CN112656510A - Spinal surgery robot puncture early warning method and system based on electromyographic signals - Google Patents
Spinal surgery robot puncture early warning method and system based on electromyographic signalsDownload PDFInfo
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Abstract
Translated fromChinese
Description
Technical Field
The invention relates to the technical field of robot-assisted surgery, in particular to a spinal surgery robot puncture early warning method and system based on electromyographic signals.
Background
The spinal surgery has the characteristics of complex anatomical structure of the operative region, dense and distributed important nerves and the like, so that higher surgical risk is caused. With the development of science and technology, image navigation and robot technology can assist a doctor to complete long-time fine operation more efficiently in spinal surgery by virtue of the advantages of accurate positioning and stable holding, and the error rate is reduced.
The inventor finds that although positioning navigation in the current spine surgery robot operation mainly depends on image information and is accurate in positioning bone tissues, the method lacks effective implementation detection means for important nerves and blood vessels in soft tissues, and the nerve root position cannot be timely and effectively detected in the puncturing process, so that nerve injury cannot be effectively avoided, and great risk exists in the robot-assisted spine surgery operation.
In addition, the intraoperative neuroelectrophysiological monitoring is a necessary means for reducing the complications of the spinal surgery and reducing the surgical risk, physiological signals pre-judged by intraoperative neuro-monitoring are timely fed back to the robot, and a corresponding feedback control strategy is formulated, so that the safety and the autonomy of the surgical robot are greatly improved, the workload of doctors is further shared, and the surgical efficiency is improved; however, relevant reports for applying intraoperative nerve monitoring information to robot feedback control are not seen at home and abroad at present.
Disclosure of Invention
In order to solve the problems, the invention provides a spinal surgery robot puncture early warning method and system based on electromyographic signals.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a spinal surgery robot puncture early warning method based on electromyographic signals, which comprises the following steps:
acquiring an electromyographic signal generated after electrical stimulation is applied to a puncture needle;
adopting a pre-constructed early warning signal classifier to obtain the distance between the puncture needle and the nerve tissue for the electromyographic signals, and judging the safety level of the puncture area where the puncture needle is positioned;
and sending out an early warning signal according to the safety level, and feeding back a puncture path control instruction to the spinal surgery robot.
In a second aspect, the present invention provides a spinal surgery robot puncture early warning system based on electromyographic signals, comprising:
the data acquisition module is used for acquiring electromyographic signals generated after the electric stimulation is applied to the puncture needles;
the judgment module is used for obtaining the distance between the puncture needle and the nerve tissue by adopting a pre-constructed early warning signal classifier on the electromyographic signals and judging the safety level of the puncture area where the puncture needle is positioned according to the distance;
and the early warning and feedback module is used for sending out early warning signals according to the safety level and feeding back a puncture path control instruction to the spinal surgery robot.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the invention provides a spinal surgery robot puncture early warning platform based on an electromyographic signal, which comprises an electromyographic signal acquisition device, a puncture early warning system and a spinal surgery robot, wherein the puncture early warning system is used for monitoring the puncture of a spinal surgery robot; the electromyographic signal acquisition device is used for acquiring generated electromyographic signals after applying electrical stimulation to the puncture needle and sending the generated electromyographic signals to the puncture early warning system; the puncture early warning system obtains the safety level of a puncture area where the puncture needle is located according to the electromyographic signals, sends out early warning signals according to the safety level and feeds back a puncture path control command to the spinal surgery robot; and planning the puncture path by the spinal surgery robot according to the puncture path control instruction.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, a training sample library of the nerve injury early warning classifier based on the electromyographic signals is constructed, the early warning signal classifier is obtained through training of the training sample library, the function of classifying the safe distance between a surgical instrument and a nerve root based on the electromyographic signals is realized, the problem that the position of nerve tissue cannot be positioned only by image information in a robot-assisted spinal puncture operation is solved, the nerve injury in the puncture process is avoided, the risk of sequelae is reduced, and the safety of the robot-assisted spinal operation is effectively improved.
The nerve injury safety early warning classifier based on the electromyographic signals is integrated into a spine minimally invasive surgery robot system, the safety distance between a puncture needle and a nerve root can be effectively detected, the nerve injury can be effectively early warned through the electromyographic signals, the early warning is sent out when the distance between the puncture needle and nerve tissues exceeds the safety distance through the combination with the spine minimally invasive surgery machine, and a feedback control instruction is sent to the robot, so that the robot replans the puncture path, and the safety of the robot for assisting the spine surgery is improved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart of a method provided in example 1 of the present invention;
fig. 2 is a diagram of an implementation of an early warning signal classifier provided in embodiment 1 of the present invention;
fig. 3 is a schematic view of a spinal surgery robot puncture early warning platform based on electromyographic signals according to embodiment 3 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the embodiment provides a spinal surgery robot puncture early warning method based on an electromyographic signal, which includes:
s1: acquiring an electromyographic signal generated after electrical stimulation is applied to a puncture needle;
s2: adopting a pre-constructed early warning signal classifier to obtain the distance between the puncture needle and the nerve tissue for the electromyographic signals, and judging the safety level of the puncture area where the puncture needle is positioned;
s3: and sending out an early warning signal according to the safety level, and feeding back a puncture path control instruction to the spinal surgery robot.
In the step S1, in the needle insertion process of the spinal surgery robot assisted spinal puncture surgery, quantitative electrical stimulation is applied to the tip of the puncture needle according to a certain frequency, and corresponding myoelectric signals are collected;
preferably, the electromyographic signals collected by the electromyographic signal collecting device are obtained; the myoelectric signal acquisition device is a myoelectric signal monitor which is generally applied clinically, has basic signal preprocessing functions of signal acquisition, filtering, amplification and the like, is stuck to the body surface position of corresponding muscle according to a spinal surgery part, and applies electric stimulation according to a certain frequency in the process of inserting a spinal surgery robot puncture needle to acquire corresponding myoelectric signals.
As shown in fig. 2, in step S2, a sufficient amount of samples are collected through animal and clinical experiments, a sample library is established by using an artificial labeling method, and an early warning signal classifier is trained by using a machine learning method;
the early warning signal classifier obtains the distance between the puncture needle and the nerve tissue through the electromyographic signal characteristics, judges whether the distance belongs to a safety distance, and obtains the safety level of the current position of the puncture needle.
Preferably, the input signal characteristics of the training sample library are the conventional characteristics of electromyographic monitoring such as amplitude, latency and wavelength of the electromyographic signals, and the output signals are the classification of safety levels such as 'safety', 'warning' and 'danger';
it is understood that the electromyographic signal characteristics and the security level classification can be further increased and refined according to the sample size expansion.
Preferably, the early warning signal classifier generally refers to a method for judging the position relationship of the charged surgical instrument and important nerve tissues such as nerve roots and the like through the characteristics of the electromyographic signals, and a specific classifier training method can be improved along with the technical progress.
In the step S3, when the distance between the puncture needle and the nerve tissue exceeds the safe distance, an early warning signal is sent out, and a control instruction is fed back to the spinal surgery robot, so that the spinal surgery robot stops or changes the puncture path, thereby avoiding nerve injury and improving the safety of the robot-assisted spinal surgery.
Preferably, the spine surgical robot system is a surgical robot system specially used for auxiliary positioning of spine surgery, and the surgical robot system is generally composed of binocular stereo image navigation and six-degree-of-freedom machinery, and can hold a surgical instrument to a specified position under guidance of the image navigation in the surgery according to a surgical plan planned before the surgery.
Preferably, the surgical robot control platform comprises robot image navigation and online control software, is connected with a controller of the robot, and realizes the online path planning of the robot through image navigation information, manual interaction or other feedback information, thereby completing the designated action under the planned path.
According to the embodiment, the safety level of the puncture path of the puncture needle is judged according to the early warning signal classifier, and a safety feedback control method is established by combining the prediction of a surgical robot navigation system on the motion trend of the puncture needle, so that unnecessary nerve injury caused in the robot-assisted puncture process is effectively avoided.
Example 2
The embodiment provides a spinal surgery robot puncture early warning system based on flesh electrical signal, includes:
the data acquisition module is used for acquiring electromyographic signals generated after the electric stimulation is applied to the puncture needles;
the judgment module is used for obtaining the distance between the puncture needle and the nerve tissue by adopting a pre-constructed early warning signal classifier on the electromyographic signals and judging the safety level of the puncture area where the puncture needle is positioned according to the distance;
and the early warning and feedback module is used for sending out early warning signals according to the safety level and feeding back a puncture path control instruction to the spinal surgery robot.
Specifically, the method comprises the following steps: in the data acquisition module, in the needle inserting process of the spinal surgery robot-assisted spinal puncture surgery, quantitative electrical stimulation is applied to the tip of a puncture needle according to a certain frequency, and corresponding myoelectric signals are acquired;
preferably, the data acquisition module acquires the electromyographic signals acquired by the electromyographic signal acquisition device, the electromyographic signal acquisition device is pasted on the body surface position of the corresponding muscle according to the spinal surgery position, and electrical stimulation is applied according to a certain frequency in the needle inserting process of the spinal surgery robot puncture needle to acquire the corresponding electromyographic signals.
In the judging module, enough sample amount is collected through animal and clinical experiments, a sample library is established by adopting an artificial labeling method, an early warning signal classifier is trained through a machine learning method, the early warning signal classifier obtains the distance between the puncture needle and the nerve tissue through the characteristics of the electromyographic signals, and judges whether the distance belongs to the safe distance or not to obtain the safety level of the current position of the puncture needle.
Preferably, the judging module comprises a training submodule, input signal characteristics of a training sample library in the training submodule are conventional characteristics of electromyographic monitoring such as amplitude, latency and wavelength of the electromyographic signals, and output signals are classification of safety levels such as 'safety', 'warning' and 'danger';
it is understood that the electromyographic signal characteristics and the security level classification can be further increased and refined according to the sample size expansion.
Preferably, the early warning signal classifier generally refers to a method for judging the position relationship of the charged surgical instrument and important nerve tissues such as nerve roots and the like through the characteristics of the electromyographic signals, and a specific classifier training method can be improved along with the technical progress.
In the early warning and feedback module, when the distance between the puncture needle and the nerve tissue exceeds the safe distance, an early warning signal is sent out, and a control instruction is fed back to the spinal surgery robot, so that the spinal surgery robot stops or changes the puncture path, the nerve injury is avoided, and the safety of the robot for assisting the spinal surgery is improved.
Preferably, the spine surgical robot system is a surgical robot system specially used for auxiliary positioning of spine surgery, and the surgical robot system is generally composed of binocular stereo image navigation and six-degree-of-freedom machinery, and can hold a surgical instrument to a specified position under guidance of the image navigation in the surgery according to a surgical plan planned before the surgery.
Preferably, the surgical robot control platform comprises robot image navigation and online control software, is connected with a controller of the robot, and realizes the online path planning of the robot through image navigation information, manual interaction or other feedback information, thereby completing the designated action under the planned path.
According to the embodiment, the safety level of the puncture path of the puncture needle is judged according to the early warning signal classifier, and a safety feedback control method is established by combining the prediction of a surgical robot navigation system on the motion trend of the puncture needle, so that unnecessary nerve injury caused in the robot-assisted puncture process is effectively avoided.
Additionally, in further embodiments, there is also provided:
a spinal surgery robot puncture early warning system based on electromyographic signals comprises: the system comprises an electromyographic signal acquisition device and an early warning signal classifier;
the electromyographic signal acquisition device acquires an electromyographic signal generated after electrical stimulation is applied to the puncture needle;
the early warning signal classifier obtains the distance between the puncture needle and the neural tissue according to the electromyographic signals, and judges whether the puncture needle keeps a safe distance from the neural tissue according to the distance to obtain the safety level of a puncture area where the puncture needle is located;
the early warning signal classifier is also used for giving out an early warning when the puncture needle exceeds the safe distance with the nervous tissue and sending out a feedback control instruction to the robot so as to stop the robot or change the puncture path.
It should be noted that the modules correspond to the steps described in embodiment 1, and the modules are the same as the corresponding steps in the implementation examples and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
Example 3
The embodiment provides a spinal surgery robot puncture early warning platform based on an electromyographic signal, which comprises an electromyographic signal acquisition device, a puncture early warning system and a spinal surgery robot; the electromyographic signal acquisition device is used for acquiring generated electromyographic signals after applying electrical stimulation to the puncture needle and sending the generated electromyographic signals to the puncture early warning system; the puncture early warning system obtains the safety level of a puncture area where the puncture needle is located according to the electromyographic signals, sends out early warning signals according to the safety level and feeds back a puncture path control command to the spinal surgery robot; and planning the puncture path by the spinal surgery robot according to the puncture path control instruction.
In this embodiment, firstly, an early warning signal classifier based on an electromyographic signal is established by the classifier construction method described in embodiment 1 or embodiment 2; next, as shown in fig. 3, when the surgical manipulator holds the puncture needle to execute the puncture path, the electromyographic signals of the corresponding portion of the electromyographic signal collecting device are input into the trained early warning signal classifier, and the surgical image navigation system is combined to determine whether the distance between the puncture needle and the important nerve root is safe.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114366309A (en)* | 2022-01-17 | 2022-04-19 | 上海锦立城医疗科技有限公司 | Surgical robot with nerve monitoring function |
| CN115137487A (en)* | 2022-07-13 | 2022-10-04 | 山东大学 | Spine endoscopic surgery robot system and method based on intraoperative physiological monitoring |
| CN115227350A (en)* | 2022-07-20 | 2022-10-25 | 武汉拓扑转化医学研究中心有限公司 | Spine probe monitoring system and method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101815475A (en)* | 2007-10-05 | 2010-08-25 | 新特斯有限责任公司 | Dilation system and method of using the same |
| US20130253533A1 (en)* | 2012-03-23 | 2013-09-26 | Innovative Surgical Solutions, Llc | Robotic surgical system with mechanomyography feedback |
| CN104983419A (en)* | 2015-07-24 | 2015-10-21 | 绵阳美科电子设备有限责任公司 | Myoelectric ultrasonic scan position indicator, positioning method thereof and fixed point myoelectric acquisition, electrostimulation and medicament injection method |
| CN105030334A (en)* | 2015-06-10 | 2015-11-11 | 中国人民解放军第二军医大学 | Opening navigation detection system for spinal surgeries |
| WO2015184131A1 (en)* | 2014-05-29 | 2015-12-03 | Jackson Avery M Iii | Illuminated endoscopic pedicle proble with proximity monitoring |
| CN105232154A (en)* | 2015-09-30 | 2016-01-13 | 钱明 | Intelligent digital spinal operation device |
| US20190000553A1 (en)* | 2002-03-06 | 2019-01-03 | Mako Surgical Corp. | Neural monitor-based dynamic haptics |
| CN111166437A (en)* | 2020-02-17 | 2020-05-19 | 杭州三坛医疗科技有限公司 | Auxiliary method for epidural anesthesia needle placement |
- 2020
- 2020-12-21CNCN202011520992.2Apatent/CN112656510B/enactiveActive
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190000553A1 (en)* | 2002-03-06 | 2019-01-03 | Mako Surgical Corp. | Neural monitor-based dynamic haptics |
| CN101815475A (en)* | 2007-10-05 | 2010-08-25 | 新特斯有限责任公司 | Dilation system and method of using the same |
| US20130253533A1 (en)* | 2012-03-23 | 2013-09-26 | Innovative Surgical Solutions, Llc | Robotic surgical system with mechanomyography feedback |
| WO2015184131A1 (en)* | 2014-05-29 | 2015-12-03 | Jackson Avery M Iii | Illuminated endoscopic pedicle proble with proximity monitoring |
| CN106455938A (en)* | 2014-05-29 | 2017-02-22 | 艾佛里·M·杰克森三世 | Illuminated endoscopic pedicle probe with proximity monitoring |
| CN105030334A (en)* | 2015-06-10 | 2015-11-11 | 中国人民解放军第二军医大学 | Opening navigation detection system for spinal surgeries |
| CN104983419A (en)* | 2015-07-24 | 2015-10-21 | 绵阳美科电子设备有限责任公司 | Myoelectric ultrasonic scan position indicator, positioning method thereof and fixed point myoelectric acquisition, electrostimulation and medicament injection method |
| CN105232154A (en)* | 2015-09-30 | 2016-01-13 | 钱明 | Intelligent digital spinal operation device |
| CN111166437A (en)* | 2020-02-17 | 2020-05-19 | 杭州三坛医疗科技有限公司 | Auxiliary method for epidural anesthesia needle placement |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114366309A (en)* | 2022-01-17 | 2022-04-19 | 上海锦立城医疗科技有限公司 | Surgical robot with nerve monitoring function |
| CN115137487A (en)* | 2022-07-13 | 2022-10-04 | 山东大学 | Spine endoscopic surgery robot system and method based on intraoperative physiological monitoring |
| CN115227350A (en)* | 2022-07-20 | 2022-10-25 | 武汉拓扑转化医学研究中心有限公司 | Spine probe monitoring system and method |
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