Intelligent real-time nerve monitoring and early warning system and method based on spinal surgery robot

Abstract

The invention belongs to the field of spinal operation equipment, and discloses an intelligent real-time nerve monitoring and early warning system and method based on a spinal operation robot, the system comprises a spinal operation robot body, the spinal operation robot body is integrated with a processor and a mechanical arm execution unit, and the mechanical arm execution unit is electrically connected with the processor; the electromyographic signal acquisition unit is electrically connected with the processor; when a spinal operation is performed on a patient, the electromyographic signal acquisition unit is used for acquiring electromyographic signals of the patient and uploading the electromyographic signals to the processor; the processor is used for carrying out risk identification on the electromyographic signals to obtain a surgical risk result, generating a risk early warning and a surgical action adjusting instruction based on the surgical risk result and sending the surgical action adjusting instruction to the mechanical arm execution unit, and the mechanical arm execution unit adjusts surgical action parameters when responding to the surgical action adjusting instruction. The use safety and reliability of the spinal surgery robot can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of spinal surgery equipment, and particularly relates to an intelligent real-time nerve monitoring and early warning system and method based on a spinal surgery robot. Background Art

[0002] Spinal surgery is a surgical method for treating spinal-related diseases, including diseases of the cervical, thoracic, and lumbar vertebrae. The purpose of spinal surgery is to relieve pain, repair or stabilize the spinal structure, relieve nerve compression, correct spinal deformities, or treat spinal infections and tumors.

[0003] Traditional spinal surgeries are usually performed manually (by doctors). With the development of intelligence, spinal surgery robots have emerged in the existing technology. A spinal surgery robot is a high-tech medical device designed to assist or perform precise operations during spinal surgery. These robotic systems are usually controlled by doctors to improve the accuracy of surgery, reduce complications, shorten the recovery time, and enhance the overall surgical outcome.

[0004] A spinal surgery robot mainly consists of a robotic arm, a navigation system, and a control interface. Among them, the robotic arm is mainly used to perform surgical operations, such as drilling holes, placing implants, etc. The navigation system is mainly used to use advanced imaging technologies (such as CT or MRI) to provide a three-dimensional view of the spine and help doctors plan the surgical path.

[0005] A spinal surgery robot can operate with an accuracy of up to the millimeter level, reducing the risk of damage to surrounding healthy tissues. Moreover, the robotic arm is more stable than a human hand, which can reduce vibrations and errors during surgery.

[0006] Since there is still a risk of damaging the patient's nerves during the surgery, and the existing spinal surgery robots do not have the function of monitoring the patient, the safety and reliability of the existing spinal surgery robots are relatively low, and it is necessary to improve the safety and reliability of the use of spinal surgery robots. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent real-time nerve monitoring and early warning system and method based on a spinal surgery robot to solve the problem that due to the risk of damaging the patient's nerves during the surgery and the existing spinal surgery robots not having the function of monitoring the patient, the safety and reliability of the existing spinal surgery robots are relatively low.

[0008] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot, and the system includes: Spinal surgery robot body, the spinal surgery robot body is integrated with a processor and a robotic arm execution unit, and the robotic arm execution unit is electrically connected to the processor; Electromyogram signal acquisition unit, electrically connected to the processor; when performing spinal surgery on a patient, the electromyogram signal acquisition unit is used to acquire the patient's electromyogram signal and upload the electromyogram signal to the processor; The processor is used to: identify risks for the electromyogram signal to obtain a surgical risk result, generate a risk warning and a surgical action adjustment instruction based on the surgical risk result, send the surgical action adjustment instruction to the robotic arm execution unit, and the robotic arm execution unit adjusts the surgical action parameters when responding to the surgical action adjustment instruction, and the surgical action parameters at least include: surgical force, surgical path or action pause.

[0009] Preferably, the system further includes: a display unit, the display unit is electrically connected to the processor, and the display unit is used to visually display the surgical risk result, the risk warning and the surgical action parameters.

[0010] Preferably, the electromyogram signal includes: electromyogram signal and evoked potential signal; the electromyogram signal acquisition unit includes: an electromyogram signal acquisition sub-unit and an evoked potential signal acquisition sub-unit; The electromyogram signal acquisition sub-unit includes: a first surface electrode, a first amplifier, a first filter and a first analog-to-digital converter, the output end of the first surface electrode is electrically connected to the input end of the first amplifier, the output end of the first amplifier is electrically connected to the input end of the first filter, the output end of the first filter is electrically connected to the input end of the first analog-to-digital converter, and the output end of the first analog-to-digital converter is electrically connected to the IO port of the processor; The evoked potential signal acquisition sub-unit includes: a stimulator, a second surface electrode, a second amplifier, a second filter, an averager and a second analog-to-digital converter, the controlled end of the stimulator is electrically connected to the IO port of the processor, the output end of the second surface electrode is electrically connected to the input end of the second amplifier, the output end of the second amplifier is electrically connected to the input end of the averager, the output end of the averager is electrically connected to the input end of the second analog-to-digital converter, and the output end of the second analog-to-digital converter is electrically connected to the IO port of the processor.

[0011] Preferably, the electromyogram signal acquisition unit is further used to: acquire an electromyogram reference sample of the patient before performing spinal surgery on the patient; The steps for the processor to perform risk identification on the electromyogram signal to obtain a surgical risk result include: Construct a set to be classified based on the electromyogram reference sample and the electromyogram signal; Cluster the set to be classified based on the K-means clustering algorithm to obtain several classification clusters and the cluster centers corresponding to each classification cluster; In each classification cluster, calculate the distance between all the electromyography signals in the cluster and the cluster center of the cluster; When the distance between any electromyography signal and the cluster center of the cluster exceeds a preset distance, mark the electromyography signal as abnormal to obtain several abnormal electromyography signals; wherein, the preset distance is determined by the average within-cluster distance of the cluster; Perform abnormal level classification on each abnormal electromyography signal to obtain the abnormal level of each abnormal electromyography signal, and use each abnormal electromyography signal and the corresponding abnormal level as the surgical risk result.

[0012] In a second aspect, the present invention provides an intelligent real-time nerve monitoring and early warning method based on a spinal surgery robot, which is implemented based on the above-mentioned intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot. The method includes: Obtain the electromyography signals during the spinal surgery on the patient; Perform risk identification on the electromyography signals to obtain a surgical risk result; Generate a risk warning and a surgical action adjustment instruction based on the surgical risk result, and send the surgical action adjustment instruction to the robotic arm execution unit of the intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot. The robotic arm execution unit adjusts the surgical action parameters when responding to the surgical action adjustment instruction. The surgical action parameters at least include: surgical force, surgical path, or action pause; Visually display the surgical risk result, risk warning, and surgical action parameters.

[0013] Preferably, before performing risk identification on the electromyography signals, the method further includes: preprocessing the electromyography signals, and the preprocessing at least includes: data cleaning, standardization processing, and normalization processing.

[0014] Preferably, performing risk identification on the electromyography signals to obtain a surgical risk result includes: Obtain an electromyography reference sample before the spinal surgery on the patient, and construct a classification set based on the electromyography reference sample; Add the obtained electromyography signals to the classification set to obtain a set to be classified; Cluster the set to be classified based on the K-means clustering algorithm to obtain several classification clusters and the cluster centers corresponding to each classification cluster; In each classification cluster, calculate the distance between all the electromyography signals in the cluster and the cluster center of the cluster; When the distance between any myoelectric signal and the cluster center of the classification cluster exceeds a preset distance, mark the myoelectric signal as abnormal to obtain a number of abnormal myoelectric signals; wherein, the preset distance is determined by the average intra-cluster distance of the classification cluster; Perform abnormal level classification on each abnormal myoelectric signal to obtain the abnormal level of each abnormal myoelectric signal, and use each abnormal myoelectric signal and the corresponding abnormal level as the surgical risk result.

[0015] Preferably, the abnormal levels include: level-1 abnormality, level-2 abnormality, and level-3 abnormality, where the severity of level-1 abnormality, level-2 abnormality, and level-3 abnormality gradually decreases.

[0016] Preferably, performing abnormal level classification on each abnormal myoelectric signal to obtain the abnormal level of each abnormal myoelectric signal includes: Extract the sampling moments corresponding to each abnormal myoelectric signal; Judge whether the sampling moments corresponding to each abnormal myoelectric signal are continuous. If so, classify the abnormal level of each abnormal myoelectric signal as level-1 abnormality; If the sampling moments corresponding to each abnormal myoelectric signal are not continuous, calculate the interval duration between two adjacent abnormal myoelectric signals; Judge whether all the interval durations are the same. If not, classify the abnormal level of each abnormal myoelectric signal as level-2 abnormality. If so, classify the abnormal level of each abnormal myoelectric signal as level-3 abnormality.

[0017] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned intelligent real-time nerve monitoring and early warning method based on a spinal surgical robot.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned intelligent real-time nerve monitoring and early warning method based on a spinal surgical robot.

[0019] Beneficial effects: The spinal robot of the present invention is deployed with a myoelectric signal acquisition unit. The myoelectric signal acquisition unit can be used to collect the myoelectric signals of patients in real time, and then upload the myoelectric signals to the processor of the spinal robot. The processor analyzes the myoelectric signals, identifies the myoelectric signals with risks, and simultaneously generates risk warnings and surgical action adjustment instructions, which can timely warn of abnormalities in myoelectric signals; and uses the robotic arm execution unit to adjust the surgical action parameters when responding to the surgical action adjustment instructions, avoiding damage to the nerves of patients and improving the use safety and reliability of the spinal surgical robot. Description of the Drawings

[0020] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and form a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the accompanying drawings: Figure 1 is a block diagram of an intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot provided by an embodiment of the present invention; Figure 2 is a flowchart of an intelligent real-time nerve monitoring and early warning method based on a spinal surgery robot provided by an embodiment of the present invention. Specific Embodiments

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the present invention in combination with the accompanying drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. It should be noted here that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation to the present invention.

[0022] Embodiment 1 Figure 1 is a block diagram of an intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot provided by an embodiment of the present invention. As Figure 1 shown, an intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot, the system includes: a spinal surgery robot body and an electromyogram signal acquisition unit.

[0023] The spinal surgery robot body is integrated with a processor and a robotic arm execution unit. The robotic arm execution unit is electrically connected to the processor. The hardware structure of the spinal surgery robot body is prior art and will not be elaborated one by one in this embodiment; The electromyogram signal acquisition unit is electrically connected to the processor; when performing spinal surgery on a patient, the electromyogram signal acquisition unit is used to collect the electromyogram signal of the patient and upload the electromyogram signal to the processor; The processor is used for: identifying risks for the electromyogram signal to obtain a surgical risk result, generating a risk warning and a surgical action adjustment instruction based on the surgical risk result, sending the surgical action adjustment instruction to the robotic arm execution unit, and the robotic arm execution unit adjusts the surgical action parameters when responding to the surgical action adjustment instruction. The surgical action parameters at least include: surgical force, surgical path or action pause.

[0024] As a further optimization of this embodiment, the electromyography (EMG) signal includes: electromyogram (EMG) signal and evoked potential signal; the EMG signal acquisition unit includes: an EMG signal acquisition subunit and an evoked potential signal acquisition subunit; Among them, the EMG signal acquisition subunit includes: a first surface electrode, a first amplifier, a first filter, and a first analog-to-digital converter. The output end of the first surface electrode is electrically connected to the input end of the first amplifier. The output end of the first amplifier is electrically connected to the input end of the first filter. The output end of the first filter is electrically connected to the input end of the first analog-to-digital converter. The output end of the first analog-to-digital converter is electrically connected to the IO port of the processor.

[0025] In this embodiment, during the operation, if the patient experiences muscle twitching, spasm, and an increase in muscle fatigue, etc., it will cause changes in the frequency or amplitude of the EMG signal. For example: 1. During muscle twitching or spasm, the frequency of the EMG signal will increase because muscle fibers are activated rapidly and frequently in a short period of time; 2. As muscle fatigue increases, in order to maintain force output, the muscle may recruit more motor units at a higher frequency; 3. In neuromuscular diseases, the activation frequency of the muscle may decrease because the recruitment of motor units is reduced or the conduction speed of muscle fibers slows down.

[0026] Therefore, the EMG signal of the patient can be collected through the first surface electrode, and then the first amplifier and the first filter are used to amplify and filter the EMG signal. The processed signal is then converted from an analog signal to a digital signal by the first analog-to-digital converter, that is, a digital EMG signal. After the digital EMG signal is sent to the processor, the processor analyzes and processes it to obtain the EMG signal diagram of the patient.

[0027] The evoked potential signal acquisition subunit includes: a stimulator, a second surface electrode, a second amplifier, a second filter, an averager, and a second analog-to-digital converter. The controlled end of the stimulator is electrically connected to the IO port of the processor. The output end of the second surface electrode is electrically connected to the input end of the second amplifier. The output end of the second amplifier is electrically connected to the input end of the averager. The output end of the averager is electrically connected to the input end of the second analog-to-digital converter. The output end of the second analog-to-digital converter is electrically connected to the IO port of the processor.

[0028] In this embodiment, the stimulator is used to apply electrical stimulation to the nervous system to induce a response; the second surface electrode can record the myoelectric response of the patient, that is, the evoked potential signal, which is also amplified and filtered by the second amplifier and the second filter; since the evoked potential signal is usually very weak, it is necessary to superimpose and average the signals through multiple stimulations and recordings to extract the waveform of the clear evoked potential signal; therefore, the averager superimposes and averages the signals through multiple stimulations and recordings to obtain the processed evoked potential signal; the second analog-to-digital converter then performs analog-to-digital conversion on the processed evoked potential signal, converts the analog evoked potential signal into a digital evoked potential signal, and sends the digital evoked potential signal to the processor, and the processor analyzes and processes the digital evoked potential signal to obtain the evoked potential signal map of the patient.

[0029] As a further optimization of this embodiment, the myoelectric signal acquisition unit is further configured to: collect the myoelectric reference sample of the patient before performing spinal surgery on the patient; therefore, the steps for the processor to perform risk identification on the myoelectric signal to obtain the surgical risk result include: constructing a set to be classified based on the myoelectric reference sample and the myoelectric signal; clustering the set to be classified based on the K-means clustering algorithm to obtain several classification clusters and the cluster centers corresponding to each classification cluster; in each classification cluster, calculate the distance between all the myoelectric signals in the classification cluster and the cluster center of the classification cluster; when the distance between any myoelectric signal and the cluster center of the classification cluster exceeds the preset distance, mark the myoelectric signal as abnormal to obtain several abnormal myoelectric signals; where the preset distance is determined by the average within-cluster distance of the classification cluster; perform abnormal level classification on each abnormal myoelectric signal to obtain the abnormal level of each abnormal myoelectric signal, and use each abnormal myoelectric signal and the corresponding abnormal level as the surgical risk result.

[0030] As a further optimization of this embodiment, the system further includes: a display unit, the display unit is electrically connected to the processor, and the display unit is used to visually display the surgical risk result, the risk warning, and the surgical operation parameters.

[0031] In this embodiment, the display unit can be a liquid crystal display, and the liquid crystal display is used to visually display the surgical risk result, the risk warning, and the surgical operation parameters; it is convenient for medical staff to view the relevant parameters during the operation.

[0032] In this embodiment, the spinal robot is equipped with an electromyogram (EMG) signal acquisition unit, which can be used to collect the patient's EMG signals in real time. Then, the EMG signals are uploaded to the processor of the spinal robot, and the processor analyzes the EMG signals to identify risky EMG signals. At the same time, a risk warning and a surgical action adjustment instruction are generated, which can timely warn of the abnormality of the EMG signals. Moreover, the robotic arm execution unit adjusts the surgical action parameters when responding to the surgical action adjustment instruction, avoiding damage to the patient's nerves and improving the safety and reliability of the spinal surgical robot.

[0033] Embodiment 2 Figure 2 is a flowchart of an intelligent real-time nerve monitoring and warning method based on a spinal surgical robot provided by an embodiment of the present invention. As Figure 2 shown, this embodiment provides an intelligent real-time nerve monitoring and warning method based on a spinal surgical robot. The method is implemented based on the intelligent real-time nerve monitoring and warning system based on the spinal surgical robot in Embodiment 1. The method mainly runs in the processor. The method includes: Step S10: Obtain the EMG signals when performing spinal surgery on the patient; in this embodiment, the EMG signals of the patient during spinal surgery can be collected by using the EMG signal acquisition unit.

[0034] Step S20: Perform risk identification on the EMG signals to obtain a surgical risk result.

[0035] In this embodiment, before performing risk identification on the EMG signals, the method further includes: preprocessing the EMG signals. The preprocessing at least includes: data cleaning, standardization processing, and normalization processing. Through preprocessing, high-frequency and low-frequency noises irrelevant to nerve signals in the EMG signals can be removed, and at the same time, the signals are standardized to a unified range for easy comparison and analysis.

[0036] After completing the preprocessing of the EMG signals, risk identification of the EMG signals is started. The specific identification steps are as follows: Step S201: Obtain an electromyogram (EMG) reference sample before performing spinal surgery on a patient, and construct a classification set based on the EMG reference sample. In this embodiment, since the actual EMG signals of each patient are different, the traditional method of directly classifying EMG signals using a fixed threshold indicates that there is an abnormality in the patient's physical condition when the threshold is exceeded. However, in actual practice, due to differences in the patient's body, the muscle structure and size of each person are different, and the EMG signals of each person are different. Therefore, the traditional fixed threshold cannot accurately judge the EMG signals of each patient. Therefore, in this application, the EMG signals of the patient before surgery are collected as reference samples, and the EMG reference samples at this time are sample data collected when the patient's physical characteristics are relatively good.

[0037] Step S202: Add the obtained EMG signals to the classification set to obtain a set to be classified.

[0038] Step S203: Cluster the set to be classified based on the K-means clustering algorithm to obtain several classification clusters and the cluster centers corresponding to each classification cluster.

[0039] Step S204: In each classification cluster, calculate the distance between all EMG signals in the classification cluster and the cluster center of the classification cluster.

[0040] Step S205: When the distance between any EMG signal and the cluster center of the classification cluster exceeds a preset distance, mark the EMG signal as abnormal to obtain several abnormal EMG signals; wherein, the preset distance is determined by the average within-cluster distance of the classification cluster.

[0041] Step S206: Classify the abnormal levels of each abnormal EMG signal to obtain the abnormal levels of each abnormal EMG signal, and use each abnormal EMG signal and the corresponding abnormal level as the surgical risk result.

[0042] In this embodiment, there are EMG signals before surgery and EMG signals during surgery in the set to be classified. The EMG signals in the two different surgical processes are mixed together, and clustering operations are performed through the K-means clustering algorithm to determine the threshold for judging whether the EMG signals are abnormal. This threshold is not fixed and is dynamically adjusted according to the differences of patients, improving the flexibility, practicability, and accuracy of abnormal judgment.

[0043] In this embodiment, there are differences in the severity of EMG signal abnormalities. For different severities of abnormalities, different adjustments need to be made to the surgical operations of the spinal robot. Therefore, as a further optimization of this embodiment, the abnormal levels include: level-one abnormality, level-two abnormality, and level-three abnormality, where the severity of level-one abnormality, level-two abnormality, and level-three abnormality gradually decreases.

[0044] Therefore, for step S206, the specific steps for classifying the abnormal levels of each abnormal electromyogram signal are as follows: Step a10: Extract the sampling moments corresponding to each abnormal electromyogram signal.

[0045] Step a20: Determine whether the sampling moments corresponding to each abnormal electromyogram signal are continuous. If so, classify the abnormal level of each abnormal electromyogram signal as a first-level abnormality. In the case of a first-level abnormality, it indicates that the patient's physical signs are rapidly deteriorating, and the operation needs to be paused in a timely manner.

[0046] Step a30: If the sampling moments corresponding to each abnormal electromyogram signal are not continuous, calculate the interval duration between two adjacent abnormal electromyogram signals.

[0047] Step a40: Determine whether all the interval durations are the same. If not, classify the abnormal level of each abnormal electromyogram signal as a second-level abnormality. If so, classify the abnormal level of each abnormal electromyogram signal as a third-level abnormality; in this embodiment, when all the interval durations are the same, it indicates that the fluctuation of the patient's electromyogram signal is stable, and the operation process of the patient needs to be paid attention to in a timely manner. If an abnormality occurs, the operation should be paused in a timely manner or the operation force and operation path should be adjusted; when the interval durations are different, it indicates that the patient's characteristics are continuously deteriorating, and the operation force and operation path need to be adjusted in a timely manner.

[0048] Step S30: Generate a risk warning and a surgical action adjustment instruction based on the surgical risk result, and send the surgical action adjustment instruction to the robotic arm execution unit of the intelligent real-time nerve monitoring and warning system based on the spinal surgical robot. The robotic arm execution unit adjusts the surgical action parameters when responding to the surgical action adjustment instruction. The surgical action parameters at least include: surgical force, surgical path, or action pause.

[0049] Step S40: Visually display the surgical risk result, risk warning, and surgical action parameters.

[0050] The present invention uses an electromyogram signal acquisition unit to collect the patient's electromyogram signal in real time, and then uploads the electromyogram signal to the processor of the spinal robot. The processor analyzes the electromyogram signal, identifies the electromyogram signal with risks, and simultaneously generates a risk warning and a surgical action adjustment instruction, which can timely warn of the abnormality of the electromyogram signal; and uses the robotic arm execution unit to adjust the surgical action parameters when responding to the surgical action adjustment instruction, avoiding damage to the patient's nerves and improving the use safety and reliability of the spinal surgical robot.

[0051] Embodiment III This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the intelligent real-time nerve monitoring and early warning method based on the spinal surgery robot in Embodiment 2.

[0052] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the intelligent real-time nerve monitoring and early warning method based on the spinal surgery robot in Embodiment 2.

[0053] In the present invention, an electromyogram signal acquisition unit is used to collect the electromyogram signals of a patient in real time, and then the electromyogram signals are uploaded to the processor of the spinal robot. The processor analyzes the electromyogram signals, identifies the electromyogram signals with risks, and simultaneously generates a risk early warning and a surgical action adjustment instruction, which can timely warn of the abnormality of the electromyogram signals; and the robotic arm execution unit adjusts the surgical action parameters when responding to the surgical action adjustment instruction, avoiding damage to the nerves of the patient and improving the use safety and reliability of the spinal surgery robot.

[0054] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.

[0055] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0056] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot, characterized in that: The system comprises: A spinal surgery robot body, wherein the spinal surgery robot body is integrated with a processor and a mechanical arm execution unit, and the mechanical arm execution unit is electrically connected to the processor; An electromyographic signal acquisition unit is electrically connected to the processor; when performing spinal surgery on a patient, the electromyographic signal acquisition unit is used to collect the patient's electromyographic signals in real time and upload the electromyographic signals to the processor; The processor is used to: identify risks of electromyographic signals to obtain surgical risk results, generate risk warnings and surgical action adjustment instructions based on the surgical risk results, and send the surgical action adjustment instructions to the robotic arm execution unit. The robotic arm execution unit adjusts surgical action parameters in response to the surgical action adjustment instructions, and the surgical action parameters include at least: surgical force, surgical path or action pause.

2. The intelligent real-time nerve monitoring and early warning system based on the spinal surgery robot according to claim 1 is characterized in that: The system further includes: a display unit, which is electrically connected to the processor and is used for visually displaying surgical risk results, risk warnings, and surgical action parameters.

3. The intelligent real-time nerve monitoring and early warning system based on spinal surgery robot according to claim 1 is characterized in that: The electromyographic signal includes: an electromyographic signal and an evoked potential signal; the electromyographic signal acquisition unit includes: an electromyographic signal acquisition subunit and an evoked potential signal acquisition subunit; The electromyography signal acquisition subunit includes: a first surface electrode, a first amplifier, a first filter and a first analog-to-digital converter, the output end of the first surface electrode is electrically connected to the input end of the first amplifier, the output end of the first amplifier is electrically connected to the input end of the first filter, the output end of the first filter is electrically connected to the input end of the first analog-to-digital converter, and the output end of the first analog-to-digital converter is electrically connected to the IO port of the processor; The evoked potential signal acquisition subunit includes: a stimulator, a second surface electrode, a second amplifier, a second filter, an averager and a second analog-to-digital converter. The controlled end of the stimulator is electrically connected to the IO port of the processor, the output end of the second surface electrode is electrically connected to the input end of the second amplifier, the output end of the second amplifier is electrically connected to the input end of the averager, the output end of the averager is electrically connected to the input end of the second analog-to-digital converter, and the output end of the second analog-to-digital converter is electrically connected to the IO port of the processor.

4. The intelligent real-time nerve monitoring and early warning system based on spinal surgery robot according to claim 1 is characterized in that: The electromyographic signal acquisition unit is also used to: acquire electromyographic reference samples of the patient before performing spinal surgery on the patient; The processor performs risk identification on the electromyographic signal to obtain the surgical risk result, including: Construct a set to be classified based on electromyographic reference samples and electromyographic signals; Based on the K-means clustering algorithm, the classification set is clustered to obtain several classification clusters and the cluster centers corresponding to each classification cluster; In each classification cluster, the distance between all the electromyographic signals in the classification cluster and the cluster center of the classification cluster is calculated; When the distance between any one electromyographic signal and the cluster center of the classification cluster exceeds a preset distance, the electromyographic signal is marked as abnormal, and a plurality of abnormal electromyographic signals are obtained; wherein the preset distance is determined by the average distance within the cluster of the classification cluster; Each abnormal electromyographic signal is classified into an abnormal grade to obtain the abnormal level of each abnormal electromyographic signal, and each abnormal electromyographic signal and the abnormal level corresponding to each abnormal electromyographic signal are used as the surgical risk result.

5. An intelligent real-time nerve monitoring and early warning method based on a spinal surgery robot, the method is implemented based on the intelligent real-time nerve monitoring and early warning system based on a spinal surgery robot according to any one of claims 1 to 4, characterized in that: The method comprises: Acquiring electromyographic signals while performing spinal surgery on patients; Conduct risk identification on electromyographic signals and obtain surgical risk results; Generate risk warning and surgical action adjustment instructions based on surgical risk results, send the surgical action adjustment instructions to the robot arm execution unit of the intelligent real-time nerve monitoring and early warning system based on the spinal surgery robot, and the robot arm execution unit adjusts the surgical action parameters in response to the surgical action adjustment instructions, and the surgical action parameters at least include: surgical force, surgical path or action pause; Visualize surgical risk results, risk warnings, and surgical action parameters.

6. The intelligent real-time nerve monitoring and early warning method based on spinal surgery robot according to claim 5 is characterized in that: Before performing risk identification on the electromyographic signal, the method further includes: preprocessing the electromyographic signal, and the preprocessing at least includes: data cleaning, standardization processing and normalization processing.

7. The intelligent real-time nerve monitoring and early warning method based on spinal surgery robot according to claim 5 is characterized in that: Risk identification is performed on electromyographic signals to obtain surgical risk results, including: Obtaining electromyographic reference samples before performing spinal surgery on a patient, and constructing a classification set based on the electromyographic reference samples; Add the acquired electromyographic signal to the classification set to obtain a set to be classified; Based on the K-means clustering algorithm, the classification set is clustered to obtain several classification clusters and the cluster centers corresponding to each classification cluster; In each classification cluster, the distance between all the electromyographic signals in the classification cluster and the cluster center of the classification cluster is calculated; When the distance between any one electromyographic signal and the cluster center of the classification cluster exceeds a preset distance, the electromyographic signal is marked as abnormal, and a plurality of abnormal electromyographic signals are obtained; wherein the preset distance is determined by the average distance within the cluster of the classification cluster; Each abnormal electromyographic signal is classified into an abnormal grade to obtain the abnormal level of each abnormal electromyographic signal, and each abnormal electromyographic signal and the abnormal level corresponding to each abnormal electromyographic signal are used as the surgical risk result.

8. The intelligent real-time nerve monitoring and early warning method based on spinal surgery robot according to claim 7 is characterized in that: The abnormality levels include: level one abnormality, level two abnormality and level three abnormality, wherein the severity of the abnormality of level one abnormality, level two abnormality and level three abnormality gradually decreases.

9. The intelligent real-time nerve monitoring and early warning method based on spinal surgery robot according to claim 8 is characterized in that: Each abnormal electromyographic signal is classified into abnormal levels to obtain the abnormal level of each abnormal electromyographic signal, including: Extracting the sampling time corresponding to each abnormal electromyographic signal; Determine whether the sampling moments corresponding to the abnormal electromyographic signals are continuous, and if so, classify the abnormality level of each abnormal electromyographic signal into a first-level abnormality; If the sampling moments corresponding to the abnormal electromyographic signals are not continuous, the interval between two adjacent abnormal electromyographic signals is calculated; It is determined whether all the interval durations are the same. If not, the abnormality level of each abnormal electromyographic signal is classified as a secondary abnormality; if so, the abnormality level of each abnormal electromyographic signal is classified as a tertiary abnormality.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the intelligent real-time nerve monitoring and early warning method based on a spinal surgery robot described in any one of claims 5 to 9 is implemented.

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