulating layer can be applied by spin coating. The magnetic connection interface on the upper part of the needle body can be made of high-strength magnetic material and realized through precision machining and assembly technology.

[0088] The workflow of the preoperative anesthesia model includes:

[0089] Calculate the root mean square value of the electromyographic signal to reflect muscle tension;

[0090] Calculate the standard deviation of ECG RR intervals to assess autonomic nervous system activity;

[0091] Calculate skin impedance value to characterize the intensity of stress response;

[0092] Calculate and fuse the three characteristic values ​​of electromyography, electrocardiography and skin impedance signals to form a comprehensive anesthesia index;

[0093] When the comprehensive anesthesia index is less than the anesthesia threshold of 0.65, it is judged as insufficient anesthesia; when the comprehensive anesthesia index is in the range of 0.65-0.85, it is judged as an ideal anesthesia state; when the comprehensive anesthesia index is greater than 0.85, it triggers an over-deep anesthesia warning;

[0094] The current parameter adjustment command is generated based on the deviation between the comprehensive anesthesia index and the anesthesia threshold. When anesthesia is insufficient, the current intensity is increased at a rate of 3mA / second, and the frequency is reduced to 1-2Hz to enhance the inhibitory effect. When anesthesia is too deep, the current intensity is reduced to 5mA and the pulse width is shortened to 0.1ms.

[0095] The electromyographic signal, electrocardiographic signal and skin impedance signal are updated every 5 seconds. When the comprehensive anesthesia index is stable in the range of 0.65-0.85 for 4 consecutive monitoring cycles, it automatically enters the maintenance state and the operation interface displays a green ready mark.

[0096] The workflow of the preoperative anesthesia mode integrates and calculates multiple physiological signals, dynamically adjusts current parameters in real time to ensure precise control of the anesthetic effect, calculates the root mean square value of the electromyographic signal, the standard deviation of the ECG RR interval, and the skin impedance value, and integrates these characteristic values ​​to form a comprehensive anesthesia index. This is the core technical feature of the process. For insufficient anesthesia, the inhibitory effect is enhanced by increasing the current intensity and reducing the frequency; for deep anesthesia, the stimulation is reduced by reducing the current intensity and shortening the pulse width. The signal is updated every 5 seconds and remains stable for 4 consecutive monitoring cycles to ensure a continuous and stable anesthetic state.

[0097] The stimulation current output will be automatically stopped if any of the following conditions are met in the preoperative anesthesia mode:

[0098] The comprehensive anesthesia index is >0.85 for 80 consecutive seconds;

[0099] ECG detected ventricular premature beats > 5 times / minute;

[0100] Single electrode contact impedance mutation> 50%;

[0101] The key technical features of the preoperative anesthesia mode include real-time acquisition and processing of electromyographic signals, electrocardiographic signals, and skin impedance signals. A comprehensive anesthesia index is formed by calculating the root mean square value of the electromyographic signal, the standard deviation of the electrocardiographic RR interval, and the skin impedance value. This index is used to evaluate the anesthesia state and generate current parameter adjustment instructions based on the deviation between the comprehensive anesthesia index and the anesthesia threshold. When the comprehensive anesthesia index is greater than 0.85 for 80 consecutive seconds, the system will automatically stop the stimulation current output to prevent excessive anesthesia. When the electrocardiogram detects more than 5 premature ventricular beats per minute, the system will also trigger the stop mechanism to ensure the patient's heart health. When the single-electrode contact impedance mutation exceeds 50%, the system will identify it as poor contact or other abnormal conditions and automatically stop the current output to avoid potential risks.

[0102] The default baseline value of the current parameters in the preoperative anesthesia mode is 15mA, 2Hz, and 0.3ms. This setting can provide an initial reference value for subsequent current parameter adjustments. Specifically, the combination of 15mA, 2Hz, and 0.3ms can provide a basic current stimulation effect during preoperative anesthesia, ensuring that the patient can have an effective anesthetic effect in the initial stage. In actual application, the system can dynamically adjust these parameters according to the patient's physiological response to achieve the best anesthetic effect. By setting the default baseline value of the current parameters in the preoperative anesthesia mode, the doctor's operating steps during the preoperative anesthesia process are simplified and the complexity of manually adjusting the current parameters is reduced. As a result, the efficiency and accuracy of electroacupuncture anesthesia treatment can be improved, and fluctuations in anesthetic effects caused by improper manual adjustment can be reduced. At the same time, by real-time monitoring and dynamic adjustment of current parameters, the safety of the treatment process is ensured and the risk of complications is reduced.

[0103] The workflow of the postoperative analgesia model is:

[0104] Load the patient's preoperative physiological baseline data;

[0105] Monitor the tension of muscle groups around the surgical incision based on electromyographic signals;

[0106] Analyze the slope of heart rate oscillation based on ECG signals to detect the autonomic nervous system response caused by pain;

[0107] Detect sweat gland secretion activity based on skin impedance signals;

[0108] The postoperative pain level is divided into four levels based on electromyographic signals, electrocardiographic signals and skin impedance signals, and the corresponding stimulation current output mode is automatically switched according to the pain level;

[0109] Among them, when the electromyographic amplitude is less than 45μV and the heart rate variability coefficient is greater than 17%, it is classified as mild pain. At this time, a 3Hz low-frequency square wave is continuously output to maintain basic analgesia;

[0110] When the myoelectricity is 45-145μV and the skin impedance decreases by more than 21%, it is classified as moderate pain, and 12Hz / 60Hz alternating sweep frequency stimulation is continuously output;

[0111] When the myoelectricity is greater than 145μV and the heart rate oscillation slope is less than 3.1ms / RR, it is classified as moderate to severe pain, at which time a 90Hz high-frequency pulse train is triggered;

[0112] When the electromyographic signal, electrocardiographic signal and skin impedance signal exceed the threshold at the same time, the output current stimulation intensity is increased to 25mA.

[0113] By real-time monitoring and analysis of the patient's electromyographic, electrocardiographic, and skin impedance signals, it is possible to dynamically assess the level of postoperative pain and automatically adjust the current output mode based on the pain level. This approach can provide personalized analgesia based on the patient's real-time physiological responses, avoiding the subjectivity and lag inherent in manual adjustment of current parameters in traditional devices. By loading the patient's preoperative physiological baseline data and monitoring postoperative physiological signals in real time, it is possible to effectively assess the patient's pain level and automatically adjust the current output mode. This approach not only improves the accuracy and stability of the analgesic effect, but also reduces the physician's workload and improves the safety and efficiency of treatment.

[0114] The workflow of the disease treatment model includes:

[0115] Select the type of disease and symptoms currently being treated from the treatment information library, and perform electrical stimulation based on the patient's current treatment stage;

[0116] The treatment information database includes standard treatment plans for neurological diseases, locomotor system diseases, and visceral diseases, as well as the corresponding acupuncture points and acupuncture current stimulation parameter ranges for each disease.

[0117] Among them, during the acute stage of treatment, 50-100Hz high-frequency current short-term shock treatment is used;

[0118] During the remission phase of the treatment, 2Hz / 15Hz medium and low frequency current alternation treatment is used;

[0119] During the recovery phase of treatment, intermittent pulse therapy is used, with current output every 0.5 minutes and a single current output lasting 2 minutes. This achieves dynamic adjustment of current parameters, improving the accuracy and effectiveness of treatment.

[0120] In the acute phase, short bursts of high-frequency current can quickly alleviate symptoms. Alternating medium- and low-frequency currents during the remission phase can help stabilize the condition. Intermittent pulse therapy during the rehabilitation phase can help accelerate recovery. These phases of treatment and the settings for current stimulation parameters significantly enhance treatment effectiveness and safety.

[0121] The safety monitoring unit triggers an emergency stop when it detects any of the following conditions:

[0122] The skin impedance signal suddenly changes by more than 29% and lasts for 130ms;

[0123] Leakage current exceeds 90μA;

[0124] The standard deviation of heart rate variability exceeds a preset safety threshold.

[0125] The safety monitoring unit can immediately trigger the emergency stop mechanism when an abnormal situation is detected, significantly improving the safety and reliability of treatment.

[0126] Working Principle: The platinum-black-coated electrode array of the detection-type acupuncture needle collects deep myoelectric signals. The Ag / AgCl composite electrode in the middle of the needle body simultaneously acquires ECG and skin impedance signals. The main control module has a built-in neural network evaluation model that integrates and analyzes these three physiological signals. It calculates the myoelectric root mean square value (RMS) to reflect muscle tension, the standard deviation of the ECG RR interval to assess autonomic nervous system activity, and the dynamic skin impedance value to represent stress intensity, generating a comprehensive anesthesia index in the range of 0-1. When the index is <0.65, the trigger current intensity increases, and when the low-frequency output index is >0.85, the intensity is automatically reduced to 5mA and the pulse width is shortened to 0.1ms, achieving dynamic balance in anesthesia depth.

[0127] Based on the myoelectric amplitude, ECG disorder and impedance mutation frequency, pain is divided into four levels, corresponding to output strategies such as 3Hz continuous square wave, 12Hz / 60Hz sweep frequency stimulation, and 90Hz pulse train. The pain level switching response time is <200ms. Combined with the preset scheme matching the treatment stage, the parameter adaptive adjustment module sends customized current to the acupuncture needle through the multi-channel stimulation output unit according to the decision-making instructions. The intensity, frequency, waveform and pulse width can be adjusted in real time. The synchronously running safety monitoring unit continuously detects skin impedance mutations, leakage current and heart rate variability abnormalities. If any indicator exceeds the limit, an emergency stop is triggered, and the blocking time is <50ms, which significantly reduces the risk of muscle rigidity or skin burns.

[0128] Since there are individual differences among patients during use, how to grasp these differences to accurately determine the parameters of the instrument in each working mode can improve the patient's treatment experience. In one embodiment, the parameter adaptive adjustment module further performs the following operations:

[0129] Obtain the patient's identity information, including age, gender, height, weight, place of origin, and parameter data of various parts of the body;

[0130] According to the pre-configured patient analysis library, the identity information is analyzed to determine the first tolerance parameter; the patient analysis library is pre-configured based on the data analysis of a large number of patients, and each analysis standard item in the library is obtained by associating the identity information of each representative group of people obtained through data analysis with the first tolerance parameter corresponding to the group of people, that is, the standard identity information set is associated with the first tolerance parameter in a one-to-one correspondence; the individual identity information of the patient is matched with the standard identity information set to retrieve the corresponding first tolerance parameter. Generally speaking, patients of different ages, genders, heights, weights, places of origin, parameter data of various parts, etc. have different tolerance to electric current stimulation. Through the pre-configured patient analysis library, it can be quantified to obtain the first tolerance parameter. The larger the first tolerance parameter, the closer the current to the upper limit of the allowable limit can be used for control in the working mode; the smaller the first tolerance parameter, the closer the current to the lower limit of the allowable limit needs to be used for control;

[0131] Obtain the patient's historical medical data; historical medical data is a powerful analytical data for individual differences among patients. By analyzing historical medical data, we can understand the individual differences of patients, so that we can further adapt to the individual patient based on the analysis of the patient's group;

[0132] Based on the data filtering rules configured for the current working mode, historical medical data is filtered to obtain relevant data. Different working modes correspond to different filtering rules. For example, if the current working mode is electroacupuncture treatment for the legs, the corresponding filtering rules will filter out relevant data related to historical treatment data related to the legs and electrical stimulation responses.

[0133] Determining a second tolerance parameter based on the relevant data and a pre-configured individual difference analysis library; specifically, extracting key parameter values ​​from the relevant data based on the pre-configured key parameter library, and then retrieving the second tolerance parameter from the individual difference analysis library using the key parameter values ​​as search items, wherein the key parameter values ​​include: numerical values ​​of tolerance description information of electrical stimulation quantified by a pre-configured quantization library, values ​​of parameters indicating whether surgical treatment has been performed, etc.; wherein the individual difference analysis library is pre-analyzed and configured; and the key parameter values ​​in the library are associated with the second tolerance parameter in a one-to-one correspondence;

[0134] Combine the first tolerance parameter and the second tolerance parameter to adjust the various working parameters of the current working mode. Generally, the adjustment control table corresponding to each working parameter is configured. The first tolerance parameter and the second tolerance parameter are used to query the adjustment control table to obtain the corresponding adjustment value. In addition, for working parameters whose data type is a range interval, the adjustment formula is as follows:

[0135]

[0136] Where, T is the adjusted working parameter value; t max , t min are the upper and lower limits of the range respectively; N1 is the value of the first tolerance parameter, N2 is the value of the second tolerance parameter; N m1 is the maximum configuration value corresponding to the first tolerance parameter; N m2 is the maximum configuration value corresponding to the second tolerance parameter; α1 and α2 are pre-configured weight coefficients corresponding to the first tolerance parameter and the second tolerance parameter;

[0137] In general, the working parameters determined by this type of range interval can be random, that is, any value in the range interval is randomly used for control, or the midpoint value is used for control; through the above analysis, precise control is achieved according to the specific situation of the patient, making it more in line with the specific situation of the patient, thereby improving the user experience.

[0138] Since the relevant data used in the historical medical data analysis is often not very comprehensive, the integrity of the relevant data is necessary to ensure the accuracy of the above adjustment. Therefore, in one embodiment, the integrity of the relevant data is verified based on preset verification rules; if the verification fails, missing items are determined; and the missing items are predicted and filled using the associated data associated with the missing items in the historical medical data of the patient's related personnel;

[0139] The steps for predicting and filling missing items are as follows:

[0140] The first correlation coefficient is determined based on the relationship between the relevant personnel and the patient; that is, the first correlation coefficient is obtained based on the relationship between the relevant personnel and the patient through the pre-configured first correlation coefficient table; the first correlation coefficient reflects the closeness of the relationship between the relevant personnel and the patient, that is, the first correlation coefficient corresponding to the direct relationship such as father and son, mother and daughter, brothers, etc. is the highest.

[0141] The second correlation coefficient is determined based on the reference comparison of the historical medical data of the relevant personnel and the medical data of the patient; that is, the matching of each data item in the reference comparison is quantified to obtain a quantitative parameter, and the quantitative parameter is then filled into a pre-configured template to obtain a reference parameter set; the reference parameter set is matched with the standard parameter set corresponding to each second correlation coefficient in the pre-configured second correlation coefficient determination library, and the second correlation coefficient associated with the matched standard parameter set is extracted; that is, the numerical values ​​of each data in the medical data are matched, and the more matching data items, the higher the second correlation coefficient;

[0142] Determining weights of data corresponding to relevant personnel based on the first correlation coefficient and the second correlation coefficient;

[0143] Based on the weights of the associated data of the missing items of each relevant person, the data of the related items in the patient's historical data of the missing items, and the weight coefficients of the related items, the data corresponding to the missing items is determined. The formula for the data corresponding to the missing items is as follows:

[0144]

[0145] In the formula, Q is the data corresponding to the missing items after supplementation, X 1i is the first correlation coefficient of the i-th related person; X 2i is the second correlation coefficient of the i-th related person; D i is the data corresponding to the missing item of the i-th related person; n is the total number of related persons; d j is the data of the jth related item of the missing item in the patient's historical data; μ j is the weight coefficient of the jth related item of the missing item in the patient's historical data; m is the total number of related items; γ1 and γ2 are pre-configured coefficients.

[0146] The missing items are completed with the patient's existing historical data and the medical data of related personnel to achieve predictive supplementation, ensure the effectiveness of the adjustment analysis, and further improve the patient experience.

[0147] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0148] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A medical integrated modular electroacupuncture anesthesia therapeutic instrument, characterized in that: It includes signal acquisition and monitoring module, main control module, parameter adaptive adjustment module, stimulation output module and safety monitoring unit; The signal acquisition and monitoring module is configured to collect myoelectric signals, electrocardiographic signals and skin impedance signals in real time through detection acupuncture needles and wires, and transmit the signals to the main control module; The main control module is configured to receive electromyographic signals, electrocardiographic signals and skin impedance signals, analyze and process the signals according to the set current usage mode, and use a built-in neural network evaluation model to perform multi-parameter fusion decision calculation and analysis on the electromyographic signals, electrocardiographic signals and skin impedance signals; Among them, the current usage modes include preoperative anesthesia mode, postoperative analgesia mode and disease treatment mode; The parameter adaptive adjustment module dynamically adjusts the current stimulation strategy based on a multi-parameter fusion decision algorithm, and adaptively adjusts the intensity, frequency, waveform and pulse width of the output current according to the patient's muscle, heartbeat and skin sweat gland activity; The stimulation output module is configured to transmit the regulated current to the corresponding detection-type acupuncture needle through a wire, and continuously output the stimulation current to the patient's acupuncture point; The safety monitoring unit is configured to monitor skin impedance mutation, leakage current and heart rate variability in real time and trigger an emergency stop mechanism.

2. A medical integrated modular electroacupuncture anesthesia therapeutic apparatus according to claim 1, characterized in that: The detection-type acupuncture needle is further configured as follows: The needle tip is configured to collect electromyographic signals from deep muscles and transmit stimulation current to acupuncture points. The outer wall of the needle tip is provided with a platinum black coating and a ring electrode array. The platinum black coating covers the 1.5mm-2mm area of ​​the needle tip. The middle part of the needle body is configured to collect ECG and skin impedance signals. It is equipped with an Ag / AgCl composite electrode layer. The composite electrode layer is arranged around the middle area of ​​the needle body, with a height of 8mm and a thickness of 200nm. It is isolated from the needle tip by a polyimide insulation layer. A magnetic connection interface is provided on the upper part of the needle body to ensure the stability and anti-interference ability of signal transmission; 3. A medical integrated modular electroacupuncture anesthesia therapeutic apparatus according to claim 1, characterized in that: The workflow of the preoperative anesthesia model includes: Calculate the root mean square value of the electromyographic signal to reflect muscle tension; Calculate the standard deviation of ECG RR intervals to assess autonomic nervous system activity; Calculate skin impedance value to characterize the intensity of stress response; Calculate and fuse the three characteristic values ​​of electromyography, electrocardiography and skin impedance signals to form a comprehensive anesthesia index; When the comprehensive anesthesia index is less than the anesthesia threshold of 0.65, it is judged as insufficient anesthesia; when the comprehensive anesthesia index is in the range of 0.65-0.85, it is judged as an ideal anesthesia state; when the comprehensive anesthesia index is greater than 0.85, it triggers an over-deep anesthesia warning; The current parameter adjustment command is generated based on the deviation between the comprehensive anesthesia index and the anesthesia threshold. When anesthesia is insufficient, the current intensity is increased at a rate of 3mA / second, and the frequency is reduced to 1-2Hz to enhance the inhibitory effect. When anesthesia is too deep, the current intensity is reduced to 5mA and the pulse width is shortened to 0.1ms. The electromyographic signal, electrocardiographic signal and skin impedance signal are updated every 5 seconds. When the comprehensive anesthesia index is stable in the range of 0.65-0.85 for 4 consecutive monitoring cycles, it automatically enters the maintenance state and the operation interface displays a green ready mark.

4. A medical integrated modular electroacupuncture anesthesia therapeutic apparatus according to claim 3, characterized in that: The preoperative anesthesia mode automatically stops the stimulation current output when any of the following conditions are met: The comprehensive anesthesia index is >0.85 for 80 consecutive seconds; ECG detected ventricular premature beats > 5 times / minute; Single electrode contact impedance mutation> 50%; 5. The medical integrated modular electroacupuncture anesthesia therapeutic apparatus according to claim 3, characterized in that: The default baseline values ​​of the current parameters of the preoperative anesthesia mode are current intensity 15 mA, frequency 2 Hz, and pulse width 0.3 ms.

6. The medical integrated modular electroacupuncture anesthesia therapeutic apparatus according to claim 1, characterized in that: The workflow of the postoperative analgesia model is as follows: Load the patient's preoperative physiological baseline data; Monitor the tension of muscle groups around the surgical incision based on electromyographic signals; Analyze the slope of heart rate oscillation based on ECG signals to detect the autonomic nervous system response caused by pain; Detect sweat gland secretion activity based on skin impedance signals; The postoperative pain level is divided into four levels based on electromyographic signals, electrocardiographic signals and skin impedance signals, and the corresponding stimulation current output mode is automatically switched according to the pain level; Among them, when the electromyographic amplitude is less than 45μV and the heart rate variation coefficient is greater than 17%, it is classified as mild pain, and a 3Hz low-frequency square wave is continuously output; When the myoelectricity is 45-145μV and the skin impedance decreases by more than 21%, it is classified as moderate pain, and 12Hz / 60Hz alternating sweep frequency stimulation is continuously output; When the myoelectricity is greater than 145μV and the heart rate oscillation slope is less than 3.1ms / RR, it is classified as moderate to severe pain, at which time a 90Hz high-frequency pulse train is triggered; When the electromyographic signal, electrocardiographic signal and skin impedance signal exceed the threshold at the same time, the output current stimulation intensity is increased to 25mA.

7. The medical integrated modular electro-acupuncture anesthesia therapeutic apparatus according to claim 1, characterized in that: The workflow of the disease treatment model includes: Select the type of disease and symptoms currently being treated from the treatment information library, and perform electrical stimulation based on the patient's current treatment stage; The treatment information database includes standard treatment plans for neurological diseases, locomotor system diseases, and visceral diseases, as well as the corresponding acupuncture points and acupuncture current stimulation parameter ranges for each disease. Among them, during the acute stage of treatment, 50-100Hz high-frequency current short-term shock treatment is used; During the remission phase of the treatment, 2Hz / 15Hz medium and low frequency current alternation treatment is used; During the recovery phase of treatment, intermittent pulse therapy is used, with current output every 0.5 minutes and a single current output lasting 2 minutes.

8. The medical integrated modular electro-acupuncture anesthesia therapeutic apparatus according to claim 1, characterized in that: The safety monitoring unit triggers an emergency stop when it detects any of the following situations: The skin impedance signal suddenly changes by more than 29% and lasts for 130ms; Leakage current exceeds 90μA; The standard deviation of heart rate variability exceeds a preset safety threshold.

9. The medical integrated modular electroacupuncture anesthesia therapeutic apparatus according to claim 1, characterized in that: The parameter adaptive adjustment module also performs the following operations: Obtain the patient's identity information, including age, gender, height, weight, place of origin, and parameter data of various parts of the body; Analyzing the identity information based on a pre-configured patient analysis library to determine a first tolerance parameter; Obtain historical medical data of patients; Based on the data screening rules configured in the current working mode, historical medical data is screened to obtain relevant data; determining a second tolerance parameter based on the relevant data and a pre-configured individual difference analysis library; The first tolerance parameter and the second tolerance parameter are comprehensively considered to adjust various operating parameters of the current operating mode.

10. The medical integrated modular electro-acupuncture anesthesia therapeutic apparatus according to claim 9, characterized in that: The parameter adaptive adjustment module also performs the following operations: Verify the integrity of relevant data based on pre-set verification rules; if verification fails, identify missing items; and predict and fill in missing items using associated data related to the missing items in the historical medical data of the patient's related personnel; The steps for predicting and filling missing items are as follows: Based on the relationship between the relevant personnel and the patient, the first correlation coefficient is determined; Determining a second correlation coefficient based on a reference comparison of the historical medical data of the relevant personnel and the medical data of the patient; Determining weights of data corresponding to relevant personnel based on the first correlation coefficient and the second correlation coefficient; The data corresponding to the missing item is determined based on the weight sum of the associated data of the missing item of each relevant person, the data of the related items of the missing item in the patient's historical data and the weight coefficient of the related items.

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