Nerve monitoring surgical instrument under endoscope and electronic equipment

Abstract

The invention discloses an endoscopic nerve monitoring surgical instrument and electronic equipment, and relates to the field of nerve evasion feedback, the method comprises the steps that the nerve monitoring surgical instrument comprises an integrated electrode, a jaw, a chip control device and a nerve monitoring wire, the integrated electrode is connected with the nerve monitoring wire, the nerve monitoring wire is connected with a nerve monitor, and the chip control device is connected with the chip control device; the nerve monitoring surgical instrument is connected with the electronic equipment; the nerve electrophysiological signals are transmitted into a nerve monitor; whether nerve distribution exists in the target tissue area or not is judged based on the numerical value of the nerve electrophysiological signal, under the condition that no nerve distribution exists in the target tissue area, high-frequency stimulation current is output, electrocoagulation operation processing is conducted on the target tissue area, and under the condition that nerve distribution exists in the target tissue area, high-frequency stimulation current is output; and determining the nerve distribution of the target area tissue according to the impedance feedback and the intensity gradient value of the electromyographic signal. The accuracy of nerve detection can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of nerve avoidance feedback, and in particular to an endoscopic nerve monitoring surgical instrument and electronic equipment. Background Art

[0002] With the rapid development of science and technology, the field of medical surgery has also ushered in unprecedented changes, especially in those extremely delicate operations involving endoscopic nerve tissue, where every detail of the surgical process is crucial. As one of the most complex and fragile systems in the human body, the integrity of the structure and function of nerve tissue plays a decisive role in the normal functioning of the human body. Therefore, during the operation, minimizing the risk of nerve damage while maintaining surgical efficiency has become a key factor in determining the success of the operation.

[0003] At present, the traditional detection of nerve coverage position is mainly through real-time monitoring of electrophysiological signals around the nerve tissue, judging the positional relationship between the detection electrode and the nerve, so as to determine the nerve coverage position. Specifically, when the detection electrode is placed around the nerve tissue, the doctor will judge the positional relationship between the detection electrode and the nerve based on the characteristics of the electrophysiological signal. For example, when the detection electrode is close to the nerve tissue, the amplitude of the collected signal may increase, and the waveform may become clearer and more regular. However, during the operation, when the doctor judges the positional relationship between the detection electrode and the nerve based on the characteristics of the electrophysiological signal, it will consume a lot of energy, and the doctor also needs to closely monitor the patient's vital signs and other information at all times, which may cause the doctor to become tired and distracted. In the case of distraction and fatigue, the doctor's decision-making accuracy is reduced, which affects the clinical effect and progress of the operation. Summary of the invention

[0004] The embodiments of the present application provide an endoscopic nerve monitoring surgical instrument and electronic equipment for improving the accuracy of nerve detection.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions: In a first aspect, an endoscopic neuromonitoring surgical instrument is provided, the neuromonitoring surgical instrument comprising an integrated electrode, a jaw, a chip control device and a neuromonitoring wire, the integrated electrode is arranged in the jaw, the integrated electrode is connected to the neuromonitoring wire, the neuromonitoring wire is connected to a neuromonitor, and the neuromonitoring surgical instrument is connected to an electronic device; Among them, the electronic device is used to obtain the target area tissue within the target surgical area, use the integrated electrodes in the jaws to output a low-frequency stimulation current to the target tissue area, transmit the neuroelectrophysiological signals in the target tissue area to the nerve monitor through the nerve monitoring wire, and display the value of the neuroelectrophysiological signal. The neuroelectrophysiological signal includes impedance feedback and electromyographic signal, and based on the value of the neuroelectrophysiological signal, it is judged whether there is nerve distribution in the target tissue area. If there is no nerve distribution in the target tissue area, the integrated electrodes in the jaws are used to output a high-frequency stimulation current to perform electrocoagulation on the target tissue area. If there is nerve distribution in the target tissue area, the nerve monitor displays impedance feedback in real time, and the intensity gradient value of the electromyographic signal determined by the chip control device is used to determine the nerve distribution of the target area tissue.

[0006] In a possible implementation of the first aspect, the jaws include an upper jaw and a lower jaw, a cutting head and an integrated electrode are provided in the jaws, the integrated electrode includes a first electrode, a second electrode and a nerve detection electrode, the first electrode and the second electrode are respectively located at the upper jaw and the lower jaw of the jaws, and the nerve detection electrode is integrated in the first electrode or the second electrode, wherein the cutting head is used to cut the target tissue area, the first electrode and the second electrode are used to perform electrocoagulation on the target tissue area, and the nerve detection electrode is used to detect nerve distribution in the target tissue area.

[0007] In a possible implementation of the first aspect, a nerve monitoring bipolar circuit is provided in the jaws, and the nerve monitoring bipolar circuit includes a base, a collector and an emitter, the collector includes a first power supply, a first control switch and a first resistor, one end of the first resistor is connected to a first common point of the base and the emitter, the other end of the first resistor is connected to one end of the first control switch, and the other end of the first control switch is connected to the positive electrode of the first power supply, the base includes a second resistor, a third resistor and a second control switch, one end of the second control switch is connected to one end of the second resistor, the other end of the second resistor is connected to the first common point of the collector and the emitter, one end of the third resistor is connected to one end of the second resistor, the other end of the third resistor is connected to a second common point of the emitter and the ground point, and the emitter is connected to a second common point of the ground point and the other end of the third resistor.

[0008] In a possible implementation of the first aspect, a nerve stimulation current circuit is provided in the jaws, and the nerve stimulation current circuit includes an input circuit and an output circuit, the input circuit includes a fourth resistor, a fifth resistor, a capacitor, a relay, and an electromyographic signal module, one end of the fourth resistor is connected to the electromyographic signal module, the other end of the fourth resistor is connected to the capacitor, the other end of the capacitor is connected to the fifth resistor, the other end of the fifth resistor is connected to the coil of the relay, and the other end of the coil is connected to the electromyographic signal module, the output circuit includes a relay, a second power supply, a third control switch and a sixth resistor, the positive pole of the second power supply is connected to the third control switch, the other end of the third control switch is connected to the sixth resistor, the other end of the sixth resistor is connected to the normally closed contact of the relay, and the other end of the normally closed contact of the relay is connected to the negative pole of the second power supply.

[0009] In a possible implementation of the first aspect, the value of the neuroelectrophysiological signal includes an impedance feedback value and an electromyographic signal, and the nerve monitoring surgical instrument is further used to determine whether the impedance feedback is a first preset impedance threshold based on the impedance feedback value, and if the impedance feedback is the first preset impedance threshold, it is determined that there is nerve tissue in the target surgical area, and the integrated electrode stops outputting the high-frequency stimulation current and outputs the low-frequency stimulation current to achieve continuous alternating operations of nerve detection and electrocoagulation operation on the target area tissue in the target surgical area; If the impedance feedback is not the first preset impedance threshold, it is determined that there is no nerve tissue in the target surgical area, and the integrated electrode outputs a high-frequency stimulation current to perform an electrocoagulation operation on the target area tissue in the target surgical area.

[0010] In a possible implementation of the first aspect, the nerve monitoring surgical instrument is also used to use an integrated electrode to output a high-frequency stimulation current to the target surgical area to perform an electrocoagulation operation on the target area tissue within the target surgical area. The chip control device obtains the first impedance feedback of the target surgical area in real time. When the first impedance feedback reaches a second preset impedance threshold, the nerve stimulation current circuit is used to output the nerve stimulation current to the target surgical area, and the second impedance feedback is obtained in real time. Based on the second impedance feedback, the relative position of the target area tissue and the nerve tissue is determined. When the relative position reaches the preset nerve threshold, the output of the high-frequency stimulation current to the target surgical area is stopped.

[0011] In a possible implementation manner of the first aspect, determining the relative position of the target area tissue and the nerve tissue based on the second impedance feedback includes: Comparing the value of the second impedance feedback of the target area tissue with the impedance feedback in the preset database to determine the tissue type of the target area tissue; In the target surgical area, the position and impedance value of each electrode in the preset array electrode are obtained; The spatial distribution of impedance values ​​is constructed through the position and impedance value of each electrode; Through spatial distribution, use the preset drawing software to draw the impedance value topology map; Determine the gradient direction of impedance value change through the impedance value topology diagram; The relative position of the target area tissue and the neural tissue is determined based on the tissue type and gradient direction.

[0012] In a possible implementation of the first aspect, the nerve distribution of the target area tissue is determined by real-time display of impedance feedback by a nerve monitor and an intensity gradient value of an electromyographic signal determined by a chip control device, including: Obtain historical impedance feedback and historical electromyographic signals within a preset time period; Based on historical impedance feedback, historical electromyographic signals and a preset time period, the impedance change rate and the electromyographic signal amplitude slope are calculated respectively; Based on the impedance change rate and the slope of the EMG signal amplitude, an impedance-EMG correlation model was constructed; Acquire the electromyographic signal within a preset time period in real time and calculate the amplitude slope of the electromyographic signal; Using the preset image processing technology, the amplitude slope of the electromyographic signal is converted into an amplitude slope distribution diagram; Marking the area in the amplitude slope distribution diagram that exceeds a preset amplitude slope threshold to obtain a high-frequency discharge area; Using the preset interpolation algorithm, the high-frequency discharge area is converted into a dynamic EMG thermal map; Extract the image features of the impedance value topology map and the dynamic EMG thermal map, and fuse the image features in the impedance-EMG correlation model to obtain the fused reconstructed image; Calculate the probability density function of nerve distribution based on the fused reconstructed image; Convert the probability density function into a visual image using preset visualization software; In the visualization image, the areas with probability density greater than the preset density threshold are marked to determine the nerve distribution of the target area tissue.

[0013] In a possible implementation manner of the first aspect, calculating the probability density function of nerve distribution according to the fused reconstructed image includes: Obtain each data point in the fused reconstructed image; Use the preset kernel function to calculate the probability density for each data point; The probability density of each data point is superimposed to obtain a probability density function curve of the fused reconstructed image, and the probability density function curve is used to characterize the probability density function.

[0014] In a second aspect, the present application provides an electronic device, including: Memory; and A processor configured to retrieve instructions from a memory and to perform the following steps when executing the instructions: The target area tissue in the target surgical area is obtained, and a low-frequency stimulation current is output to the target tissue area using the integrated electrodes in the jaws. The neuroelectrophysiological signals in the target tissue area are transmitted to the nerve monitor through the nerve monitoring wire, and the value of the neuroelectrophysiological signals is displayed. The neuroelectrophysiological signals include impedance feedback and electromyographic signals. Based on the value of the neuroelectrophysiological signals, it is determined whether there is nerve distribution in the target tissue area. If there is no nerve distribution in the target tissue area, a high-frequency stimulation current is output using the integrated electrodes in the jaws to perform electrocoagulation on the target tissue area. If there is nerve distribution in the target tissue area, the nerve monitor displays impedance feedback in real time, and the intensity gradient value of the electromyographic signal determined by the chip control device is used to determine the nerve distribution of the target area tissue.

[0015] Through the above technical solution, the combination of integrated electrodes and nerve monitoring wires enables surgical instruments to accurately locate nerve positions. Through impedance feedback and electromyographic signal analysis, surgeons can clearly understand the distribution of nerves and provide accurate navigation for surgery. Through real-time nerve monitoring, the surgical team can accurately determine whether there are nerves in the target surgical area, thereby avoiding accidental damage to nerves during surgery, greatly improving the safety of surgery. After confirming that there are no nerves in the target tissue area, the surgical instrument can quickly switch to the high-frequency stimulation current mode for electrocoagulation, simplifying the surgical steps and improving surgical efficiency. Endoscopic nerve monitoring surgical instruments significantly improve the safety and efficiency of surgery through real-time and accurate nerve monitoring functions, reduce postoperative complications, enhance the confidence of surgeons, and promote the advancement of medical technology.

[0016] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of an endoscopic nerve monitoring surgical instrument provided in an embodiment of the present application; Figure 2 A flowchart of an endoscopic neural dynamic avoidance feedback monitoring method provided in an embodiment of the present application; Figure 3A bipolar circuit diagram of nerve monitoring of an endoscopic nerve monitoring surgical instrument provided in an embodiment of the present application; Figure 4 A nerve stimulation current circuit diagram of an endoscopic nerve monitoring surgical instrument provided in an embodiment of the present application; Figure 5 A schematic structural diagram of a chip control device for an endoscopic nerve monitoring surgical instrument provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0020] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0021] Figure 1 The structure diagram of an endoscopic nerve monitoring surgical instrument according to an embodiment of the present application is schematically shown. Figure 1 As shown, an embodiment of the present application provides a structural schematic diagram of an endoscopic nerve monitoring surgical instrument, wherein the nerve monitoring surgical instrument includes an integrated electrode, a jaw, a chip control device and a nerve monitoring wire, wherein the integrated electrode is disposed in the jaw, the integrated electrode is connected to the nerve monitoring wire, the nerve monitoring wire is connected to a nerve monitor, and the nerve monitoring surgical instrument is connected to an electronic device, wherein the electronic device is used to obtain target area tissue within the target surgical area.

[0022] Figure 2 A flow chart of an endoscopic neural dynamic avoidance feedback monitoring method provided in an embodiment of the present application, the method may include the following steps.

[0023] S110, using the integrated electrodes in the jaws to output a low-frequency stimulation current to the target tissue area; S120, transmitting the neurophysiological signal in the target tissue area to the neuromonitoring instrument through the neuromonitoring wire, and displaying the value of the neurophysiological signal, wherein the neurophysiological signal includes impedance feedback and electromyographic signal; S130, judging whether there is nerve distribution in the target tissue area based on the value of the neuroelectrophysiological signal; S140, in the case where there is no nerve distribution in the target tissue area, using the integrated electrodes in the jaws to output a high-frequency stimulation current to perform an electrocoagulation operation on the target tissue area; S150. In the case where there is nerve distribution in the target tissue area, the nerve distribution of the target area tissue is determined by real-time display of impedance feedback by a nerve monitor and intensity gradient value of the electromyographic signal determined by a chip control device.

[0024] In this embodiment, the integrated electrode is arranged inside the jaws and fits closely with the clamping surface of the jaws. The integrated electrode is used to output low-frequency or high-frequency stimulation current, the high-frequency stimulation current is used to perform electrocoagulation treatment on the target tissue area, and the low-frequency stimulation current is used to perform nerve detection treatment on the target tissue area. The jaws are used to clamp and fix the tissue in the surgical area, provide a stable contact surface for the integrated electrode, and ensure the accurate transmission of electrical signals and the effective effect of the stimulation current. The chip control device is used to process and analyze the neuroelectrophysiological signals received from the integrated electrode, including impedance feedback and electromyographic signals, and control the working mode of the surgical instrument according to the analysis results. The nerve monitoring wire connects the integrated electrode and the nerve monitor, and is responsible for transmitting the neuroelectrophysiological signals to the nerve monitor for display and analysis. In this embodiment, the electronic device can be a tablet computer, desktop, laptop, handheld computer, wearable device, notebook computer, ultra-mobile personal computer (ultra-mobile personal computer, UMPC), netbook and other devices with processors. Of course, the electronic device can also be a server. The embodiment of the present application does not impose any special restrictions on the specific form of the electronic device. The electronic equipment is used to obtain target tissue information within the target surgical area, control the working mode of the surgical instrument, and display the numerical value and analysis results of the neuroelectrophysiological signal.

[0025] The working principle of the endoscopic neuromonitoring surgical instrument is, first of all, to use electronic equipment to obtain the target area tissue in the target surgical area. Specifically, before the operation begins, the surgeon starts the electronic equipment and makes corresponding settings according to the surgical requirements. The electronic equipment usually has a high-resolution display screen and advanced image processing functions, which can clearly display the tissue structure in the surgical area. Real-time images of the target surgical area are obtained through an endoscope. In one embodiment, the surgeon can use these images, combined with his or her own professional knowledge and experience, to identify and determine the target area tissue that needs to be treated. In another embodiment, the target area tissue that needs to be treated can be identified by a preset image recognition algorithm.

[0026] After identifying the target area tissue, the surgeon can use the positioning tool or marking function on the electronic device to accurately locate and mark the target area tissue on the image. In this embodiment, the target surgical area refers to the specific anatomical area where the surgery is performed, which is determined by the purpose of the surgery and the specific condition of the patient; the target area tissue refers to the specific tissue that needs to be operated or monitored in the target surgical area. Then, the low-frequency stimulation current is output to the target tissue area through the integrated electrode in the jaws. The low-frequency stimulation current is used to stimulate the nerve to produce an electrophysiological response. While outputting the stimulation current, the integrated electrode receives the neuroelectrophysiological signals in the target tissue area, including impedance feedback and electromyographic signals, which reflect the response of the nerve to the stimulation current. The received neuroelectrophysiological signals are transmitted to the nerve monitor through the nerve monitoring wire. Based on the value of the neuroelectrophysiological signal, it is determined whether there is nerve distribution in the target area tissue. And according to the judgment result of the nerve distribution, the surgeon will decide the next operation. If there is no nerve distribution in the target area tissue, electrocoagulation can be performed; if there is nerve distribution, it needs to be handled more carefully to avoid nerve damage. After confirming that there is no nerve distribution in the target area tissue, the surgical instrument outputs a high-frequency stimulation current through the integrated electrode in the jaws. This high-frequency current has enough energy to destroy tissue to achieve the purpose of hemostasis or cutting. The high-frequency stimulation current performs electrocoagulation on the target area tissue to ensure that bleeding during the operation is controlled while avoiding damage to surrounding tissue. If there is nerve distribution in the target area tissue, the nerve monitor will display impedance feedback in real time. The change in impedance feedback can reflect the relative position relationship between the nerve and the electrode, helping the surgeon to locate the nerve more accurately. At the same time, the chip control device will analyze the electromyographic signal and determine its intensity gradient value. The intensity gradient of the electromyographic signal can reflect the intensity and direction of nerve activity. Combine the impedance feedback and the intensity gradient value of the electromyographic signal to avoid nerve damage.

[0027] Through the above technical solution, the combination of integrated electrodes and nerve monitoring wires enables surgical instruments to accurately locate nerve positions. Through impedance feedback and electromyographic signal analysis, surgeons can clearly understand the distribution of nerves and provide accurate navigation for surgery. Through real-time nerve monitoring, the surgical team can accurately determine whether there are nerves in the target surgical area, thereby avoiding accidental damage to nerves during surgery, greatly improving the safety of surgery. After confirming that there are no nerves in the target tissue area, the surgical instrument can quickly switch to the high-frequency stimulation current mode for electrocoagulation, simplifying the surgical steps and improving surgical efficiency. Endoscopic nerve monitoring surgical instruments significantly improve the safety and efficiency of surgery through real-time and accurate nerve monitoring functions, reduce postoperative complications, enhance the confidence of surgeons, and promote the advancement of medical technology.

[0028] In one implementation of the present embodiment, the jaws include an upper jaw and a lower jaw, a cutting head and an integrated electrode are provided in the jaws, the integrated electrode includes a first electrode, a second electrode and a nerve detection electrode, the first electrode and the second electrode are respectively located at the upper jaw and the lower jaw of the jaws, and the nerve detection electrode is integrated in the first electrode or the second electrode, wherein the cutting head is used to cut the target tissue area, the first electrode and the second electrode are used to perform electrocoagulation on the target tissue area, and the nerve detection electrode is used to detect the nerve distribution in the target tissue area.

[0029] In this embodiment, the jaws are composed of an upper jaw and a lower jaw, and the upper jaw and the lower jaw can move relative to each other so as to clamp, cut or process tissue. A blade is provided in the jaws, which is a component used to cut the target tissue area. The blade is usually sharp and designed with a specific shape to meet different surgical needs; integrated electrodes are also provided in the jaws, which are used to provide electrical energy during surgery to achieve functions such as electrocoagulation or nerve detection; the integrated electrodes include a first electrode, a second electrode and a nerve detection electrode; the first electrode and the second electrode are respectively located in the upper jaw and the lower jaw of the jaws, and are respectively used to electrocoagulate the clamped tissue area, that is, to coagulate the tissue through the action of electrical energy to achieve the purpose of hemostasis or tissue destruction; the nerve detection electrode is integrated in the first electrode or the second electrode, and is mainly used to detect the nerve distribution in the target tissue area, and to identify the position and activity state of the nerve by sending and receiving electrical signals, thereby helping the surgeon to avoid nerve damage during surgery.

[0030] Endoscopic nerve monitoring surgical instruments can simultaneously achieve tissue cutting, electrocoagulation and nerve detection during surgery, improving the accuracy and safety of surgery. In particular, the application of nerve detection electrodes enables surgeons to understand the position and status of nerves in real time during surgery, thereby avoiding nerve damage and protecting the patient's nerve function.

[0031] In one implementation of the present embodiment, a nerve monitoring bipolar circuit is provided in the jaws, and the nerve monitoring bipolar circuit includes a base, a collector and an emitter, the collector includes a first power supply, a first control switch and a first resistor, one end of the first resistor is connected to a first common point of the base and the emitter, the other end of the first resistor is connected to one end of the first control switch, and the other end of the first control switch is connected to the positive electrode of the first power supply, the base includes a second resistor, a third resistor and a second control switch, one end of the second control switch is connected to one end of the second resistor, the other end of the second resistor is connected to the first common point of the collector and the emitter, one end of the third resistor is connected to one end of the second resistor, the other end of the third resistor is connected to a second common point of the emitter and the ground point, and the emitter is connected to a second common point of the ground point and the other end of the third resistor.

[0032] In this embodiment, Figure 3 A bipolar circuit diagram of a nerve monitoring surgical instrument for endoscopic nerve monitoring provided by an embodiment of the present application is shown. Figure 3 A bipolar circuit for neuromonitoring is arranged in the jaws of the endoscopic neuromonitoring surgical instrument. The specific structure of the bipolar circuit for neuromonitoring includes three main parts: a base, a collector and an emitter. The collector includes a first power supply, a first control switch and a second resistor; one end of the second resistor is connected to the first common point of the base and the emitter, which is an important connection point in the circuit and is used to connect part of the circuit of the base and the emitter to the collector; the other end of the second resistor is connected to one end of the first control switch, and the first control switch is used to control the on and off of the circuit; the other end of the first control switch is connected to the positive electrode of the first power supply, and the first power supply provides electrical energy for the circuit. The base includes a third resistor, a fourth resistor and a second control switch; one end of the second control switch is connected to one end of the third resistor, which is used to control the on and off of the base part of the circuit; the other end of the third resistor is connected to the first common point of the collector and the emitter, and forms a circuit connection with the collector; one end of the fourth resistor is connected to one end of the third resistor, and together they constitute part of the circuit of the base; the other end of the fourth resistor is connected to the second common point of the emitter and the ground point, and the base is connected to the emitter and the ground part. The emitter is connected to a second common point of the ground point and the other end of the fourth resistor to form a grounding part of the circuit.

[0033] Specifically, when the first control switch and the second control switch are both in a closed state, the circuit forms a path. The electric energy provided by the first power supply flows through the collector, the base and the emitter to form a current loop. The second resistor, the third resistor and the fourth resistor play the role of current limiting and voltage dividing in the circuit to protect the circuit components from damage by excessive current. The nerve monitoring bipolar circuit can monitor the nerve distribution in the tissue area clamped by the jaws through a specific electrical signal sending and receiving mechanism. When nerves are present, the electrical characteristics of the circuit (such as resistance, current or voltage) may be changed, which can be detected by the circuit and converted into a usable nerve monitoring signal.

[0034] Through the nerve monitoring bipolar circuit set in the jaws, the surgical instrument can monitor the distribution and activity status of nerves in real time during surgery, providing the surgeon with accurate nerve location information, helping to avoid nerve damage and improve the safety of surgery.

[0035] In one implementation of the present embodiment, a nerve stimulation current circuit is provided in the jaws, and the nerve stimulation current circuit includes an input circuit and an output circuit. The input circuit includes a fourth resistor, a fifth resistor, a capacitor, a relay, and an electromyographic signal module. One end of the fourth resistor is connected to the electromyographic signal module, and the other end of the fourth resistor is connected to the capacitor, and the other end of the capacitor is connected to the fifth resistor. The other end of the fifth resistor is connected to the coil of the relay, and the other end of the coil is connected to the electromyographic signal module. The output circuit includes a relay, a second power supply, a third control switch, and a sixth resistor. The positive pole of the second power supply is connected to the third control switch, and the other end of the third control switch is connected to the sixth resistor. The other end of the sixth resistor is connected to the normally closed contact of the relay, and the other end of the normally closed contact of the relay is connected to the negative pole of the second power supply.

[0036] In this embodiment, refer to Figure 4, an embodiment of the present application provides a nerve stimulation current circuit diagram of an endoscopic nerve monitoring surgical instrument, in which a nerve stimulation current circuit is integrated in the jaws, and the circuit is designed to provide nerve stimulation under specific conditions. The nerve stimulation current circuit is mainly divided into two parts: an input circuit and an output circuit. The input circuit is mainly responsible for receiving and processing external signals, which are usually conditions or instructions required to trigger nerve stimulation; in this example, the input circuit includes a fourth resistor, a fifth resistor, a capacitor, a relay and an electromyographic signal module; the electromyographic signal module is used to detect the patient's electromyographic signal, which is a possible condition for triggering nerve stimulation; the fourth resistor, the fifth resistor and the capacitor together constitute a signal conditioning circuit for filtering, amplifying or shaping the electromyographic signal to ensure the accuracy and reliability of the signal; the relay controls the on and off of the output circuit according to the processed electromyographic signal. The connection relationship of the input circuit is that one end of the fourth resistor is connected to the electromyographic signal module to receive the electromyographic signal as input; the other end of the fourth resistor is connected to the capacitor, and the capacitor is used for signal filtering or delaying; the other end of the capacitor is connected to the fifth resistor to further adjust the signal; the other end of the fifth resistor is connected to the coil of the relay, and the coil controls the switching state of the relay according to the received signal; the other end of the coil returns to the electromyographic signal module to form a closed loop.

[0037] The output circuit is mainly responsible for generating and outputting the nerve stimulation current after receiving the signal of the input circuit. In this embodiment, the output circuit includes a relay, a second power supply, a third control switch and a sixth resistor; the second power supply provides electrical energy for the generation of the nerve stimulation current; the third control switch allows the user or system to automatically control the start and stop of the nerve stimulation; the sixth resistor is used to limit the size of the nerve stimulation current to ensure the safety and effectiveness of the stimulation; the relay controls the on and off of the output circuit according to the signal of the input circuit, and when the relay is closed, the second power supply generates the nerve stimulation current through the sixth resistor. The connection relationship of the output circuit is that the positive pole of the second power supply is connected to the third control switch, and the control switch is used to manually or automatically control the on and off of the circuit; the other end of the third control switch is connected to the sixth resistor, and the sixth resistor is used to limit the current size; the other end of the sixth resistor is connected to the normally closed contact of the relay, when the relay coil is energized, the normally closed contact is disconnected, and the circuit is disconnected; when the coil is de-energized, the normally closed contact is closed, and the circuit is turned on; the other end of the normally closed contact of the relay is connected to the negative pole of the second power supply to form a complete output circuit.

[0038] The working principle of the nerve stimulation current circuit is that when the electromyographic signal module detects a specific electromyographic signal, it is transmitted to the coil of the relay through the fourth resistor, the capacitor, and the fifth resistor, so that the relay coil is energized, the normally closed contact of the relay is disconnected, the output circuit is disconnected, and no nerve stimulation current is generated. When the electromyographic signal module does not detect a specific electromyographic signal or the signal is lower than the threshold, the relay coil is de-energized, the normally closed contact of the relay is closed, and the second power supply forms a closed loop through the third control switch, the sixth resistor, and the normally closed contact of the relay to generate a nerve stimulation current.

[0039] The nerve stimulation current circuit can provide precise nerve stimulation during surgery or rehabilitation treatment. Through the control of electromyographic signals, the stimulation intensity and frequency can be adjusted according to the patient's real-time physiological state, improving the accuracy and effectiveness of treatment. It also realizes the control of the generation of nerve stimulation current according to electromyographic signals, providing a more intelligent and precise control method for medical equipment.

[0040] In one implementation of this embodiment, the value of the neuro-electrophysiological signal includes an impedance feedback value and an electromyographic signal. The nerve monitoring surgical instrument is also used to determine whether the impedance feedback is a first preset impedance threshold based on the impedance feedback value. If the impedance feedback is the first preset impedance threshold, it is determined that there is nerve tissue in the target surgical area, and the integrated electrode stops outputting high-frequency stimulation current and outputs low-frequency stimulation current to achieve continuous alternating operations of nerve detection and electrocoagulation operation on the target area tissue in the target surgical area.

[0041] If the impedance feedback is not the first preset impedance threshold, it is determined that there is no nerve tissue in the target surgical area, and the integrated electrode outputs a high-frequency stimulation current to perform an electrocoagulation operation on the target area tissue in the target surgical area.

[0042] In this embodiment, the value of the neuroelectrophysiological signal is used to monitor and determine whether there is nerve tissue in the target surgical area, and the output current type of the integrated electrode is controlled accordingly to achieve intelligent alternation of nerve detection and electrocoagulation operation. The impedance feedback value is measured by a nerve monitoring surgical instrument and reflects the electrical impedance between the surgical instrument and the target tissue. The change in impedance can indirectly reflect the properties of the tissue. For example, the presence of nerve tissue may cause a specific change in impedance. The electromyographic signal is a bioelectric signal that records muscle activity through electrodes. It reflects the state of muscle contraction and relaxation. The electromyographic signal is measured by a nerve monitoring surgical instrument and is used for nerve monitoring.

[0043] After obtaining the neuroelectrophysiological signal, based on the value of the impedance feedback, it is determined whether the impedance feedback is the first preset impedance threshold. In this embodiment, the first preset impedance threshold is determined based on experience or experiment, and is a reference standard threshold for determining whether there is nerve tissue in the target surgical area. Specifically, the impedance feedback value monitored in real time is compared with the first preset impedance threshold. When the impedance feedback value is equal to or close to the first preset impedance threshold, the surgical system determines that there may be nerve tissue in the target surgical area. In order to avoid damage to the nerve tissue, the surgical instrument will immediately stop outputting the high-frequency current that may stimulate or damage the nerve tissue. At the same time, the surgical instrument may output a low-frequency stimulation current for nerve detection of the target area tissue to confirm the specific location and distribution of the nerve tissue. When the impedance feedback value is not equal to the first preset impedance threshold, the surgical system determines that there is no nerve tissue in the target surgical area. At this time, the integrated electrode will output a high-frequency stimulation current for electrocoagulation of the target area tissue. The high-frequency stimulation current has a strong coagulation effect and can quickly and effectively stop bleeding or destroy tissue. When it is determined that there is nerve tissue in the target surgical area, the surgical instrument will alternately perform nerve detection and electrocoagulation operations. This alternating operation can ensure that the nerve tissue is protected from damage during the operation while achieving effective treatment of the target tissue.

[0044] By monitoring the impedance feedback value, it is determined whether there is nerve tissue in the target surgical area, and the output current type of the integrated electrode is controlled accordingly. This intelligent alternating operation method can improve the safety and accuracy of the surgery and reduce the risk of damage to nerve tissue during the surgery. At the same time, it also reflects the trend of medical equipment towards intelligence and precision.

[0045] In one implementation of this embodiment, the nerve monitoring surgical instrument is also used to use an integrated electrode to output a high-frequency stimulation current to the target surgical area to perform an electrocoagulation operation on the target area tissue within the target surgical area. The first impedance feedback of the target surgical area is obtained in real time through the chip control device. When the first impedance feedback reaches a second preset impedance threshold, the nerve stimulation current circuit is used to output the nerve stimulation current to the target surgical area, and the second impedance feedback is obtained in real time. Based on the second impedance feedback, the relative position of the target area tissue and the nerve tissue is determined. When the relative position reaches the preset nerve threshold, the output of the high-frequency stimulation current to the target surgical area is stopped.

[0046] In this embodiment, the nerve monitoring surgical instrument is equipped with an integrated electrode, which can apply a high-frequency stimulation current to the tissue in the target surgical area. The high-frequency stimulation current is mainly used for electrocoagulation operation, that is, the target area tissue is coagulated by the thermal effect of the current to achieve the purpose of hemostasis or cutting. The first impedance feedback is used to reflect the change in electrical impedance between the surgical instrument and the target tissue. The first impedance feedback of the target surgical area is obtained by the surgical instrument. When the first impedance feedback reaches the second preset impedance threshold, the surgical instrument triggers a specific response mechanism. The second preset impedance threshold is set according to surgical needs and experience, and is used to determine when it is necessary to switch to the nerve monitoring mode of the surgical instrument. Specifically, after reaching the second preset impedance threshold, the surgical instrument will use the nerve stimulation current circuit to output the nerve stimulation current to the target surgical area. The nerve stimulation current is a low-frequency, low-intensity current used to stimulate nerve tissue and produce a detectable response.

[0047] Subsequently, while outputting the nerve stimulation current, the surgical instrument will obtain the second impedance feedback in real time. While outputting the nerve stimulation current, the surgical instrument will obtain the second impedance feedback in real time. And based on the second impedance feedback, the surgical instrument can determine the relative position of the target area tissue and the nerve tissue. In other words, when the relative position reaches the preset nerve threshold, it means that the surgical instrument may be close to or has contacted the nerve tissue. In order to avoid damage to the nerve tissue, the surgical instrument will immediately stop outputting high-frequency stimulation current to the target surgical area.

[0048] The nerve monitoring surgical instrument realizes an intelligent surgical operation process through integrated electrodes, real-time impedance monitoring, nerve stimulation current output and relative position judgment. This surgical instrument can maximize the safety of nerve tissue while ensuring the surgical effect, thereby improving the success rate and safety of the operation.

[0049] In one implementation of this embodiment, determining the relative position of the target area tissue and the nerve tissue based on the second impedance feedback includes: Comparing the value of the second impedance feedback of the target area tissue with the impedance feedback in the preset database to determine the tissue type of the target area tissue; In the target surgical area, the position and impedance value of each electrode in the preset array electrode are obtained; The spatial distribution of impedance values ​​is constructed through the position and impedance value of each electrode; Through spatial distribution, use the preset drawing software to draw the impedance value topology map; Determine the gradient direction of impedance value change through the impedance value topology diagram; The relative position of the target area tissue and the neural tissue is determined based on the tissue type and gradient direction.

[0050] The value of the second impedance feedback of the target area tissue is compared with the impedance feedback in the preset database to determine the tissue type of the target area tissue. Specifically, bioimpedance is a complex quantity composed of resistance value and reaction value, which reflects the inherent properties of biological tissue in the electromagnetic field. Different tissue types will show different bioimpedance characteristics due to differences in their cell structure, water content and ion distribution. Therefore, by measuring bioimpedance, the identification of tissue types can be achieved. The second impedance feedback of the target area tissue is obtained. Then, the value of this impedance feedback is compared with the impedance feedback in the preset database to determine the tissue type of the target area tissue. Specifically, by comparing the real-time impedance feedback with the impedance feedback in the preset database, when the real-time impedance feedback is highly matched with the impedance feedback of a certain tissue type in the database, it can be preliminarily judged that the target area tissue belongs to this tissue type. If the real-time impedance feedback does not match any tissue type in the database, further analysis and judgment may be required, or other unknown factors may be considered.

[0051] Next, in the target surgical area, the position and impedance value of each electrode in the preset array electrode are obtained. Specifically, in this embodiment, the preset array electrode can be determined according to the actual situation. The array electrode is usually composed of multiple electrodes and is fixed on the surgical instrument with a certain spacing and arrangement. Before the operation begins, the specific position of each electrode on the surgical instrument is determined by calibration or preset program. These position data can be stored in the control system of the surgical instrument for subsequent impedance measurement and tissue identification. Impedance measurement is achieved by applying an alternating current signal to the electrode and measuring the voltage and current between the electrodes. During the operation, the surgical instrument will apply an alternating current signal to each electrode in the array electrode regularly or as needed. At the same time, the voltage and current between the electrodes are measured to calculate the impedance value of each electrode. It can be achieved by Ohm's law that the impedance value is equal to the voltage divided by the current. Subsequently, the measured impedance value will be transmitted to the control system of the surgical instrument in real time for subsequent tissue identification and analysis.

[0052] After obtaining the position and impedance value of each electrode in the preset array electrode, the spatial distribution of the impedance value is constructed through the position and impedance value of each electrode. First, after obtaining the specific position of each electrode in the array electrode in the surgical area, the position of each electrode in the surgical area is output in the form of three-dimensional coordinates, which can be obtained through the calibration program or preset settings of the surgical instrument. At the same time, it is also necessary to measure and record the impedance value corresponding to each electrode. The impedance value is calculated by applying an alternating current signal to the electrode and measuring the voltage and current between the electrodes. In order to construct the spatial distribution of the impedance value, it is necessary to establish a unified coordinate system, which can take a certain part of the surgical instrument as the origin and the length, width, and height of the surgical area as the coordinate axis. The position coordinates of each electrode are associated with its corresponding impedance value. In the coordinate system, the position of each electrode corresponds to an impedance value, and the distribution of an impedance value in space is obtained. Using three-dimensional drawing software or tools, the spatial distribution of the impedance value can be presented in the form of a three-dimensional graph. For example, color or height can be used to represent the size of the impedance value, so as to intuitively show the distribution of the impedance value in space. By analyzing the spatial distribution of impedance values, the boundaries and distribution of different tissues in the surgical area can be identified. The difference in impedance values ​​can reflect the type, density, moisture content and other characteristics of the tissue, thereby providing guidance for surgical operations. And by real-time monitoring of the spatial distribution changes of impedance values, abnormal conditions during surgery can be discovered in a timely manner. For example, when the impedance value of a certain area changes significantly, it may mean that the tissue state of the area has changed (such as bleeding, edema, etc.).

[0053] Subsequently, the impedance value topology map is drawn through the spatial distribution using the preset drawing software. Specifically, the impedance value topology map can be drawn through the preset drawing software for three-dimensional drawing and topology drawing, such as MATLAB, Python's Matplotlib library, etc. In the drawing software, select "3D Scatter Plot" or similar functions to create a scatter plot, and map the three-dimensional coordinate data of the electrode to the X, Y, and Z axes of the scatter plot. And add a color mapping function to the scatter plot, map the impedance value data to the color of the scatter points, and set the color gradient according to the size of the impedance value, such as from blue (low impedance) to red (high impedance). Adjust the size and shape of the scatter points as needed to better show the distribution of the impedance value. Next, add coordinate axis labels to the three-dimensional scatter plot, clarify the meaning of the X, Y, and Z axes (such as the spatial position of the electrode), add color bars, and mark the correspondence between color and impedance value to facilitate the observer to understand the topology map. Through three-dimensional drawing, the impedance value topology map is obtained.

[0054] After obtaining the impedance value topology map, the gradient direction of the impedance value change is determined through the impedance value topology map. In this embodiment, the impedance value topology map is a graphical representation, in which the position of the electrode and the corresponding impedance value are presented in a three-dimensional scatter plot or other forms, which can intuitively show the distribution of the impedance value in the surgical area or the study area. In three-dimensional space, the gradient is a vector that represents the direction in which the function grows fastest at a certain point. For the impedance value topology map, the gradient of the impedance value in space can be calculated by taking the derivative of the impedance value function in space. The impedance value function is a mathematical function that describes the relationship between impedance and circuit parameters in a circuit. The impedance value function expression is as follows: Z=R+jX Among them, Z is impedance, the unit is ohm (Ω). R is resistance, which represents the resistance encountered by current when flowing in a conductor, the unit is ohm (Ω). j is an imaginary unit. X is reactance, which represents the resistance of inductance and capacitance in the circuit to alternating current, the unit is ohm (Ω).

[0055] In discrete data, the gradient can be calculated using numerical differentiation methods. For example, for each electrode point, the impedance change rate in the X, Y, and Z directions can be calculated to obtain the gradient vector of the point. In the impedance value topology map, arrows or other vector graphics can be used to represent the gradient direction. The length and color of the arrow can represent the magnitude of the gradient, and the direction of the arrow represents the fastest direction of impedance change. By observing the visualization results of the impedance value topology map and the gradient direction, the change trend of the impedance value in space can be determined.

[0056] Next, the relative position of the target area tissue and the nerve tissue is determined in combination with the tissue type and the gradient direction. In this embodiment, the tissue type refers to different types of tissues in the organism, such as nerve tissue, muscle tissue, connective tissue, etc. Each tissue has its own unique structure and function. For example, the nerve tissue is mainly composed of nerve cells and glial cells, which are responsible for transmitting and processing nerve signals. The gradient direction usually refers to the direction in which the function value grows fastest in mathematics and physics. In the fields of biology and medicine, the gradient direction can be used to describe the spatial variation trend of a certain property (such as density, hardness, impedance value, etc.) in biological tissue. Specifically, the tissue type of the target area is determined by the value of the second impedance feedback of the target area tissue. And mathematical and physical methods (such as numerical differentiation, image processing, etc.) are used to calculate and analyze the gradient direction of a certain property (such as impedance value) in the target area. Combined with the tissue type and gradient direction of the target area, the relative position of the target area tissue and the nerve tissue is determined. For example, if the gradient direction points to a specific direction, and the impedance value gradually increases in this direction, and the tissue type in this direction gradually transitions to nerve tissue, then it can be determined that the target area tissue is adjacent or close to the nerve tissue in this direction.

[0057] By identifying tissue types and using impedance value topology to visually display impedance distribution, determine the gradient direction of impedance value changes, and determine the relative position of target area tissue and nerve tissue, we can more accurately understand the tissue structure and characteristics within the target surgical area, thereby formulating more precise and safe surgical plans, improving the success rate and safety of the operation, and thus improving the safety and success rate of the operation.

[0058] In one implementation of this embodiment, the nerve distribution of the target area tissue is determined by real-time display of impedance feedback by a nerve monitor and the intensity gradient value of the electromyographic signal determined by the chip control device, including: Obtain historical impedance feedback and historical electromyographic signals within a preset time period; Based on historical impedance feedback, historical electromyographic signals and a preset time period, the impedance change rate and the electromyographic signal amplitude slope are calculated respectively; Based on the impedance change rate and the slope of the EMG signal amplitude, an impedance-EMG correlation model was constructed; Acquire the electromyographic signal within a preset time period in real time and calculate the amplitude slope of the electromyographic signal; Using the preset image processing technology, the amplitude slope of the electromyographic signal is converted into an amplitude slope distribution diagram; Marking the area in the amplitude slope distribution diagram that exceeds a preset amplitude slope threshold to obtain a high-frequency discharge area; Using the preset interpolation algorithm, the high-frequency discharge area is converted into a dynamic EMG thermal map; Extract the image features of the impedance value topology map and the dynamic EMG thermal map, and fuse the image features in the impedance-EMG correlation model to obtain the fused reconstructed image; Calculate the probability density function of nerve distribution based on the fused reconstructed image; Convert the probability density function into a visual image using preset visualization software; In the visualization image, the areas with probability density greater than the preset density threshold are marked to determine the nerve distribution of the target area tissue.

[0059] In this embodiment, refer to Figure 5, a schematic structural diagram of a chip control device for an endoscopic nerve monitoring surgical instrument provided in an embodiment of the present application, IN1 represents input signal 1 (usually used to receive external control signals or data); IN2 represents input signal 2 (a second input channel or control signal); V1 represents voltage input / reference voltage 1 (may be used for power input or internal voltage regulation); V0 represents voltage input / reference voltage 0 (another voltage input or ground reference point); GND represents ground (common ground terminal); OUT represents output signal (signal output terminal after chip processing); SEL represents selection signal (used for mode selection, channel switching or function configuration); VSV represents power supply voltage input (may be used to power the chip main power supply or a specific function, such as a positive voltage input).

[0060] First, historical impedance feedback and historical electromyographic signals within a preset time period are obtained. Specifically, the historical impedance feedback can be collected by retrieving the impedance feedback data within the preset time period from a dedicated database. These data may be stored in the form of digital signals, including timestamps, impedance values, and other information. The historical electromyographic signals can be collected by retrieving the electromyographic signal data within the preset time period from a dedicated database. These data may include electromyographic signals of multiple channels, and each channel corresponds to the measurement result of an electrode.

[0061] After obtaining historical impedance feedback and historical electromyographic signals, the impedance change rate and the electromyographic signal amplitude slope are calculated based on the historical impedance feedback, the historical electromyographic signals and the preset time period. Specifically, the historical impedance feedback data is extracted from the preset time period. For the impedance value at each time point, the impedance change amount compared with the previous time point is calculated. The impedance change amount can be obtained by subtracting the impedance value at the previous time point from the current impedance value. Subsequently, the impedance change rate is obtained by dividing the impedance change amount by the time interval. If the data is sampled at a fixed interval, the time interval is known; if the timestamp is discontinuous, the time interval corresponding to each change amount needs to be calculated. Next, the historical electromyographic signal data is extracted from the preset time period to ensure that the timestamp of the data is continuous or sampled at a fixed interval. For the electromyographic signal at each time point, its amplitude is calculated. The amplitude can be obtained by calculating the difference between the maximum and minimum values ​​of the signal. For the amplitude at each time point, the amplitude change amount compared with the previous time point (or the time point before the fixed time interval) is calculated. The amplitude change amount can be obtained by subtracting the amplitude at the previous time point from the current amplitude. Then, the amplitude change is divided by the time interval to get the amplitude slope. Again, if the data is sampled at fixed intervals, the time interval is known; if the timestamps are discontinuous, the time interval corresponding to each change needs to be calculated.

[0062] After the impedance change rate and the EMG signal amplitude slope, the impedance-EMG association model is constructed based on the impedance change rate and the EMG signal amplitude slope. Specifically, the impedance change rate is calculated. For each time point, the difference ΔZ between the impedance value Z and the impedance value at the previous time point is calculated. The time interval Δt (usually the sampling interval) is calculated, and the ratio of the impedance difference to the time interval is used as the impedance change rate. Subsequently, the amplitude of the EMG signal is determined, which can be achieved by calculating the envelope or root mean square (RMS) of the signal. For each time point, the difference ΔA between the amplitude and the amplitude at the previous time point is calculated, using the same time interval Δt, and the ratio of the amplitude difference to the time interval is used as the EMG amplitude slope. The calculated impedance change rate and EMG amplitude slope are used as feature data, and the data is divided into a training set and a validation set for model training and validation. Next, according to the data characteristics and research purposes, a suitable mathematical model or machine learning algorithm is selected, such as linear regression, support vector machine and other models. Using the training data set, the impedance change rate is used as one of the input features, the EMG amplitude slope is used as another input feature, and the relationship between the two (such as correlation, causality, etc.) is used as the output target. By training the model and adjusting the parameters to optimize the prediction performance, the impedance-EMG association model is obtained.

[0063] The electromyographic signal within a preset time period is acquired in real time, and the amplitude slope of the electromyographic signal is calculated. Specifically, the amplitude of the electromyographic signal can be determined by calculating the envelope or root mean square (RMS) of the signal. For each time point, the difference ΔA between the amplitude and the amplitude at the previous time point is calculated, the same time interval Δt is used, and the ratio of the amplitude difference to the time interval is used as the amplitude slope of the electromyographic signal.

[0064] After obtaining the amplitude slope of the electromyographic signal, the amplitude slope of the electromyographic signal is converted into an amplitude slope distribution diagram using a preset image processing technology. In this embodiment, the preset image processing technology can be a grayscale image, and the amplitude slope data is mapped to a grayscale level to generate a grayscale image. In a grayscale image, the brightness value of a pixel represents the magnitude of the amplitude slope. The mapped data is processed using a selected image processing algorithm to generate an amplitude slope distribution diagram, which can be implemented by programming, using image processing tools such as Python's OpenCV library, MATLAB, etc. to obtain an amplitude slope distribution diagram.

[0065] After obtaining the amplitude slope distribution graph, the area in the amplitude slope distribution graph that exceeds the preset amplitude slope threshold is marked to obtain the high-frequency discharge area. In this embodiment, the preset amplitude slope threshold can be determined based on experimental data, historical experience or theoretical analysis. Specifically, the amplitude slope distribution graph is read using image processing software or programming tools (such as Python's OpenCV library, MATLAB, etc.), and the amplitude slope distribution graph is binarized. That is, each pixel value in the image is compared with the preset amplitude slope threshold. If the pixel value is greater than the threshold, the pixel is set to white (or other highlight colors); otherwise, it is set to black (or other low-brightness colors). In the binarized image, the white (or highlight color) area is the area that exceeds the preset amplitude slope threshold, that is, the high-frequency discharge area. These areas can be marked using the marking tool in the image processing software or the drawing function in the programming tool, such as using a red border, fill color, etc. to highlight. By observing the marked image, the position and range of the high-frequency discharge area can be clearly seen. These areas usually correspond to the parts with higher amplitude slope in the electromyographic signal, which may indicate abnormal or special states of muscle activity and obtain high-frequency discharge areas.

[0066] After obtaining the high-frequency discharge area, the high-frequency discharge area is converted into a dynamic EMG heat map using a preset interpolation algorithm. The dynamic EMG heat map is a visualization technology used to display the dynamic changes of electromyographic signals (EMG) in time and space. The intensity or activity level of the EMG signal is represented by color mapping (such as from cold colors to warm colors), thereby intuitively reflecting the distribution and changes of muscle activity. In this embodiment, the preset interpolation algorithm can be linear interpolation, which is used to estimate the value of an unknown point between known data points, assuming that the change between two known points is linear, and calculate the value of the unknown point by a straight line equation. Specifically, the amplitude slope value of the high-frequency discharge area is interpolated using the selected interpolation algorithm to generate a smooth and continuous data set. The interpolated data set should contain all time points on the time axis and the corresponding amplitude slope values. The interpolated amplitude slope value is mapped to the color range of the heat map. For example, a color gradient can be set to map a lower amplitude slope value to a colder color (such as blue) and a higher amplitude slope value to a hotter color (such as red). Heatmaps can be generated using image processing software or programming tools (such as Python's matplotlib library, MATLAB, etc.). In a heatmap, the horizontal axis represents time, and the vertical axis can represent the electrode position (if it is multi-channel EMG data) or the range of amplitude slope (if it is single-channel data). The amplitude slope value at each time point is mapped to a corresponding color, forming a dynamic, time-varying heatmap.

[0067] Next, the image features of the impedance value topology map and the dynamic EMG thermogram are extracted, and the image features are fused in the impedance-EMG association model to obtain the fused reconstructed image. Specifically, the feature extraction of the impedance value topology map can extract the texture features and shape features of the impedance value topology map through image processing technology. The texture features can be extracted by using grayscale co-occurrence matrix, local binary pattern (LBP) and other methods to extract the texture features of the image. If there are obvious shapes or contours in the impedance value topology map, these shape features can be extracted, such as area, perimeter, aspect ratio, etc. The feature extraction of the dynamic EMG thermogram can extract the color distribution features in the thermogram, such as color histogram, color moment, etc. Similar to the impedance value topology map, the texture features of the thermogram can also be extracted to capture the dynamic changes of muscle activity. Subsequently, the features extracted from the impedance value topology map and the dynamic EMG thermogram are spliced ​​to form a unified feature vector. The spliced ​​feature vector is reduced in dimension and selected using feature selection algorithms (such as principal component analysis PCA, linear discriminant analysis LDA, etc.) to retain the most important features. Use the fused feature vector as the input of the model, use the corresponding label (such as muscle activity status, impedance value, etc.) as the output of the model, and train the model through the training set, adjust the parameters and settings of the model to obtain the best model performance. Use the trained impedance-EMG association model to predict the new input data to obtain the reconstructed impedance value or muscle activity status. According to the prediction results, combined with the topological structure and color mapping relationship of the original image, generate a fused reconstructed image. For example, the predicted impedance value can be mapped to the topological map using an interpolation algorithm to form a new impedance value topological map, or the predicted muscle activity state can be mapped to the thermal map to form a new dynamic EMG thermal map.

[0068] According to the fused reconstructed image, the probability density function of the nerve distribution is calculated. First, the image needs to be segmented to separate each region. This can be achieved through threshold segmentation, edge detection, region growing and other methods. For each segmented region, features related to neural activity are extracted. These features may include the area, shape, texture, color intensity, etc. of the region. If the image is dynamic (such as a time series image), time series features such as amplitude changes and frequency can also be extracted. The probability density function of the nerve distribution can be calculated by the kernel density estimation (KDE) method. Kernel density estimation is a non-parametric statistical method used to estimate the probability density function of a random variable. Subsequently, the estimated probability density function is visualized for intuitive understanding and analysis, which can be achieved by drawing probability density curves, contour maps, etc.

[0069] Next, in the visualized image, the area with a probability density greater than a preset density threshold is marked to determine the nerve distribution of the target area tissue. In this embodiment, the preset density threshold can be determined according to the specific application background and requirements. For example, if the goal is to identify high-density nerve distribution areas, the threshold can be set relatively high so that only areas with significantly higher probability density are marked. Specifically, in the visualized image, the probability density value of each pixel is traversed, and those pixels greater than the preset density threshold are marked. The probability density image is converted into a binary image, in which the pixel values ​​greater than the threshold are set to 1 (or white), and the pixel values ​​less than or equal to the threshold are set to 0 (or black). The connected areas of the binarized image are marked to distinguish different high-density areas, and the marked high-density areas are superimposed on the original image, or marked with different colors to more intuitively observe the nerve distribution.

[0070] Visualizing images and analyzing results not only helps us understand the mechanism of muscle activity and the process of neuromuscular conduction, but also allows us to intuitively observe the active areas and distribution patterns of the neuromuscular system. At the same time, by real-time monitoring and analyzing electromyographic signals, we can promptly detect abnormal changes in muscle activity, providing new methods and means for motor control research, rehabilitation training evaluation, and other aspects.

[0071] In one implementation of this embodiment, calculating the probability density function of nerve distribution according to the fused reconstructed image includes: Obtain each data point in the fused reconstructed image; Use the preset kernel function to calculate the probability density for each data point; The probability density of each data point is superimposed to obtain a probability density function curve of the fused reconstructed image, and the probability density function curve is used to characterize the probability density function.

[0072] First, obtain each data point in the fused reconstructed image. In this embodiment, the fused reconstructed image is usually composed of a series of pixels or grid points. Each pixel or grid point represents a specific spatial position and has a value associated with the position (such as brightness, color intensity, etc.). By traversing each element of the image matrix, the value of each data point can be extracted. This can be achieved by programming, using loops or vectorized operations to access and process each element in the matrix. For each data point, in addition to its value, its position information may also need to be recorded.

[0073] The probability density is calculated for each data point using a preset kernel function. Specifically, the kernel function can be Gaussian kernel, Epanechnikov kernel, rectangular kernel, etc. The choice of kernel function depends on the characteristics of the data and the application requirements. Bandwidth is another important parameter in the kernel function, which controls the width of the kernel function, that is, the smoothing range around the data point. The bandwidth can be determined by cross-validation, Silverman's rule, and other methods. First, create an array of the same size as the data set to store the probability density of each data point. For each data point, calculate its distance from all other data points in the data set. For each distance, apply the preset kernel function to calculate the weight. The kernel function is usually a function with distance as the independent variable, and its value decreases as the distance increases. All calculated weights are added up and divided by the size of the data set (or a function of the bandwidth) to obtain the probability density of the data point.

[0074] The probability density of each data point is superimposed to obtain the probability density function curve of the fused reconstructed image. The probability density function curve is used to characterize the probability density function. That is to say, after calculating the probability density of each data point, these probability densities need to be superimposed. The probability density values ​​of each data point are summed up to obtain the total probability density of the entire data set. Secondly, in order to generate the probability density function curve, it is necessary to associate the superimposed probability density value with the corresponding data point position (such as pixel coordinates, eigenvalues, etc.). Drawing software (such as Matplotlib, Seaborn, etc.) can be used to draw these points into a continuous curve. This curve is the probability density function curve of the fused reconstructed image. The generated probability density function curve can be used to characterize the probability density distribution of the fused reconstructed image. By observing this curve, you can understand the distribution of image data, density peaks and valleys, and other characteristics.

[0075] By obtaining the probability density of each data point in the fused reconstructed image and superimposing the probability density function curve, we can have a deeper understanding of the characteristics of the image data, obtain a comprehensive understanding of the image data, and help improve the accuracy of nerve detection.

[0076] The present application also provides an electronic device, comprising: Memory; and A processor configured to retrieve instructions from a memory and to perform the following steps when executing the instructions: The target area tissue in the target surgical area is obtained, and a low-frequency stimulation current is output to the target tissue area using the integrated electrodes in the jaws. The neuroelectrophysiological signals in the target tissue area are transmitted to the nerve monitor through the nerve monitoring wire, and the value of the neuroelectrophysiological signals is displayed. The neuroelectrophysiological signals include impedance feedback and electromyographic signals. Based on the value of the neuroelectrophysiological signals, it is determined whether there is nerve distribution in the target tissue area. If there is no nerve distribution in the target tissue area, a high-frequency stimulation current is output using the integrated electrodes in the jaws to perform electrocoagulation on the target tissue area. If there is nerve distribution in the target tissue area, the nerve monitor displays impedance feedback in real time, and the intensity gradient value of the electromyographic signal determined by the chip control device is used to determine the nerve distribution of the target area tissue.

[0077] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0083] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0085] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An endoscopic nerve monitoring surgical instrument, characterized in that: The neuromonitoring surgical instrument comprises an integrated electrode, a jaw, a chip control device and a neuromonitoring wire, the integrated electrode is arranged in the jaw, the integrated electrode is connected to the neuromonitoring wire, the neuromonitoring wire is connected to the neuromonitor, and the neuromonitoring surgical instrument is connected to the electronic device; Among them, the electronic device is used to obtain the target area tissue within the target surgical area, use the integrated electrodes in the jaws to output a low-frequency stimulation current to the target tissue area, transmit the neuroelectrophysiological signals in the target tissue area to the nerve monitor through the nerve monitoring wire, and display the value of the neuroelectrophysiological signal. The neuroelectrophysiological signal includes impedance feedback and electromyographic signal, and based on the value of the neuroelectrophysiological signal, it is judged whether there is nerve distribution in the target tissue area. If there is no nerve distribution in the target tissue area, the integrated electrodes in the jaws are used to output a high-frequency stimulation current to perform electrocoagulation on the target tissue area. If there is nerve distribution in the target tissue area, the nerve monitor displays impedance feedback in real time, and the intensity gradient value of the electromyographic signal determined by the chip control device is used to determine the nerve distribution of the target area tissue.

2. The endoscopic nerve monitoring surgical instrument according to claim 1, characterized in that: The jaws include an upper jaw and a lower jaw, and a cutting head and an integrated electrode are arranged in the jaws. The integrated electrode includes a first electrode, a second electrode and a nerve detection electrode. The first electrode and the second electrode are respectively located at the upper jaw and the lower jaw of the jaws, and the nerve detection electrode is integrated in the first electrode or the second electrode. The cutting head is used to cut the target tissue area, the first electrode and the second electrode are used to perform electrocoagulation on the target tissue area, and the nerve detection electrode is used to detect the nerve distribution in the target tissue area.

3. The endoscopic nerve monitoring surgical instrument according to claim 2, characterized in that: A nerve monitoring bipolar circuit is provided in the jaws, and the nerve monitoring bipolar circuit includes a base, a collector and an emitter. The collector includes a first power supply, a first control switch and a first resistor. One end of the first resistor is connected to a first common point of the base and the emitter, the other end of the first resistor is connected to one end of the first control switch, and the other end of the first control switch is connected to the positive electrode of the first power supply. The base includes a second resistor, a third resistor and a second control switch. One end of the second control switch is connected to one end of the second resistor, and the other end of the second resistor is connected to the first common point of the collector and the emitter, one end of the third resistor is connected to one end of the second resistor, and the other end of the third resistor is connected to a second common point of the emitter and the ground point, and the emitter is connected to a second common point of the ground point and the other end of the third resistor.

4. The endoscopic nerve monitoring surgical instrument according to claim 2, characterized in that: A nerve stimulation current circuit is provided in the jaws, and the nerve stimulation current circuit includes an input circuit and an output circuit. The input circuit includes a fourth resistor, a fifth resistor, a capacitor, a relay, and an electromyographic signal module. One end of the fourth resistor is connected to the electromyographic signal module, the other end of the fourth resistor is connected to the capacitor, the other end of the capacitor is connected to the fifth resistor, the other end of the fifth resistor is connected to the coil of the relay, and the other end of the coil is connected to the electromyographic signal module. The output circuit includes a relay, a second power supply, a third control switch and a sixth resistor. The positive pole of the second power supply is connected to the third control switch, the other end of the third control switch is connected to the sixth resistor, the other end of the sixth resistor is connected to the normally closed contact of the relay, and the other end of the normally closed contact of the relay is connected to the negative pole of the second power supply.

5. The endoscopic nerve monitoring surgical instrument according to claim 3, characterized in that: The value of the neuroelectrophysiological signal includes an impedance feedback value and an electromyographic signal. The neuromonitoring surgical instrument is also used to determine whether the impedance feedback is a first preset impedance threshold value based on the impedance feedback value. If the impedance feedback is the first preset impedance threshold value, it is determined that there is nerve tissue in the target surgical area, and the integrated electrode stops outputting the high-frequency stimulation current and outputs the low-frequency stimulation current to achieve continuous alternating operations of nerve detection and electrocoagulation operation on the target area tissue in the target surgical area; If the impedance feedback is not the first preset impedance threshold, it is determined that there is no nerve tissue in the target surgical area, and the integrated electrode outputs a high-frequency stimulation current to perform an electrocoagulation operation on the target area tissue in the target surgical area.

6. The endoscopic nerve monitoring surgical instrument according to claim 4, characterized in that: The nerve monitoring surgical instrument is also used to use integrated electrodes to output high-frequency stimulation current to the target surgical area to perform electrocoagulation on the target area tissue within the target surgical area. The chip control device obtains the first impedance feedback of the target surgical area in real time. When the first impedance feedback reaches a second preset impedance threshold, the nerve stimulation current circuit is used to output the nerve stimulation current to the target surgical area, and the second impedance feedback is obtained in real time. Based on the second impedance feedback, the relative position of the target area tissue and the nerve tissue is determined. When the relative position reaches the preset nerve threshold, the output of high-frequency stimulation current to the target surgical area is stopped.

7. The endoscopic nerve monitoring surgical instrument according to claim 6, characterized in that: Based on the second impedance feedback, the relative position of the target area tissue and the nerve tissue is determined, including: Comparing the value of the second impedance feedback of the target area tissue with the impedance feedback in the preset database to determine the tissue type of the target area tissue; In the target surgical area, the position and impedance value of each electrode in the preset array electrode are obtained; The spatial distribution of impedance values ​​is constructed through the position and impedance value of each electrode; Through spatial distribution, use the preset drawing software to draw the impedance value topology map; Determine the gradient direction of impedance value change through the impedance value topology diagram; The relative position of the target area tissue and the neural tissue is determined based on the tissue type and gradient direction.

8. The endoscopic nerve monitoring surgical instrument according to claim 7, characterized in that: The nerve distribution of the target area tissue is determined by the real-time display of impedance feedback by the nerve monitor and the intensity gradient value of the electromyographic signal determined by the chip control device, including: Obtain historical impedance feedback and historical electromyographic signals within a preset time period; Based on historical impedance feedback, historical electromyographic signals and a preset time period, the impedance change rate and the electromyographic signal amplitude slope are calculated respectively; Based on the impedance change rate and the slope of the EMG signal amplitude, an impedance-EMG correlation model was constructed; Acquire the electromyographic signal within a preset time period in real time and calculate the amplitude slope of the electromyographic signal; Using the preset image processing technology, the amplitude slope of the electromyographic signal is converted into an amplitude slope distribution diagram; Marking the area in the amplitude slope distribution diagram that exceeds a preset amplitude slope threshold to obtain a high-frequency discharge area; Using the preset interpolation algorithm, the high-frequency discharge area is converted into a dynamic EMG thermal map; Extract the image features of the impedance value topology map and the dynamic EMG thermal map, and fuse the image features in the impedance-EMG correlation model to obtain the fused reconstructed image; Calculate the probability density function of nerve distribution based on the fused reconstructed image; Convert the probability density function into a visual image using preset visualization software; In the visualization image, the areas with probability density greater than the preset density threshold are marked to determine the nerve distribution of the target area tissue.

9. The endoscopic nerve monitoring surgical instrument according to claim 8, characterized in that: According to the fused reconstructed image, the probability density function of nerve distribution is calculated, including: Obtain each data point in the fused reconstructed image; Use the preset kernel function to calculate the probability density for each data point; The probability density of each data point is superimposed to obtain a probability density function curve of the fused reconstructed image, and the probability density function curve is used to characterize the probability density function.

10. An electronic device, characterized in that: include: Memory; as well as A processor configured to retrieve instructions from a memory and to perform the following steps when executing the instructions: The target area tissue in the target surgical area is obtained, and a low-frequency stimulation current is output to the target tissue area using the integrated electrodes in the jaws. The neuroelectrophysiological signals in the target tissue area are transmitted to the nerve monitor through the nerve monitoring wire, and the value of the neuroelectrophysiological signals is displayed. The neuroelectrophysiological signals include impedance feedback and electromyographic signals. Based on the value of the neuroelectrophysiological signals, it is determined whether there is nerve distribution in the target tissue area. If there is no nerve distribution in the target tissue area, a high-frequency stimulation current is output using the integrated electrodes in the jaws to perform electrocoagulation on the target tissue area. If there is nerve distribution in the target tissue area, the nerve monitor displays impedance feedback in real time, and the intensity gradient value of the electromyographic signal determined by the chip control device is used to determine the nerve distribution of the target area tissue.

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