Time-domain interferential electrical stimulation closed-loop regulation method and system for tremor rehabilitation

Abstract

The application discloses a time-domain interference electric stimulation closed-loop regulation method and system for tremor rehabilitation, and belongs to the technical field of biomedical engineering. The application monitors the myoelectric signal of a patient in real time, extracts tremor-related neuromuscular activity characteristics, and evaluates the severity of tremor based on the characteristics. The application uses a closed-loop feedback mechanism to dynamically adjust the parameters of time-domain interference electric stimulation, including the position of the stimulation electrode, the current intensity, the carrier frequency and the difference frequency. By gradually optimizing the target point selection and parameter combination, the best stimulation scheme that minimizes the degree of tremor is found. In addition, the application provides a complete closed-loop regulation system, which can realize full-process automation, reduce human intervention and improve the stability of treatment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biomedical engineering, and particularly relates to a time-domain interference electric stimulation closed-loop adjustment method and system for tremor rehabilitation. BACKGROUND

[0002] Motor tremor is an involuntary and rhythmic shaking that occurs during limb movement, commonly seen in neurological diseases such as Parkinson's disease or cerebellar lesions, and can also be caused by factors such as drugs and metabolic abnormalities. This tremor usually worsens during movement, especially when approaching the target, which severely affects the patient's fine motor skills and quality of life. Currently, there are limited treatment options for motor tremor, and drugs and surgery have certain effects but are limited, so developing new treatment options has important clinical significance. Currently, deep brain stimulation (DBS) is one of the effective methods for treating motor tremor, which can significantly improve the motor function of patients by stimulating target points such as the subthalamic nucleus (STN) or the medial part of the globus pallidus (GPi). However, DBS as an invasive surgery has certain drawbacks, such as surgical risks, device infections, electrode displacement, and long-term implantation complications, so its clinical use is limited.

[0003] Recently, temporal interference stimulation (TI) as a new non-invasive neural modulation technology has gradually attracted attention. Temporal interference stimulation applies sinusoidal current with slightly different frequencies (kilohertz level) outside the skull to generate low-frequency envelope waveforms (several hertz to several tens of hertz) in the deep brain, thereby achieving deep stimulation of specific brain regions. Temporal interference stimulation uses the interference effect of frequency difference current to focus the electric field in the deep brain, avoiding the shortcomings of traditional transcranial electrical stimulation technology, which has a shallow modulation depth and is difficult to accurately stimulate target points. Preliminary clinical studies have shown that temporal interference stimulation can effectively stimulate deep brain target points and has good application prospects in the treatment of motor tremor, but currently temporal interference stimulation for motor tremor rehabilitation intervention is based on a fixed parameter scheme (stimulation target and stimulation parameters), so how to accurately adjust individual stimulation targets and parameters based on individual differences to achieve individual precise intervention is still a problem to be solved. SUMMARY

[0004] Therefore, the present application aims to provide a time-domain interference electric stimulation closed-loop adjustment method and system for tremor rehabilitation, taking the individual tremor degree of a patient as a feedback parameter, dynamically adjusting the stimulation parameters (electric stimulation target point, electrode position, current intensity and frequency) by real-time monitoring of the tremor signal, so as to realize individual accurate neural rehabilitation regulation. The system aims to overcome the shortcomings of the prior art and provide a closed-loop personalized, efficient and safe non-invasive rehabilitation regulation scheme for the treatment of patients with motor tremor.

[0005] The specific technical solutions adopted by the present application are as follows:

[0006] In a first aspect, the present application provides a time-domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation, comprising:

[0007] S1, selecting a candidate target point in a target point selection area of the brain;

[0008] S2, for each candidate target point currently selected, simulating different stimulation electrode positions and stimulation current intensities by using a personalized head model constructed for the stimulation object, taking the maximum peak electric field at the candidate target point and the maximum focusing degree as the optimization objective, and determining the best stimulation electrode position and the best stimulation current intensity of each stimulation electrode by an optimization algorithm;

[0009] S3: for each candidate target point currently selected, after arranging each stimulation electrode according to the best stimulation electrode position, sampling the stimulation current carrier frequency and the difference frequency of each stimulation electrode to form a series of candidate stimulation frequency parameter combinations, continuously adjusting the candidate stimulation frequency parameter combinations under the condition that the stimulation electrode fixedly outputs the best stimulation current intensity, generating and outputting the stimulation current control signals corresponding to different candidate stimulation frequency parameter combinations to the stimulation electrode in turn, and real-time collecting the electromyographic signals of the stimulation object after being stimulated by the stimulation current, extracting the tremor frequency feature and the tremor intensity feature therefrom and calculating the tremor degree score, and selecting the candidate stimulation frequency parameter combination with the minimum tremor degree score as the best stimulation frequency parameter combination;

[0010] S4, performing S2 and S3 on the candidate target points in the target point selection area respectively, taking the minimization of the tremor degree score as the target, searching for the optimal target point, and saving the best stimulation electrode position, the best stimulation current intensity and the best stimulation frequency parameter combination corresponding to the optimal target point.

[0011] As a preferred embodiment of the first aspect, for the candidate target points in the target point selection area, taking the STN-GPi loop as the target point selection area, the optimal target point is searched by dichotomy, and the process of dichotomy is as follows:

[0012] Firstly, the S2 and S3 are respectively performed on the STN region and the GPi region as two candidate target points, to obtain tremor degree scores corresponding to the two candidate target points respectively;

[0013] Then, the relative sizes of the tremor degree scores of the two candidate target points are judged, and the candidate target point with the smaller tremor degree score is retained, while the middle position of the two target points is selected as a new candidate target point to replace the other candidate target point with the larger tremor degree score, and the S2 and S3 are re-performed on the updated two candidate target points to obtain tremor degree scores corresponding to the two candidate target points respectively.

[0014] The candidate target points are iteratively updated to approach the optimal solution until a termination condition is reached, and the optimal target point with the smallest tremor degree score is obtained.

[0015] As a preferred embodiment of the first aspect, the optimization algorithm adopts a genetic algorithm, each group of feasible solutions of the genetic algorithm is at least four electrode positions in the EEG 10-10 standard lead system and the current intensity of each electrode, and the fitness function of the genetic algorithm is defined as the ratio of the peak electric field at the candidate target point to the focusing degree, and the focusing degree is defined as the brain volume greater than the preset percentage of the peak electric field.

[0016] As a preferred embodiment of the first aspect, when calculating the tremor degree score, the root mean square value of the electromyographic signal is taken as the tremor intensity feature, the electromyographic signal zero-crossing rate is taken as the tremor frequency feature, and the two feature parameters are weighted and summed to obtain the tremor degree score.

[0017] As a preferred embodiment of the first aspect, when calculating the tremor degree score, the tremor severity of the stimulation object determined according to clinical evaluation is needed, and the weighting weight value pre-fitted with clinical data for the tremor severity is called to weight and sum the tremor frequency feature and the tremor intensity feature.

[0018] As a preferred embodiment of the first aspect, the electromyographic signal is collected in real time by arranging surface electromyographic sensors on the limb muscle parts of the stimulation object.

[0019] As a preferred embodiment of the first aspect, the individual head model is obtained by brain tissue segmentation and three-dimensional reconstruction from the head MRI and / or CT scan data of the stimulation object, and during the execution of the optimization algorithm process, for each group of candidate stimulation electrode positions and stimulation current intensities, a stimulation electrode model is loaded at the corresponding position of the individual head model, then finite element meshing is performed and dielectric parameters are respectively assigned to the brain tissue and the stimulation electrode model in the individual head model, finally stimulation current is applied according to the corresponding stimulation current intensity, and brain electric field distribution is simulated to calculate the peak electric field at the candidate target point and the focusing degree.

[0020] In a second aspect, the present application provides a time-domain interference electric stimulation closed-loop adjustment system for tremor rehabilitation, for implementing the time-domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation according to any one of the solutions of the first aspect.

[0021] a signal acquisition unit configured to acquire, in real time, an electromyography signal of a muscle part of a limb of a stimulation subject;

[0022] a stimulation execution unit configured to generate, by a signal generator, a stimulation current control signal meeting the intensity and frequency requirements according to the received stimulation current intensity and stimulation frequency parameters, and output the stimulation current control signal to a stimulation electrode to generate a corresponding stimulation current;

[0023] a closed-loop adjustment unit configured to, in the process of iteratively executing the S2 and S3, continuously regulate the stimulation current output by the stimulation execution unit according to the electromyography signal acquired in real time by the signal acquisition unit, until an optimal target point with a minimum tremor degree score is obtained, and a combination of a best stimulation electrode position, a best stimulation current intensity, and a best stimulation frequency corresponding to the optimal target point.

[0024] In a third aspect, the present application provides a computer program product comprising computer programs / instructions, which, when executed by a processor, can implement the time-domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation according to any one of the solutions of the first aspect.

[0025] In a fourth aspect, the present application provides a computer electronic device comprising a memory and a processor.

[0026] The memory is configured to store a computer program.

[0027] The processor is configured to, when executing the computer program, can implement the time-domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation according to any one of the solutions of the first aspect.

[0028] Compared with the prior art, the present application has the following beneficial effects:

[0029] (1) The time-domain interference electric stimulation closed-loop regulation method for tremor designed by the present application can dynamically adjust the stimulation parameters according to the real-time tremor degree of the patient through the closed-loop feedback method, and realize the generation of individualized stimulation parameters. This dynamic regulation method based on the individual characteristics of the patient can better adapt to the changes in the patient's condition and improve the individual precision of the stimulation parameters.

[0030] (2) The application provides a complete closed-loop control system, including a signal acquisition unit, a closed-loop control unit and a stimulation unit. The systematic solution realizes the full-process automation from signal acquisition to stimulation implementation, reduces human intervention, improves the stability and repeatability of treatment, can quickly determine the intervention scheme and improve the treatment efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0031] The accompanying drawings are used to better understand the present application and do not limit the present application. Among them:

[0032] Figure 1 A step schematic diagram of the time-domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation;

[0033] Figure 2 A control feedback process schematic diagram in a single round of parameter optimization cycle;

[0034] Figure 3 A schematic diagram of an exemplary flow of the time-domain interference electric stimulation closed-loop adjustment;

[0035] Figure 4 A module composition schematic diagram of the time-domain interference electric stimulation closed-loop control system for motor tremor;

[0036] Figure 5 A structural schematic diagram of a computer electronic device. DETAILED DESCRIPTION

[0037] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below. In the following description, a lot of specific details are set forth in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the spirit of the present application, so the present application is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present application can be combined accordingly without conflict.

[0038] In the description of the present application, it should be understood that when an element is considered to be "connected" to another element, it can be directly connected to the other element or indirectly connected to the other element with an intermediate element. In contrast, when an element is referred to as being "directly" connected to another element, there is no intermediate element.

[0039] The application extracts tremor-related neuromuscular activity features by monitoring the myoelectric signal of the patient in real time, and evaluates the severity of the tremor based on these features, thereby constructing a closed-loop feedback mechanism, and finding the optimal stimulation scheme that minimizes the degree of tremor by dynamically adjusting the parameters of the time-domain interference electric stimulation (including the position of the stimulation electrode, the current intensity, the carrier frequency and the difference frequency), and gradually optimizing the target point selection and the combination of the electric stimulation parameters.

[0040] Referring to Figure 1 As shown in the figure, in an embodiment of the application, a time-domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation is provided, which includes a total of four steps S1-S4, which will be described in detail below.

[0041] S1, selecting a candidate target point in the target point selection area of the brain.

[0042] It should be noted that the target point selection area can be selected according to the actual stimulation needs, and can be determined by a clinician or an expert experience. Since the STN-GPi loop is the core pathway of the basal ganglia motor regulation, and studies have shown that stimulating target points such as the Subthalamic nucleus (STN) or Globu pallidus interna (GPi) can significantly improve the motor function of patients. Therefore, in the embodiment of the application, the STN-GPi loop is recommended as the target point selection area, from which the candidate target points are selected. The number of candidate target points selected from the STN-GPi loop can be determined according to actual needs, and the selection of the candidate target points can be batch selection in the iterative optimization process, or one-time selection. That is, all candidate target points can be selected at one time in the target point selection area, and then the optimal target point is selected by performing the subsequent optimization and screening process one by one. Alternatively, part of the target points can be selected in the target point selection area, the target point selection area is narrowed based on the optimization results of these target points, and the candidate target points are updated, and the screening and optimization are continued. The selection method of the candidate target points is determined according to the selected optimization algorithm, which is not limited.

[0043] S2, for each candidate target point selected at present, different stimulation electrode positions and stimulation current intensities are simulated and simulated by using the individual head model constructed for the stimulation object, the peak electric field at the candidate target point and the maximum focusing degree are taken as the optimization target, and the best stimulation electrode position and the best stimulation current intensity of each stimulation electrode are determined by the optimization algorithm.

[0044] It should be noted that when the time domain interference electric stimulation is performed on the candidate target point in the present application, the stimulation current needs to be synchronously applied through the four stimulation electrodes, therefore, the feasible solution of the set of stimulation electrode positions and stimulation current intensity in the optimization algorithm contains the spatial positions of the four stimulation electrodes relative to the head and the current intensity output by each of the four stimulation electrodes, and each set of feasible solution in the simulation needs to correspond to the setting of the stimulation electrode model at the four positions and the application of the simulation current by each stimulation electrode according to the corresponding current intensity. It should be noted that the four electrodes are the minimum configuration when the time domain interference electric stimulation is performed, and the stimulation electrodes can also be more than four if needed.

[0045] In the present application, it is recommended to apply the stimulation current by using the EEG 10-10 standard lead system, and since the positions of the stimulation electrodes in the EEG 10-10 standard lead system are relatively fixed, the optimization of the electrode positions by the optimization algorithm can be searched from all the electrode positions in the EEG 10-10 standard lead system. When the stimulation current intensity is optimized, certain limitation conditions should be met, i.e., the current intensity of a single pair is less than 2.0 mA, and the total current intensity is 4 mA, so as to avoid adverse reactions possibly caused by the excessively high current intensity.

[0046] In addition, the above optimization algorithm can be implemented by using different algorithms, and since the optimization targets are the maximization of the peak electric field at the candidate target point and the focusing degree, the joint optimization needs to be performed by simultaneously considering the two optimization targets. In actual application, the spatial positions of the four stimulation electrodes relative to the head and the current intensity output by each of the four stimulation electrodes can be optimized by using a multi-objective optimization algorithm with the peak electric field at the candidate target point and the focusing degree as the two optimization targets, or the two optimization targets can be fused into a single optimization target, and a single-objective optimization algorithm is used to optimize the spatial positions of the four stimulation electrodes relative to the head and the current intensity output by each of the four stimulation electrodes.

[0047] In the embodiments of the present application, the above optimization algorithm can use the genetic algorithm, each set of feasible solution of the genetic algorithm is the four electrode positions in the EEG 10-10 standard lead system and the current intensity of each electrode, and the fitness function of the genetic algorithm is defined as the ratio of the peak electric field at the candidate target point to the focusing degree, and the focusing degree is defined as the brain volume greater than the preset percentage of the peak electric field. Thus, the two optimization targets of the peak electric field and the focusing degree are unified into a single optimization target in the form of a ratio, and the complexity of the optimization algorithm is reduced.

[0048] It should be noted that the preset percentage of the peak electric field used for calculating the focusing degree can be adjusted according to actual optimization, and in the embodiments of the present application, it is recommended to use 85% of the peak electric field, i.e., the peak electric field E max After that, it is further judged whether the electric field intensity of each brain voxel is greater than 85% Emax The brain volume size corresponding to the voxel with the electric field intensity greater than 85% E max The brain volume size corresponding to the voxel with the electric field intensity greater than 85% E

[0049] In addition, after obtaining each candidate target point currently selected, and the best stimulating electrode position and the best stimulating current intensity determined for the candidate target point, the carrier frequency and the difference frequency of the stimulating current can be further optimized to obtain the best stimulating frequency parameter combination for the candidate target point. It should be noted that when the stimulating frequency parameter combination (i.e., the carrier frequency and the difference frequency) of the stimulating current is optimized, the corresponding optimization algorithm can also be selected according to actual needs, and a relatively complex optimization algorithm such as a genetic algorithm can also be used in theory. However, since the solution space of the stimulating frequency parameter combination optimization is small, the entire solution space can be directly traversed in the S3 step of the present application, and the computing resources and time consumed are also relatively low. The carrier frequency and the difference frequency can be uniformly sampled within their respective value ranges, then combined to form all candidate solutions, each candidate solution is traversed, and the optimal solution is selected.

[0050] In addition, it should be noted that during the execution of the above optimization algorithm, finite element simulation needs to be performed for each candidate stimulating electrode position and stimulating current intensity, and the simulation technology can be implemented by referring to the prior art. In the embodiments of the present application, the personalized head model required for simulation can be obtained from the head MRI and / or CT scan data (MRI data is recommended) of the stimulation subject through brain tissue segmentation (U-Net can be used) and three-dimensional reconstruction, and during the execution of the optimization algorithm, for each candidate stimulating electrode position and stimulating current intensity, the stimulating electrode model is loaded at the corresponding position of the personalized head model, then the finite element grid is divided, and the dielectric parameters are respectively assigned to the brain tissue and the stimulating electrode model in the personalized head model, finally the stimulating current is applied according to the corresponding stimulating current intensity, the brain electric field distribution is simulated and analyzed, and then the peak electric field at the candidate target point and the focusing degree are calculated.

[0051] S3: For each candidate target point currently selected, after arranging each stimulating electrode according to the best stimulating electrode position, the carrier frequency and the difference frequency of the stimulating current of each stimulating electrode are sampled to form a series of candidate stimulating frequency parameter combinations, the candidate stimulating frequency parameter combinations are continuously adjusted under the condition that the stimulating electrode fixedly outputs the best stimulating current intensity, different candidate stimulating frequency parameter combinations are generated in turn, the stimulating current control signals corresponding to the different candidate stimulating frequency parameter combinations are output to the stimulating electrode, and the electromyographic signals of the stimulation subject after receiving the stimulating current are collected in real time, the tremor frequency feature and the tremor intensity feature are extracted therefrom, and the tremor degree score is calculated, and the candidate stimulating frequency parameter combination with the smallest tremor degree score is selected as the best stimulating frequency parameter combination.

[0052] It should be noted that when sampling the candidate stimulation frequency parameter combination, the carrier frequency and the difference frequency each have a corresponding value space, and a series of carrier frequency values and difference frequency values can be sampled from the value space in a uniform sampling manner, and then combined in pairs to form a series of candidate stimulation frequency parameter combinations, each pair of candidate stimulation frequency parameter combinations including a specific carrier frequency value and a specific difference frequency value. Subsequently, a stimulation current control signal can be generated according to the pair of candidate stimulation frequency parameter combinations at the optimal stimulation electrode position and the optimal stimulation current intensity, so as to control the stimulation electrode to generate a stimulation current that meets the required current intensity, carrier frequency value and difference frequency value. Since there are a series of candidate stimulation frequency parameter combinations, each candidate stimulation frequency parameter combination needs to be traversed in turn in this step, and then the corresponding stimulation current control signal is generated and applied to the stimulation object, and then the electromyographic signal of the stimulation object after being stimulated by the stimulation current is collected to score the tremor degree. The control feedback process is shown in FIG. 8. Figure 2 After all the candidate stimulation frequency parameter combinations are traversed, the candidate stimulation frequency parameter combination with the minimum tremor degree score can be selected as the optimal stimulation frequency parameter combination.

[0053] In addition, in the present application, when calculating the tremor degree score, it is necessary to extract the tremor frequency feature and the tremor intensity feature from the electromyographic signal. The specific forms of the two features can be optimized according to the actual signal. In an embodiment of the present application, it is recommended to use the root mean square value of the electromyographic signal as the tremor intensity feature, and to use the zero-crossing rate of the electromyographic signal as the tremor frequency feature. The two feature parameters are weighted and summed to obtain the tremor degree score. The electromyographic signal can be collected in real time by arranging surface electromyographic sensors on the limb muscle parts of the stimulation object.

[0054] S4, performing S2 and S3 on the candidate target points in the target selection area respectively, searching for the optimal target point with the minimum tremor degree score as the target, and saving the optimal stimulation electrode position, the optimal stimulation current intensity and the optimal stimulation frequency parameter combination corresponding to the optimal target point.

[0055] It should be noted that the above-mentioned S2 and S3 steps need to be performed for each candidate target point respectively, and each time S2 and S3 are executed, the optimal stimulation electrode position, the optimal stimulation current intensity and the optimal stimulation frequency parameter combination for the current candidate target point can be selected. After all the candidate target points are traversed, the optimal target point can be selected. In theory, traversing all the candidate target points can certainly find the optimal target point, but the search efficiency of this traversal method is low, so in an embodiment of the present application, a bisection method is recommended to search for the optimal target point. For the candidate target points in the target selection area, taking the STN-GPi loop as an exemplary target selection area, the optimal target point is searched by the bisection method, and the process of the bisection method is as follows:

[0056] First, the S2 and S3 are respectively performed on the STN region and the GPi region as two candidate target points to obtain the tremor degree scores corresponding to the two candidate target points respectively;

[0057] Then, the relative sizes of the tremor degree scores of the two candidate target points are judged, the candidate target point with the smaller tremor degree score is reserved, the middle position of the two target points is selected as a new candidate target point to replace the other candidate target point with the larger tremor degree score, and the S2 and S3 are respectively performed again on the updated two candidate target points to obtain the tremor degree scores corresponding to the two candidate target points respectively.

[0058] The optimal target point with the smallest tremor degree score is obtained by continuously iterating and updating the candidate target points to approach the optimal solution until a termination condition is reached. The termination condition can be that the number of iterations reaches a preset maximum number or the change trend of the tremor degree score converges.

[0059] Therefore, in the preferred embodiment of the present application, an exemplary flow of the whole time-domain interference electric stimulation closed-loop adjustment can be seen from Figure 3 As shown in the figure. In the flow, the STN-GPi loop is taken as the recommended target point selection region, and the bisection method is used for efficient target point search. The specific implementation steps are as follows:

[0060] Step 1), first determine the initial position of the stimulation target point: select two initial candidate target points (denoted as a and b respectively) from the STN-GPi loop as the target point selection region. The initial candidate target point a is set in the STN region, and the initial candidate target point b is set in the GPi region.

[0061] Step 2), according to the currently selected target points a and b, the best stimulation electrode position and current intensity are obtained through the target optimization algorithm.

[0062] In this embodiment, the number of stimulating electrodes is 4, the optimization method of the respective optimal electrode position and current intensity is genetic algorithm, the search space of electrode position can be the EEG 10-10 standard lead system, all the electrodes in the system are labeled, the chromosome is composed of four electrode labels and their respective current intensities, the single current intensity is less than 2.0 mA, and the total current intensity is 4 mA, so as to avoid adverse reactions caused by too high current intensity, while ensuring sufficient stimulation effect; the electric field distribution is simulated and calculated according to the finite element method; the fitness function can be defined as the ratio of the peak electric field at the target point to the focusing degree, wherein the focusing degree is defined as the brain volume greater than 85% of the peak electric field; the initial population starts from 1000 randomly generated individuals, for each individual, the fitness value is calculated, according to the fitness value, the individuals with higher fitness are selected into the next generation, and 100 elites are reserved in each generation; 500 parent individuals are randomly selected to generate new offspring individuals by crossover; the newly generated offspring individuals are randomly mutated with a mutation frequency of 20% to increase population diversity; the selection, crossover and mutation operations are repeated until the maximum iteration number is reached or the fitness is no longer significantly improved; the optimal electrode position (determined according to the label), current intensity and fitness value of the four stimulating electrodes are output, and thus the optimal stimulating electrode position of the four stimulating electrodes and the optimal stimulating current intensity of each stimulating electrode are obtained.

[0063] Step 3), based on the determined optimal stimulating electrode position and optimal stimulating current intensity, the carrier frequency and difference frequency parameter combination is optimized and adjusted to find the optimal stimulating frequency parameter combination for the target point. The parameter optimization method of the carrier frequency and the difference frequency is as follows:

[0064] Under the premise of safety, according to the existing research and clinical experience of movement tremor treatment, the safety range of carrier frequency and difference frequency is set. Specifically: the carrier frequency is selected in the high frequency range (such as 1000~5000 Hz) to meet the high frequency requirement of time domain interference electric stimulation; the difference frequency is set in the tremor related frequency range of movement tremor patients (such as 1~200 Hz) to accurately regulate the tremor symptoms. Within the above safety range, the carrier frequency and the difference frequency are sampled and freely combined according to the preset step size to form a series of candidate stimulating frequency parameter combinations. Then, under the condition that the four stimulating electrodes fixedly output their respective optimal stimulating current intensities, the current frequency parameters output by the electrodes are adjusted to each candidate stimulating frequency parameter combination in turn, and the stimulating current control signals corresponding to different candidate stimulating frequency parameter combinations are generated in turn and output to the stimulating electrodes, and the electromyographic signals of the stimulated object after receiving the stimulating current are collected in real time, from which the tremor frequency feature and the tremor intensity feature are extracted and the tremor degree score is calculated, and the candidate stimulating frequency parameter combination with the smallest tremor degree score is selected as the optimal stimulating frequency parameter combination.

[0065] The process of calculating tremor severity score based on the electromyography signal can realize objective, quantitative and continuous evaluation of tremor state by analyzing the electrophysiological characteristics of tremor-related muscle groups. In the embodiments of the present application, surface electromyography sensors can be arranged on the muscle parts of the patient's forearm, leg or wrist to obtain muscle activity signals in real time, and the original electromyography signals can be preprocessed by denoising, filtering and standardization. Key parameters closely related to tremor intensity and frequency are extracted. The embodiments recommend selecting RMS (root mean square value) and ZCR (zero-crossing rate) as the core indicators for tremor state analysis. RMS is used as a tremor intensity feature to reflect the overall activation level and contraction strength of the muscle. The higher the value, the greater the degree of muscle continuous contraction or abnormal activation. ZCR is used as a tremor frequency feature to represent the frequency change of the periodic component in the electromyography signal, and its value is positively correlated with the tremor frequency. Based on these two physiological indicators, a tremor severity score (TSS) model is constructed, which can be expressed as:

[0066]

[0067] where α and β are weight coefficients that can be optimized by fitting clinical data, and RMS norm and ZCR norm are the normalized RMS and ZCR values, respectively. The scoring model quantitatively maps the tremor state to a continuous numerical interval.

[0068] Through research, it is found that the weight coefficients in the above TSS model are related to the severity of the patient's tremor. Therefore, in the embodiments of the present application, the tremor severity of the stimulation object determined by clinical evaluation is needed first. Clinical evaluation can be achieved by using clinical scale evaluation method. Then the weighted weight value pre-fitted with clinical data for the tremor severity is called to weight and sum the tremor frequency feature and the tremor intensity feature to obtain the above TSS score. The results of clinical scale evaluation can be divided into four levels: "no tremor, mild tremor, moderate tremor, severe tremor", to describe the severity of the patient's tremor. Assuming that the total score score obtained by clinical evaluation is normalized to 0~1, the level division rules are as follows: no tremor: score < 0.1; mild tremor: 0.1 ≤ score < 0.3; moderate tremor: 0.3 ≤ score < 0.6; severe tremor: score ≥ 0.6. The four tremor levels correspond to the related weight coefficient fitting values respectively:

[0069]

[0070] In addition to the present embodiment, the tremor degree score can also be solved by fusing the indicators such as the waveform length, the average absolute value, the tremor frequency band power ratio, the power spectral density, the center frequency, the frequency band width, the tremor activity transient enhancement zone, etc. of the electromyogram.

[0071] Step 4, based on the two candidate target points a and b currently selected, the optimal target point corresponding to the best stimulation electrode position, the best stimulation current intensity and the best stimulation frequency parameter combination can be obtained through the above steps, and the corresponding tremor degree score TSS can also be obtained. Then, the target points are selected by distance iteration step by step, and the best stimulation electrode position is obtained according to the selected target point. The selection method of the target point is dichotomy, and the selection method is as follows:

[0072] For the target point a initially set in the STN region and the target point b initially set in the GPi region, the tremor degree scores of the two under the best stimulation electrode position, the best stimulation current intensity and the best stimulation frequency parameter combination can be compared. The smaller the tremor degree score is, the better the stimulation effect is, so the relative advantages and disadvantages of the stimulation effects of the two target points can be compared, and then the candidate target point with the smaller tremor degree score is retained, and the middle position of the two target points is selected as a new candidate target point to replace the other candidate target point with the larger tremor degree score, and the optimization steps described above are performed again for the updated two candidate target points. In the present application, the target point a and the target point b are taken as examples, and the updating process is as follows:

[0073] The stimulation effects produced by the stimulation parameter combinations corresponding to the target point a and the target point b are compared. If the stimulation effect produced by the stimulation parameter combination corresponding to the target point a is better than that of the target point b, the target point selection area is reduced to the loop between the target point a and the midpoint c of a and b next time, that is, the target point b is replaced by the position of c, and the optimization of the stimulation parameters is performed again, and the stimulation effect is compared with that of the target point a. If the stimulation effect produced by the stimulation parameter combination corresponding to the target point a is worse than that of the target point b, the target point selection area is reduced to the loop between the target point b and the midpoint c of a and b next time, that is, the target point a is replaced by the position of c, and the optimization of the stimulation parameters is performed again, and the stimulation effect is compared with that of the target point b.

[0074] Therefore, the dichotomy described above can quickly and efficiently find the target point that minimizes the tremor degree of the patient by continuously reducing the target point selection range, and avoids global traversal. When the iteration number is set to complete or the tremor degree is not obviously improved after the multiple optimization stimulations, the algorithm execution is ended, and the target point and the stimulation parameter combination corresponding to the best stimulation effect are selected.

[0075] Therefore, the time domain interference electric stimulation closed-loop regulation method for tremor rehabilitation provided by the application can dynamically adjust the stimulation parameters according to the real-time tremor degree of the patient through the closed-loop feedback method, and generate a personalized stimulation scheme. This dynamic regulation method based on the individual characteristics of the patient can better adapt to the changes in the patient's condition.

[0076] It should be particularly pointed out that the time domain interference electric stimulation closed-loop regulation process of the above S1-S4 steps only describes the regulation output of the stimulation signal and the feedback optimization process based on the collected electromyographic signal, but does not involve the specific electric stimulation treatment process, that is, the above S1-S4 of the application correspond to the signal feedback control and optimization of the instrument equipment level, but the output of the electric stimulation current and the action on the target position of the actual patient belong to the process outside the S1-S4 of the application. Therefore, the technical solution only involves the signal processing process, but does not involve the disease treatment process.

[0077] In another embodiment of the application, a time domain interference electric stimulation closed-loop regulation system for tremor rehabilitation can be further provided, which is used to implement the time domain interference electric stimulation closed-loop regulation method for tremor rehabilitation shown in the above S1-S4 steps. In the system, the core includes a signal acquisition unit, a stimulation execution unit and a closed-loop regulation unit. The specific form of each unit and the mutual cooperation relationship are described in detail below.

[0078] The above signal acquisition unit is used to acquire the electromyographic signal of the muscle part of the extremities of the stimulation object in real time;

[0079] The above stimulation execution unit is used to generate a stimulation current control signal meeting the intensity and frequency requirements through a signal generator according to the received stimulation current intensity and stimulation frequency parameters, and output the stimulation current control signal to the stimulation electrode to generate a corresponding stimulation current;

[0080] The above closed-loop regulation unit is used to continuously regulate the stimulation current output by the stimulation execution module according to the electromyographic signal acquired in real time by the signal acquisition module in the process of iteratively executing the S2 and S3 according to the time domain interference electric stimulation closed-loop regulation method for tremor rehabilitation in the foregoing embodiments, until the optimal target point with the minimum tremor degree score and the combination of the best stimulation electrode position, the best stimulation current intensity and the best stimulation frequency parameter corresponding to the optimal target point are obtained.

[0081] It should be pointed out that the above signal acquisition unit can be a sensor capable of accurately detecting the electromyographic signal and a corresponding signal processing circuit, for example, a mature electromyographic amplifier and electromyographic electrode combination scheme can be used to realize it. In addition,

[0082] It should be noted that the above signal acquisition unit and stimulation execution unit are hardware modules, and the main function of the closed-loop regulation unit is realized through a software module, but can be carried on an electronic device with data processing and signal transceiver capability to realize. The electronic device cooperates with the signal acquisition unit and the stimulation execution unit to realize the time-domain interference electric stimulation closed-loop regulation method for tremor rehabilitation in the foregoing embodiments as a whole, and finally outputs the stimulation current under the combination of the optimal stimulation electrode position, the optimal stimulation current intensity and the optimal stimulation frequency parameter through the stimulation execution unit. Further, the stimulation current acts on the optimal target point of the stimulation object, i.e., the patient, to obtain the best treatment effect.

[0083] The stimulation execution unit can be implemented by referring to the transcranial electric stimulation system in the prior art. The signal control part inside the stimulation execution unit is composed of a constant current source, a voltage boost pump, a waveform generator and the like, and can output an electric stimulation waveform with settable current intensity, frequency and time interval parameters. The waveform synthesizer, the constant current source and the voltage boost pump can adopt an integrated chip form of a finished product. Such a chip can modify the chip parameters through programming. In the embodiments of the present application, the output channel of the stimulation execution module is at least 4, because the time-domain interference electric stimulation for tremor rehabilitation needs to arrange 4 stimulation electrodes at the same time.

[0084] The closed-loop regulation unit is the core of the regulation and control of the present application, and the S1-S4 steps executed inside the closed-loop regulation unit can be further realized by a plurality of software function modules. Referring to Figure 4As shown, in the embodiment of the present application, the feedback processing subunit, the first optimization subunit and the second optimization subunit can be divided. The feedback processing subunit is used to control the loop of the parameter closed loop condition, which includes a tremor degree evaluation module and an execution decision module, the tremor degree evaluation module is used to extract the tremor frequency feature and the tremor intensity feature from the electrocardiosignal and perform weighted processing to obtain the tremor degree score of each round of loop, so as to quantitatively reflect the severity of the tremor and record; the execution decision module decides whether to continue to execute the iteration step according to the tremor degree score. The first optimization subunit and the second optimization subunit are both parameter optimization units, wherein the first optimization subunit includes a target point selection module and an electrode position and current intensity optimization module, and the second optimization subunit includes a frequency optimization module and a feedback module. The first optimization subunit optimizes the electrode position based on the EEG 10-10 standard lead system (that is, selects 4 electrodes from the system to be activated and output), wherein the target point selection module selects the STN-GPi loop as the target point selection area, and gradually narrows the target point range through dichotomy; the electrode position and current intensity optimization module obtains the best stimulation electrode position and the best current intensity according to the selected target point through multi-objective optimization; the second optimization subunit is based on the determined electrode position and the best current intensity, wherein the frequency optimization module freely combines the carrier frequency and the difference frequency of the current according to the preset safety range and the step size, and sequentially outputs the parameter combination meeting the requirements to the stimulation execution unit; the feedback module finds the stimulation parameter combination that minimizes the tremor degree for the determined target point according to the tremor degree evaluation module in the feedback processing unit and stores it, that is, saves the finally optimized parameters (the best stimulation current intensity and the best stimulation frequency parameter combination).

[0085] It should be noted that the method steps S1-S4 described above can be essentially realized in the form of a computer program or a software functional module.

[0086] Therefore, based on the same inventive concept, as shown, Figure 5 The present application also provides a computer electronic device corresponding to the time domain interference electric stimulation closed loop adjustment method for tremor rehabilitation provided by the above-mentioned embodiment, which comprises a memory and a processor.

[0087] The memory is used to store a computer program.

[0088] The processor is used to realize the time domain interference electric stimulation closed loop adjustment method for tremor rehabilitation as described above when the computer program is executed.

[0089] In addition, the logic instructions in the memory described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application.

[0090] Therefore, based on the same inventive concept, the present application provides a computer readable storage medium corresponding to the time domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation, and the storage medium stores a computer program. When the computer program is executed by a processor, the method for tremor rehabilitation can be realized.

[0091] Therefore, based on the same inventive concept, the present application provides a computer program product, including computer programs / instructions, which can be executed by a processor to realize the time domain interference electric stimulation closed-loop adjustment method for tremor rehabilitation as described above.

[0092] Specifically, in the computer readable storage medium of the above three embodiments, the computer program stored therein can be executed by a processor to execute the steps S1-S4.

[0093] It can be understood that the storage medium can include a random access memory (RAM) and a non-volatile memory (NVM), such as at least one disk memory. Meanwhile, the storage medium can also be a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. various media that can store program codes.

[0094] It can be understood that the processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. It can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0095] In addition, it should be noted that, for the convenience and brevity of the description, the specific working process of the system described above can refer to the corresponding process in the foregoing method embodiments, and will not be described here. In the embodiments provided in the present application, the division of steps or modules in the system and method described is only a logical functional division, and there can be another division manner in actual implementation, for example, multiple modules or steps can be combined or integrated together, or a module or step can be split.

[0096] The above-described embodiments are only some of the preferred implementation schemes of the present application, but not intended to limit the present application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation shall fall within the protection scope of the present application.

Claims

1. A time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation, characterized in that, include: The signal acquisition unit is used to acquire electromyographic signals from the muscles of the limbs of the stimulated object in real time. The stimulation execution unit is used to generate a stimulation current control signal that meets the intensity and frequency requirements based on the received stimulation current intensity and stimulation frequency parameters through a signal generator, and output it to the stimulation electrode to generate a corresponding stimulation current. A closed-loop modulation unit for executing a time-domain interferometric electrical stimulation closed-loop modulation method for tremor rehabilitation, the method comprising: S1. Select candidate targets in the target selection area of ​​the brain; S2. For each candidate target point currently selected, a personalized head model constructed for the stimulation object is used to simulate different stimulation electrode positions and stimulation current intensities. The optimization goal is to maximize the peak electric field and focus at the candidate target point. The optimal stimulation electrode position and the optimal stimulation current intensity of each stimulation electrode are determined through optimization algorithms. S3: For each candidate target point currently selected, after arranging each stimulation electrode according to the optimal stimulation electrode position, the carrier frequency and difference frequency of the stimulation current of each stimulation electrode are sampled to form a series of candidate stimulation frequency parameter combinations. While the stimulation electrode outputs the optimal stimulation current intensity, the candidate stimulation frequency parameter combinations are continuously adjusted to generate stimulation current control signals corresponding to different candidate stimulation frequency parameter combinations in sequence and output to the stimulation electrode. The electromyographic signals of the stimulated object after receiving the stimulation current are collected in real time, and the tremor frequency characteristics and tremor intensity characteristics are extracted from them and the tremor degree score is calculated. The candidate stimulation frequency parameter combination with the smallest tremor degree score is selected as the optimal stimulation frequency parameter combination. S4. Perform S2 and S3 on the candidate target points in the target selection area respectively, with the goal of minimizing the tremor severity score, search for the optimal target point, and save the optimal stimulation electrode position, optimal stimulation current intensity and optimal stimulation frequency parameter combination corresponding to the optimal target point. Furthermore, during the iterative execution of S2 and S3, the closed-loop adjustment unit continuously adjusts the stimulation current output by the stimulation execution module based on the electromyographic signals collected in real time by the signal acquisition module, until the optimal target point with the lowest tremor score is obtained, as well as the optimal combination of the optimal stimulation electrode position, the optimal stimulation current intensity, and the optimal stimulation frequency parameters corresponding to the optimal target point.

2. The time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation as described in claim 1, characterized in that, For candidate target points in the target selection region, using the STN-GPi loop as the target selection region, the optimal target point is searched using a binary search method. The binary search process is as follows: First, using the STN region and the GPi region as two candidate targets, S2 and S3 are executed respectively to obtain the tremor severity scores corresponding to the two candidate targets. Then, the relative magnitude of the tremor severity scores of the two candidate targets is determined. The candidate target with the smaller tremor severity score is retained, and the middle position of the two targets is selected as a new candidate target to replace the other candidate target with the larger tremor severity score. Then, S2 and S3 are executed again for the two updated candidate targets to obtain the tremor severity scores corresponding to the two candidate targets respectively. By iteratively updating candidate targets to approximate the optimal solution until the termination condition is met, the optimal target with the minimum tremor score is obtained.

3. The time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation as described in claim 1, characterized in that, The optimization algorithm uses a genetic algorithm. Each feasible solution of the genetic algorithm is at least four electrode positions in the EEG 10-10 standard lead system and the current intensity of each electrode. The fitness function of the genetic algorithm is defined as the ratio of the peak electric field to the focus at the candidate target point. The focus is defined as the brain volume that is greater than a preset percentage of the peak electric field.

4. The time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation as described in claim 1, characterized in that, When calculating the tremor severity score, the root mean square value of the electromyography (EMG) signal is used as the tremor intensity feature, and the zero crossover rate of the EMG signal is used as the tremor frequency feature. The two feature parameters are weighted and summed to obtain the tremor severity score.

5. The time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation as described in claim 4, characterized in that, When calculating the tremor severity score, it is necessary to first determine the severity of the tremor of the stimulus subject based on the clinical assessment, and then use the weighted weight values ​​obtained by fitting clinical data in advance for the severity of the tremor to perform a weighted summation of the tremor frequency characteristics and tremor intensity characteristics.

6. The time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation as described in claim 1, characterized in that, The electromyographic signals are collected in real time by deploying surface electromyographic sensors on the muscles of the limbs of the stimulated subject.

7. The time-domain interferometric electrical stimulation closed-loop modulation system for tremor rehabilitation as described in claim 1, characterized in that, The personalized head model is obtained by brain tissue segmentation and three-dimensional reconstruction from the head MRI and / or CT scan data of the stimulated object. During the execution of the optimization algorithm, for each group of candidate stimulation electrode positions and stimulation current intensities, stimulation electrode models are loaded at the corresponding positions of the personalized head model. Then, finite element meshing is performed, and dielectric parameters are assigned to the brain tissue and stimulation electrode models in the personalized head model respectively. Finally, stimulation current is applied according to the corresponding stimulation current intensity, and the brain electric field distribution is simulated to obtain the peak electric field and focus at the candidate target point.

8. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, this computer program / instruction enables a time-domain interferometric electrical stimulation closed-loop modulation method for tremor rehabilitation, comprising: S1. Select candidate targets in the target selection area of ​​the brain; S2. For each candidate target point currently selected, a personalized head model constructed for the stimulation object is used to simulate different stimulation electrode positions and stimulation current intensities. The optimization goal is to maximize the peak electric field and focus at the candidate target point. The optimal stimulation electrode position and the optimal stimulation current intensity of each stimulation electrode are determined through optimization algorithms. S3: For each candidate target point currently selected, after arranging each stimulation electrode according to the optimal stimulation electrode position, the carrier frequency and difference frequency of the stimulation current of each stimulation electrode are sampled to form a series of candidate stimulation frequency parameter combinations. While the stimulation electrode outputs the optimal stimulation current intensity, the candidate stimulation frequency parameter combinations are continuously adjusted to generate stimulation current control signals corresponding to different candidate stimulation frequency parameter combinations in sequence and output to the stimulation electrode. The electromyographic signals of the stimulated object after receiving the stimulation current are collected in real time, and the tremor frequency characteristics and tremor intensity characteristics are extracted from them and the tremor degree score is calculated. The candidate stimulation frequency parameter combination with the smallest tremor degree score is selected as the optimal stimulation frequency parameter combination. S4. Perform S2 and S3 on the candidate target points in the target selection area respectively, with the goal of minimizing the tremor severity score, search for the optimal target point, and save the optimal stimulation electrode position, optimal stimulation current intensity and optimal stimulation frequency parameter combination corresponding to the optimal target point.

9. The computer program product as described in claim 8, characterized in that, For candidate target points in the target selection region, using the STN-GPi loop as the target selection region, the optimal target point is searched using a binary search method. The binary search process is as follows: First, using the STN region and the GPi region as two candidate targets, S2 and S3 are executed respectively to obtain the tremor severity scores corresponding to the two candidate targets. Then, the relative magnitude of the tremor severity scores of the two candidate targets is determined. The candidate target with the smaller tremor severity score is retained, and the middle position of the two targets is selected as a new candidate target to replace the other candidate target with the larger tremor severity score. Then, S2 and S3 are executed again for the two updated candidate targets to obtain the tremor severity scores corresponding to the two candidate targets respectively. By iteratively updating candidate targets to approximate the optimal solution until the termination condition is met, the optimal target with the minimum tremor score is obtained.

10. The computer program product as claimed in claim 8, characterized in that, The optimization algorithm uses a genetic algorithm. Each feasible solution of the genetic algorithm is at least four electrode positions in the EEG 10-10 standard lead system and the current intensity of each electrode. The fitness function of the genetic algorithm is defined as the ratio of the peak electric field to the focus at the candidate target point. The focus is defined as the brain volume that is greater than a preset percentage of the peak electric field.

11. The computer program product as described in claim 8, characterized in that, When calculating the tremor severity score, the root mean square value of the electromyography (EMG) signal is used as the tremor intensity feature, and the zero crossover rate of the EMG signal is used as the tremor frequency feature. The two feature parameters are weighted and summed to obtain the tremor severity score.

12. The computer program product as described in claim 11, characterized in that, When calculating the tremor severity score, it is necessary to first determine the severity of the tremor of the stimulus subject based on the clinical assessment, and then use the weighted weight values ​​obtained by fitting clinical data in advance for the severity of the tremor to perform a weighted summation of the tremor frequency characteristics and tremor intensity characteristics.

13. The computer program product as described in claim 8, characterized in that, The electromyographic signals are collected in real time by deploying surface electromyographic sensors on the muscles of the limbs of the stimulated subject.

14. The computer program product as described in claim 8, characterized in that, The personalized head model is obtained by brain tissue segmentation and three-dimensional reconstruction from the head MRI and / or CT scan data of the stimulated object. During the execution of the optimization algorithm, for each group of candidate stimulation electrode positions and stimulation current intensities, stimulation electrode models are loaded at the corresponding positions of the personalized head model. Then, finite element meshing is performed, and dielectric parameters are assigned to the brain tissue and stimulation electrode models in the personalized head model respectively. Finally, stimulation current is applied according to the corresponding stimulation current intensity, and the brain electric field distribution is simulated to obtain the peak electric field and focus at the candidate target point.

15. A computer electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor, when executing the computer program, is configured to implement a time-domain interferometric electrical stimulation closed-loop modulation method for tremor rehabilitation, the method comprising: S1. Select candidate targets in the target selection area of ​​the brain; S2. For each candidate target point currently selected, a personalized head model constructed for the stimulation object is used to simulate different stimulation electrode positions and stimulation current intensities. The optimization goal is to maximize the peak electric field and focus at the candidate target point. The optimal stimulation electrode position and the optimal stimulation current intensity of each stimulation electrode are determined through optimization algorithms. S3: For each candidate target point currently selected, after arranging each stimulation electrode according to the optimal stimulation electrode position, the carrier frequency and difference frequency of the stimulation current of each stimulation electrode are sampled to form a series of candidate stimulation frequency parameter combinations. While the stimulation electrode outputs the optimal stimulation current intensity, the candidate stimulation frequency parameter combinations are continuously adjusted to generate stimulation current control signals corresponding to different candidate stimulation frequency parameter combinations in sequence and output to the stimulation electrode. The electromyographic signals of the stimulated object after receiving the stimulation current are collected in real time, and the tremor frequency characteristics and tremor intensity characteristics are extracted from them and the tremor degree score is calculated. The candidate stimulation frequency parameter combination with the smallest tremor degree score is selected as the optimal stimulation frequency parameter combination. S4. Perform S2 and S3 on the candidate target points in the target selection area respectively, with the goal of minimizing the tremor severity score, search for the optimal target point, and save the optimal stimulation electrode position, optimal stimulation current intensity and optimal stimulation frequency parameter combination corresponding to the optimal target point.

16. The computer electronic device as claimed in claim 15, characterized in that, For candidate target points in the target selection region, using the STN-GPi loop as the target selection region, the optimal target point is searched using a binary search method. The binary search process is as follows: First, using the STN region and the GPi region as two candidate targets, S2 and S3 are executed respectively to obtain the tremor severity scores corresponding to the two candidate targets. Then, the relative magnitude of the tremor severity scores of the two candidate targets is determined. The candidate target with the smaller tremor severity score is retained, and the middle position of the two targets is selected as a new candidate target to replace the other candidate target with the larger tremor severity score. Then, S2 and S3 are executed again for the two updated candidate targets to obtain the tremor severity scores corresponding to the two candidate targets respectively. By iteratively updating candidate targets to approximate the optimal solution until the termination condition is met, the optimal target with the minimum tremor score is obtained.

17. The computer electronic device as claimed in claim 15, characterized in that, The optimization algorithm uses a genetic algorithm. Each feasible solution of the genetic algorithm is at least four electrode positions in the EEG 10-10 standard lead system and the current intensity of each electrode. The fitness function of the genetic algorithm is defined as the ratio of the peak electric field to the focus at the candidate target point. The focus is defined as the brain volume that is greater than a preset percentage of the peak electric field.

18. The computer electronic device as claimed in claim 15, characterized in that, When calculating the tremor severity score, the root mean square value of the electromyography (EMG) signal is used as the tremor intensity feature, and the zero crossover rate of the EMG signal is used as the tremor frequency feature. The two feature parameters are weighted and summed to obtain the tremor severity score.

19. The computer electronic device as claimed in claim 18, characterized in that, When calculating the tremor severity score, it is necessary to first determine the severity of the tremor of the stimulus subject based on the clinical assessment, and then use the weighted weight values ​​obtained by fitting clinical data in advance for the severity of the tremor to perform a weighted summation of the tremor frequency characteristics and tremor intensity characteristics.

20. The computer electronic device as claimed in claim 15, characterized in that, The electromyographic signals are collected in real time by deploying surface electromyographic sensors on the muscles of the limbs of the stimulated subject.

21. The computer electronic device as claimed in claim 15, characterized in that, The personalized head model is obtained by brain tissue segmentation and three-dimensional reconstruction from the head MRI and / or CT scan data of the stimulated object. During the execution of the optimization algorithm, for each group of candidate stimulation electrode positions and stimulation current intensities, stimulation electrode models are loaded at the corresponding positions of the personalized head model. Then, finite element meshing is performed, and dielectric parameters are assigned to the brain tissue and stimulation electrode models in the personalized head model respectively. Finally, stimulation current is applied according to the corresponding stimulation current intensity, and the brain electric field distribution is simulated to obtain the peak electric field and focus at the candidate target point.

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