CN113730816B - System, method, computer device and storage medium for automatically detecting motion threshold - Google Patents
System, method, computer device and storage medium for automatically detecting motion threshold Download PDFInfo
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Abstract
The application relates to a system, a method, a computer device and a storage medium for automatically detecting a motion threshold, wherein the system comprises: comprising the following steps: the device comprises a mechanical arm, a navigation system, a control module, a transcranial stimulator and a myoelectric evoked potential instrument; the transcranial stimulator comprises a stimulation coil, and the mechanical arm is in transmission connection with the stimulation coil; the mechanical arm, the navigation system, the transcranial stimulator and the myoelectricity evoked potential instrument are all connected with the control module; the transcranial stimulator is used for stimulating brain functional areas and causing corresponding nerve feedback; the myoelectricity evoked potential instrument is used for collecting physiological signal data and transmitting the data to the control module; the control module integrates the navigation system and the coordinate system of the mechanical arm; the control module acquires the position and the target position of the stimulation coil, acquires the state of the mechanical arm and controls the mechanical arm to drive the stimulation coil to a designated position.
Description
Technical Field
The present application relates to the field of motion threshold detection technologies, and in particular, to a system, a method, a computer device, and a storage medium for automatically detecting a motion threshold.
Background
Transcranial magnetic stimulation (Transcranial Magnetic Stimulation, hereinafter TMS) is a magnetic stimulation technique that uses a pulsed magnetic field to act on the central nervous system (mainly the brain), changing the membrane potential of cortical nerve cells to induce currents, affecting the metabolism and neuroelectric activity in the brain, thereby causing a series of physiological and biochemical reactions, which has been gaining increasing acceptance in the fields of cognitive neuroscience, clinical neuropsychiatric diseases and rehabilitation, because of its advantages of no pain, no injury, no radiation, etc. The accuracy of the stimulus intensity is an important index of whether TMS treatment is effective or not, and the stimulus intensity is determined by taking a threshold value as a reference (typically 80% -120% rMT), and the threshold value of different individuals is quite different, so that threshold detection is an item which must be performed before all clinical treatments and scientific researches of the nervous system diseases. In view of the difference of the magnetic field focus points of the same type of stimulation coils with different skill levels of operators, how to quickly and accurately complete the threshold detection becomes a difficult problem to be solved.
The currently common threshold detection methods mainly comprise the following two methods:
1. visual observation of muscle spasms (OM-MT): the operator puts the stimulation coil in the brain motor function area to be tested, continuously adjusts the intensity, moves the stimulation position to perform single stimulation until the shake of the fingers of the upper limb/the shake of the lower limb of the opposite side to be tested is observed, and gradually reduces the stimulation intensity until the shake is not observed, so as to judge the tested threshold value. This method requires a certain experience on the one hand for the operator and on the other hand, the accuracy of the measured threshold is poor.
2. Electromyography MT measurement (EMG-MT): the operation process of the method is similar to that of a visual inspection method, an electrode of an myoelectricity evoked potential instrument (MEP) is attached to a corresponding part of an upper limb/lower limb to be tested, a coil is moved to a brain movement functional area, and the minimum intensity when the amplitude of about 50 mu V can be acquired for 5 times in 10 times is the left M1 resting movement threshold (rMT) by continuously adjusting the single stimulation intensity and observing the amplitude of the MEP. Although the method can quantitatively detect the movement threshold, the testing process is complex, how to adjust the stimulus intensity and the stimulus position, and how to judge the effectiveness of the trigger waveform all have high requirements on the skill of operators, the detection efficiency is low, and the method is difficult to popularize in a large scale clinically.
Disclosure of Invention
The application provides a system, a device, computer equipment and a storage medium for automatically detecting a motion threshold value, so as to improve the detection speed and accuracy of a moving target.
In a first aspect, the present application provides a system for automatically detecting a motion threshold, comprising: the device comprises a mechanical arm, a navigation system, a control module, a transcranial stimulator and a myoelectric evoked potential instrument; the transcranial stimulator comprises a stimulation coil, and the mechanical arm is in transmission connection with the stimulation coil; the mechanical arm, the navigation system, the transcranial stimulator and the myoelectricity evoked potential instrument are all connected with the control module; the transcranial stimulator is used for stimulating brain functional areas and causing corresponding nerve feedback; the myoelectricity evoked potential instrument is used for collecting physiological signal data and transmitting the data to the control module; the control module integrates the navigation system and the coordinate system of the mechanical arm; the control module acquires the position and the target position of the stimulation coil, acquires the state of the mechanical arm and controls the mechanical arm to drive the stimulation coil to a designated position.
In a second aspect, the present application further provides a method for automatically detecting a motion threshold, where the system for automatically detecting a motion threshold includes:
the mechanical arm, the navigation system, the control module, the transcranial stimulator and the myoelectricity evoked potential instrument are connected in a communication way;
integrating the coordinate system of the navigation system and the coordinate system of the mechanical arm system, and uniformly converting the integrated coordinate system and the coordinate system into the same coordinate system;
and setting preset parameters, informing the navigation system of the positions of the tested anatomical feature points, and controlling the whole system to finish automatic detection of the motion threshold by a binary search method.
In a third aspect, the present application also provides a computer device comprising a memory and a processor; the memory is used for storing a computer program; the processor is configured to execute the computer program and implement the method for automatically detecting a motion threshold as described above when the computer program is executed.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement a method of automatically detecting a motion threshold as described above.
The application discloses a system, a method, equipment and a storage medium for automatically detecting a motion threshold, wherein the system for automatically detecting the motion threshold is based on TMS technology, medical image processing technology, optical navigation technology and intelligent mechanical arm control technology, integrates the technologies, and has the advantages of simplicity in operation (no professional skill requirement on operators) and rapidness and accuracy (detection is completed by one key after an approximate area is selected) compared with a common detection system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of a system for automatically detecting motion thresholds provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for automatically detecting a motion threshold provided by an embodiment of the present application;
fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
Embodiments of the present application provide a system, method, computer device, and storage medium for automatically detecting a motion threshold. The system for automatically detecting the motion threshold solves the defects of low accuracy determination, complex operation and low efficiency of the existing motion threshold, and provides a simple and efficient evaluation means for diagnosis and treatment of nervous system diseases.
Referring to fig. 1, the system for automatically detecting a motion threshold according to the present application includes a mechanical arm, a navigation system, a control module, a transcranial stimulator and a myoelectric evoked potential apparatus, wherein the transcranial stimulator includes a stimulation coil. The mechanical arm is in transmission connection with the stimulation coil, and the mechanical arm drives the stimulation coil to move so as to stimulate the set stimulation point. The mechanical arm, the navigation system, the transcranial stimulator and the myoelectricity evoked potential instrument are all connected with the control module. Transcranial stimulators are used to stimulate brain functional areas, causing corresponding neural feedback. The myoelectricity evoked potential instrument is used for collecting physiological signal data and transmitting the collected data to the control module. The control module integrates the navigation system and the coordinate system of the mechanical arm, acquires the position and the target position of the stimulation coil, acquires the state of the mechanical arm and controls the mechanical arm to drive the stimulation coil to the designated position and the stimulation point so as to stimulate. The system for automatically detecting the motion threshold value disclosed by the application uses a navigation system, a mechanical arm, a transcranial stimulator and an myoelectric evoked potential instrument to jointly detect the motion threshold value, has the advantages of simplicity, rapidness and accuracy in operation compared with a common detection system, and has no professional skill requirement on operators.
The navigation system of the present application uses existing sophisticated navigation systems that include an optical tracker, a navigation marker, and software. The application mainly realizes three-dimensional reconstruction of two-dimensional MRI image data, and real-time calculation of current position coordinates and target position coordinates (target position coordinates) of a stimulation coil.
The mechanical arm is a mature cooperative mechanical arm in the market, and comprises an electric control box, the mechanical arm and a force sensor. The mechanical arm does not relate to structural design and bottom layer motion control of the mechanical arm, and is only developed on an API provided by the existing mechanical arm so as to achieve the acquisition of the state of the mechanical arm and the motion control function of each joint, so that the mechanical arm is controlled by the control module to move the stimulation coil to a designated position.
The myoelectricity evoked potential instrument uses mature equipment in the market, and the application performs secondary development on the API thereof and increases a communication interface, so that the myoelectricity evoked potential instrument is in communication connection with the control module, thereby transmitting the acquired physiological electric signal data to the control module and providing data input for the subsequent threshold data calculation.
The control module is provided with a communication interface which is in communication connection with the mechanical arm, the navigation system, the transcranial stimulator and the myoelectric evoked potential instrument, so that the mechanical arm, the navigation system, the transcranial stimulator and the myoelectric evoked potential instrument are controlled, and the movement threshold is automatically calculated according to a preset algorithm.
When the system is used, the control module firstly acquires the current position and the target position of the stimulation coil from the navigation communication interface in real time, acquires the equipment state information from the transcranial stimulation instrument communication interface and acquires the state information from the mechanical arm communication interface. The operation of each component is controlled, the output intensity is set through a transcranial stimulator communication interface, the single pulse output of the control equipment is controlled, the mechanical arm is controlled to move to a stimulation point through a mechanical arm communication interface, physiological electric signal data are obtained from a myoelectricity evoked potential instrument, and then a movement threshold is calculated.
In an alternative embodiment, the navigation system comprises any one of optical navigation based on binocular ranging principle, optical navigation based on depth camera positioning, electromagnetic navigation based on magnetic navigation sensors.
The application also provides a method for automatically detecting the motion threshold, which is used for the system for automatically detecting the motion threshold, thereby realizing the purpose of automatically detecting the motion threshold.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for automatically detecting a motion threshold according to an embodiment of the application. The method comprises the steps one to three.
And step one, communication connection is established among the mechanical arm, the navigation system, the control module, the transcranial stimulator and the myoelectric evoked potential instrument.
Specifically, the mechanical arm, the navigation system, the transcranial stimulator and the myoelectricity evoked potential instrument are all connected with the control module through the communication interface to construct a communication system for controlling the operation of each component in a simulation control mode. Communication services between the various components include, but are not limited to, USB, serial, socket communication modes.
The process starts the carrier of the control module, which includes but is not limited to a common PC or an industrial personal computer. And then the navigation system hardware is electrified, the navigation system software module is started so as to be connected with the control module in opposition, in the process, the navigation software can be started in a single operation mode or in a mode that the control module calls the functional module through an API, and communication connection is established between the navigation software and the control module after the navigation software is started. The electrical control box of the robotic arm is then energized and connected to the control module, which may be accomplished in two modes, but not limited to, a single control switch, and a trigger switch using the control module carrier. And then starting the mechanical arm, initializing the force sensor, detecting whether the mechanical arm is in a preset initial posture by the control module, and controlling the mechanical arm to move to the initial posture if the mechanical arm is not in the initial posture. Finally, the power supplies of the transcranial stimulator and the myoelectricity evoked potential instrument are started, the transcranial stimulator and the control module are started to establish communication connection, and the power-on process of the transcranial stimulator can be realized by adopting two modes of a single control switch and a trigger switch using a control module carrier. After the power supply of the myoelectric evoked potential apparatus is started, the system is used for the first time, and a signal synchronization line for starting the transcranial stimulator and the myoelectric evoked potential apparatus is required to be connected.
And step two, integrating the coordinate system of the navigation system and the coordinate system of the mechanical arm system, and uniformly converting the integrated coordinate system and the coordinate system into the same coordinate system.
Specifically, since the navigation system and the robot arm system adopt different coordinate systems, the coordinate systems of the navigation system and the robot arm system need to be integrated and uniformly converted into the same coordinate system. So as to facilitate the accurate movement of the subsequent control analog control mechanical arm.
Thirdly, setting preset parameters, informing the navigation system of the positions of the tested anatomical feature points, and controlling the whole system to finish automatic detection of the motion threshold value through a binary search method.
Specifically, preset parameters are set first, a navigation system is informed of the positions of the tested anatomical feature points (namely, a head model is built), the initial stimulation positions are calculated by using a traditional 10-20 electroencephalogram dividing method, and a binary search method is used for automatically detecting the motion threshold.
The method described above uses existing sophisticated navigation systems including optical trackers, navigation markers and software. The application mainly realizes three-dimensional reconstruction of two-dimensional MRI image data, and real-time calculation of current position coordinates and target position coordinates (target position coordinates) of a stimulation coil.
In an embodiment of the present application, the preset parameters include stimulus spacing, stimulus intensity range, and test accuracy. The stimulus interval is used for generating a stimulus matrix; the stimulation intensity range can be defined according to the safe stimulation intensity range, and can be defined according to the experience of doctors; the test accuracy may be set to 5% or may be set according to the experience of the user, and is not limited herein.
In an alternative embodiment, step three includes steps S101-S109.
S101, informing a navigation system of the position of the tested anatomical feature point, and establishing a head model.
Specifically, a marker rod matched with a navigation system is used for assisting in completing feature point calibration, and a head model is built for subsequent work.
S101, finding out the optimal stimulation points, and generating a stimulation matrix according to the preset interval.
Specifically, the initial stimulation position is determined by combining a 10-20 lead positioning method, and a stimulation matrix is generated by taking the stimulation point as the center according to a preset interval. The stimulation matrix may be a 3*3 matrix, 4*4 matrix, or the like.
S102, controlling the mechanical arm to move the stimulation coil to a stimulation point on the stimulation matrix.
Specifically, the stimulation matrix includes a plurality of stimulation points, and the control module controls the robotic arm to move the stimulation coil to any one of the stimulation points on the stimulation matrix.
S103, adjusting the output intensity of the transcranial stimulator to the median intensity of the stimulation intensity range.
Specifically, the stimulation intensity range is an output intensity range input by a user, and the initial test adjusts the output intensity of the transcranial stimulation apparatus to the median intensity of the stimulation intensity range.
S104, outputting single pulse by the transcranial stimulator, reading data by the myoelectric evoked potential instrument, analyzing the data, and judging whether the current intensity is an effective evoked potential.
Specifically, after the control module judges that the mechanical arm reaches the target stimulation point, the control module controls the transcranial stimulation instrument to output a single stimulation pulse according to the set output intensity. And reading data by using a myoelectric evoked potential instrument, performing OPT model fitting, and judging whether the data is effective evoked potential or not according to the waveform data after fitting.
S105, if the effective evoked potential is the effective evoked potential, the median of the current stimulus intensity and the low value is calculated, the step S106 is performed, and if the effective evoked potential is not the effective evoked potential, the step S107 is performed.
Specifically, if the judgment result is the effective evoked potential, the median of the current stimulus intensity and the low value is calculated, the low value is the minimum value of the preset stimulus intensity range, and then step S106 is performed. If the current stimulus intensity is not the effective evoked potential, the current stimulus intensity is low, and the current stimulus intensity needs to be searched upwards, and the process proceeds to step S107.
And S106, if the difference value between the recalculated median value and the intensity of the current stimulation is larger than the test precision, controlling the output intensity of the transcranial stimulation instrument to be the calculated median value, returning to the step S104, and if the difference value between the recalculated median value and the intensity of the current stimulation is smaller than the test precision, recording a threshold value and a stimulation point, and ending the process.
Specifically, if the difference between the recalculated median and the current stimulus intensity is greater than the test accuracy, the current stimulus intensity is not the motion threshold and needs to be further detected, and at this time, the control module controls the output intensity of the transcranial stimulator to be the recalculated median, and the step S104 is performed again. If the difference between the recalculated median and the current stimulus intensity is smaller than the test precision, the current stimulus intensity is a motion threshold, the threshold and the stimulus point are recorded at the moment, and the process is ended.
S107, judging whether the last stimulus intensity is the effective evoked potential, if not recording or not, proceeding to step S108, and if the last stimulus intensity is the effective evoked potential, proceeding to step S109.
Specifically, it is determined whether the previous stimulus intensity is a valid evoked potential, if no record is made or the previous stimulus intensity is an invalid evoked potential, the process proceeds to step S108. If the last stimulus intensity is the effective evoked potential, the process proceeds to step S109.
S108, adjusting the output intensity to the median value of the maximum value of the current intensity and the stimulation intensity range, judging whether the median value exceeds the safety intensity, if so, controlling the mechanical arm to move the stimulation coil to another stimulation point on the stimulation matrix, returning to the step S104, and if not, adjusting the output intensity of the transcranial stimulation instrument to be the median value, and returning to the step S104.
Specifically, if the last stimulus intensity is not a valid evoked potential, it is indicated that the last stimulus intensity is less than the current stimulus intensity, and that both stimulus intensities are below the exercise threshold, and then the exercise threshold needs to be found upwards, and the output intensity is adjusted to the median of the current intensity and the maximum value of the stimulus intensity range. At this time, it is required to determine whether the newly calculated median exceeds the safety intensity, if yes, it is indicated that the point is unsafe and affects the health of the human body, at this time, it is indicated that the one stimulation point is not the stimulation point to be found, at this time, the control mechanical arm moves the stimulation coil to another stimulation point on the stimulation matrix, and returns to step S104 again, and the movement threshold of the other stimulation point is re-detected.
If the median of the maximum values of the current intensity and the stimulus intensity range does not exceed the safety intensity, it is indicated that the current median intensity is likely to be the motion threshold, and further judgment is required, and step S104 is required to be performed to continue searching.
S109, adjusting the output intensity of the transcranial stimulator to be the median value of the current intensity and the last stimulation intensity, and returning to the step S104.
Specifically, if the last stimulation intensity is the effective intensity, it is indicated that the motion threshold is between the current stimulation intensity and the last stimulation intensity, and the output intensity of the transcranial stimulation apparatus is adjusted to the intermediate value between the current intensity and the last stimulation intensity, and the process returns to step S104 again.
According to the method, the method is operated until the motion threshold is found or the stimulus point and the motion threshold are not found, and the stimulus interval is required to be adjusted, for example, the original stimulus matrix is 3*3, the new stimulus interval is 4*4, and the detection is performed again until the detection point and the motion threshold are found.
The application combines a binary search method and a self-adaptive learning evaluation method, the binary search method is the basis of the whole automatic detection method, when the data volume is insufficient, the stimulation intensity uses preset bottom and top elements, and the initial stimulation position is calculated by using the traditional 10-20 brain electrical dividing method. The stimulation intensity and the stimulation position of each effective threshold value are used as the input of the self-adaptive algorithm, the bottom and top elements and the stimulation initial position of the binary search method are continuously optimized according to normal distribution, and the whole detection process of the automatic detection method can be more and more efficient along with the increase of the data quantity.
The method described above may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server or a terminal.
With reference to FIG. 3, the computer device includes a processor, memory, and a network interface connected by a system bus, where the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any of a number of systems for automatically detecting a motion threshold.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a number of systems for automatically detecting motion thresholds.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in FIG. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In an embodiment of the present application, a computer readable storage medium is further provided, where the computer readable storage medium stores a computer program, where the computer program includes program instructions, and the processor executes the program instructions to implement any one of the systems for automatically detecting a motion threshold provided in the embodiments of the present application.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (5)
1. A method for automatically detecting a motion threshold, comprising the steps of:
the method comprises the steps that communication connection is established among a mechanical arm, a navigation system, a control module, a transcranial stimulator and a myoelectricity evoked potential instrument;
integrating the coordinate system of the navigation system and the coordinate system of the mechanical arm system, and uniformly converting the integrated coordinate system and the coordinate system into the same coordinate system;
setting preset parameters, wherein the preset parameters comprise: the method comprises the following steps of informing a navigation system of the positions of the tested anatomical feature points according to the stimulus interval, the stimulus intensity range and the test precision, and controlling the whole system to complete automatic detection of the motion threshold value by a binary search method, wherein the method comprises the following steps:
step S101, informing a navigation system of the position of the tested anatomical feature point, and establishing a head model;
step S101, finding out the optimal stimulation points, and generating a stimulation matrix according to a preset interval;
step S102, controlling the mechanical arm to move the stimulation coil to a stimulation point on the stimulation matrix;
step S103, adjusting the output intensity of the transcranial stimulator to the median intensity of the stimulation intensity range;
step S104, outputting single pulse by the transcranial stimulator, reading data by the myoelectric evoked potential tester, analyzing the data, and judging whether the current intensity is a valid evoked potential;
step 105, if the effective evoked potential is the effective evoked potential, calculating the median of the current stimulus intensity and the low value, entering step 106, and if the effective evoked potential is not the effective evoked potential, entering step 107;
step S106, if the difference value between the recalculated median value and the intensity of the current stimulation is larger than the test precision, controlling the output intensity of the transcranial stimulation instrument to be the calculated median value, returning to step S104, if the difference value between the recalculated median value and the intensity of the current stimulation is smaller than the test precision, recording a threshold value and a stimulation point, and ending the process;
step S107, judging whether the last stimulus intensity is a valid evoked potential, if not, entering step S108, and if the last stimulus intensity is a valid evoked potential, entering step S109;
step S108, adjusting the output intensity to the median value of the maximum value of the current intensity and the stimulation intensity range, judging whether the median value exceeds the safety intensity, if so, controlling the mechanical arm to move the stimulation coil to another stimulation point on the stimulation matrix, returning to step S104, and if not, adjusting the output intensity of the transcranial stimulation instrument to be the median value, and returning to step S104;
step S109, adjusting the output intensity of the transcranial stimulator to be the median value of the current intensity and the last stimulation intensity, and returning to step S104.
2. A system for automatically detecting a motion threshold for performing the method for automatically detecting a motion threshold as in claim 1, wherein the system for automatically detecting a motion threshold comprises: the device comprises a mechanical arm, a navigation system, a control module, a transcranial stimulator and a myoelectric evoked potential instrument; the transcranial stimulator comprises a stimulation coil, and the mechanical arm is in transmission connection with the stimulation coil; the mechanical arm, the navigation system, the transcranial stimulator and the myoelectricity evoked potential instrument are all connected with the control module; the transcranial stimulator is used for stimulating brain functional areas and causing corresponding nerve feedback; the myoelectricity evoked potential instrument is used for collecting physiological signal data and transmitting the data to the control module; the control module integrates the navigation system and the coordinate system of the mechanical arm; the control module acquires the position and the target position of the stimulation coil, acquires the state of the mechanical arm and controls the mechanical arm to drive the stimulation coil to a designated position;
the control module acquires the current position and the target position of the stimulation coil from the navigation communication interface in real time, acquires the equipment state information from the transcranial stimulation instrument communication interface and acquires the state information from the mechanical arm communication interface; the output intensity is set through the transcranial stimulator communication interface, the single pulse output of the control equipment is controlled, the mechanical arm is controlled to move to a stimulation point through the mechanical arm communication interface, physiological electric signal data are obtained from the myoelectric evoked potential instrument, and then the movement threshold is calculated.
3. The system for automatically detecting motion threshold according to claim 2, wherein the navigation system comprises any one of optical navigation based on binocular ranging principle, optical navigation based on depth camera positioning, electromagnetic navigation based on magnetic navigation sensor.
4. A computer device, the computer device comprising a memory and a processor;
the memory is used for storing a computer program;
the processor for executing the computer program and for implementing the method of automatically detecting a motion threshold as claimed in claim 1 when the computer program is executed.
5. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement the method of automatically detecting a motion threshold as claimed in claim 1.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104001266A (en) * | 2014-06-13 | 2014-08-27 | 中国医学科学院生物医学工程研究所 | Method for determining transcranial magnetic stimulation (TMS) amount based on distance measurement |
| CN105268104A (en) * | 2014-07-22 | 2016-01-27 | 北京脑泰科技发展有限公司 | Transcranial magnetic stimulator system of control stimulation coil end |
| CN107684664A (en) * | 2017-11-06 | 2018-02-13 | 关沛棠 | A kind of Intelligent Composite waveform electrical transcranial stimulation system |
| CN110639127A (en) * | 2019-09-26 | 2020-01-03 | 深圳英智科技有限公司 | Transcranial magnetic stimulation system |
| CN110896609A (en) * | 2018-09-27 | 2020-03-20 | 武汉资联虹康科技股份有限公司 | TMS positioning navigation method for transcranial magnetic stimulation treatment |
| CN111729200A (en) * | 2020-07-27 | 2020-10-02 | 浙江大学 | Transcranial magnetic stimulation automatic navigation system and method based on depth camera and magnetic resonance |
| CN114733073A (en) * | 2022-05-10 | 2022-07-12 | 深圳英智科技有限公司 | Transcranial magnetic stimulation method and system based on electromagnetic navigation positioning and electronic equipment |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7651459B2 (en) * | 2004-01-06 | 2010-01-26 | Neuronetics, Inc. | Method and apparatus for coil positioning for TMS studies |
| US9884200B2 (en) * | 2008-03-10 | 2018-02-06 | Neuronetics, Inc. | Apparatus for coil positioning for TMS studies |
-
2021
- 2021-08-19 CN CN202110956421.1A patent/CN113730816B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104001266A (en) * | 2014-06-13 | 2014-08-27 | 中国医学科学院生物医学工程研究所 | Method for determining transcranial magnetic stimulation (TMS) amount based on distance measurement |
| CN105268104A (en) * | 2014-07-22 | 2016-01-27 | 北京脑泰科技发展有限公司 | Transcranial magnetic stimulator system of control stimulation coil end |
| CN107684664A (en) * | 2017-11-06 | 2018-02-13 | 关沛棠 | A kind of Intelligent Composite waveform electrical transcranial stimulation system |
| CN110896609A (en) * | 2018-09-27 | 2020-03-20 | 武汉资联虹康科技股份有限公司 | TMS positioning navigation method for transcranial magnetic stimulation treatment |
| CN110639127A (en) * | 2019-09-26 | 2020-01-03 | 深圳英智科技有限公司 | Transcranial magnetic stimulation system |
| CN111729200A (en) * | 2020-07-27 | 2020-10-02 | 浙江大学 | Transcranial magnetic stimulation automatic navigation system and method based on depth camera and magnetic resonance |
| CN114733073A (en) * | 2022-05-10 | 2022-07-12 | 深圳英智科技有限公司 | Transcranial magnetic stimulation method and system based on electromagnetic navigation positioning and electronic equipment |
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