Ddyskinesia treatment effect evaluation system based on HD-sEMG and neuron analysis

Abstract

The invention belongs to the technical field of medical diagnosis and treatment evaluation, and particularly relates to a dyskinesia treatment effect evaluation system based on HD-sEMG and neuron analysis. According to the method, the pathological tremor symptom of the hand of the dyskinesia patient is induced, front arm HD-sEMG data are collected, and the improvement effect of the pathological tremor symptom of the dyskinesia patient before and after DBS treatment is analyzed and evaluated; the system comprises: a data acquisition platform for recording surface myoelectricity data of forearm flexor muscle and extensor muscle; the data preprocessing module is used for removing noise and baseline drift through filtering and improving the signal quality; the motion unit decomposition and recognition module is used for decomposing and recognizing the motion units of the flexor muscle and the extensor muscle of the forearm; the microcosmic neuron characteristic analysis module is used for analyzing neuron discharge characteristics and reflecting the change condition of neural control; the improvement condition of tremor symptoms is evaluated by comparing neuron characteristic changes before and after brain deep electrical stimulation treatment, and a doctor is assisted in optimizing a treatment scheme, so that the treatment effect is improved, and the life quality of a patient is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical diagnosis and treatment evaluation, and specifically relates to a system for evaluating the therapeutic effect of deep brain electrical stimulation on pathological tremor symptoms in the hands of patients with movement disorders. Background Art

[0002] Movement disorders are a class of neurological diseases that affect motor control, usually resulting in abnormalities in both voluntary and involuntary movements. Tremor is a common movement disorder that manifests as involuntary, rhythmic tremors in a part of the body. Parkinson's disease is one of the most common pathological tremor diseases, which manifests as resting tremors due to impaired motor control function caused by the degeneration of the dopamine system. Dystonia can also cause pathological tremors, which are usually exacerbated during specific postures or movements. Pathological tremors seriously affect patients' daily life functions, social interactions, mental health, and overall quality of life. Therefore, early diagnosis and comprehensive assessment strategies are essential to reduce the negative impact of pathological tremors on patients' lives. Deep brain stimulation (DBS) is a treatment method that implants electrodes in specific areas of the brain and delivers electrical stimulation to these areas, thereby regulating abnormal neural activity and improving patients' pathological tremors. DBS has shown significant effects in improving movement disorder symptoms, especially for pathological tremor symptoms that are difficult to control with drugs.

[0003] At present, the diagnosis and evaluation of pathological tremor mostly rely on the results of neurological examinations and tremor assessment scales. However, these traditional assessment techniques and methods may have some limitations and disadvantages: (1) Invasiveness and discomfort: Neurological examinations may require patients to undergo more invasive examinations. For example, detailed neurological examinations may require some patients to endure uncomfortable examinations. For example, acupuncture electromyography. (2) Restricted to specific environments: Some patients with movement disorders need to undergo magnetic resonance imaging to further investigate the patient's intracranial conditions. However, magnetic resonance imaging requires the patient to be still. Patients with head or limb tremors are prone to imaging artifacts, which affects the assessment of intracranial tremor-related conditions. Some auxiliary examinations such as magnetic resonance imaging require specific facilities and environments, which limits their application in outpatient or mobile diagnostic environments. (3) Subjectivity: Tremor assessment scales may be affected by the patient's subjective report and the doctor's subjective evaluation. There are certain problems of subjectivity and measurement inaccuracy, and there is a lack of detailed analysis of the microscopic level of neuromuscular activity.

[0004] To overcome these shortcomings, an evaluation method based on surface electromyography (sEMG) technology has emerged to provide more objective and accurate evaluation data. sEMG records the electrical signals generated by neurons when muscles are active. These signals are transmitted to the skin surface through muscle fibers and subcutaneous tissue to form electrical signals. Pathological tremor is a typical manifestation of the peripheral nervous system in patients with movement disorders, manifested as continuous excitation and contraction of muscles. Therefore, the severity of pathological tremor symptoms in patients with movement disorders can be evaluated by directly recording muscle electrical activity. On the one hand, sEMG technology records muscle electrical activity by placing electrodes on the skin surface, which is non-invasive and more comfortable and safe for patients. Tremors can be repeatedly measured at different time points and conditions, without being restricted by the environment. On the other hand, objective physiological signals are conducive to avoiding the limitations of subjective evaluation, and can provide detailed analysis of motor unit activity at the microscopic level by capturing subtle changes in neuromuscular activity.

[0005] At present, researchers have proposed a variety of methods to evaluate the changes in muscle activity characteristics of patients before and after DBS from the perspective of sEMG, mainly focusing on the analysis of electromyographic characteristics at the macro level. Commonly used methods include spectrum analysis, signal morphology analysis, and nonlinear dynamics analysis. These methods mostly use monopolar or bipolar electrodes to evaluate the effects of different DBS parameter settings on pathological tremor symptoms. Existing solutions mainly include:

[0006] 1. A method for analyzing the differences in sEMG signal characteristics of patients with Parkinson's disease pathological tremor under different DBS conditions. This method uses bipolar electrodes and analyzes the electromyographic characteristics of 13 patients with Parkinson's disease pathological tremor based on spectrum, signal morphology and nonlinear dynamics. The therapeutic effect of DBS is evaluated by quantifying the electromyographic signal characteristics under different DBS parameter settings (pulse amplitude, frequency and width) [1].

[0007] 2. A method for evaluating the effect of DBS on Parkinson's disease pathological tremor based on the principal component tracking method. This method uses monopolar or bipolar electrodes (depending on the patient) to analyze the sEMG data of 13 Parkinson's disease pathological tremor patients in the DBS-on and DBS-off states and 13 healthy controls based on the correlation dimension and recurrence rate characteristics. This method evaluates the treatment effect by quantifying the effect of DBS on the neuromuscular function of patients with Parkinson's disease pathological tremor [2].

[0008] These existing technical solutions mainly analyze the tremor improvement effect before and after DBS by the amplitude, frequency and nonlinear characteristics of sEMG signals, but have the following shortcomings:

[0009] Defect 1: Limitations of high-density surface electromyography acquisition. Existing methods mostly use monopolar or bipolar electrodes, which have limited coverage and number of channels for acquiring signals, and are usually insufficient to fully capture the electromyographic activity of the target muscle group. Due to the limited number of electrodes, it is difficult to ensure the comprehensiveness and accuracy of the data, and some key electromyographic signal features may be missed, affecting the accuracy of the analysis results.

[0010] Defect 2: Limitation of macroscopic EMG signal characteristics. Existing methods mainly focus on the macroscopic EMG signal characteristics, mostly based on the analysis of parameters such as amplitude and frequency based on spectrum, signal morphology and nonlinear dynamics, but do not conduct in-depth research on the microscopic level of neuronal discharge characteristics and laws. This deficiency limits the comprehensive understanding of the changes in neuromuscular activity before and after DBS stimulation. Summary of the invention

[0011] The purpose of the present invention is to provide a movement disorder treatment effect evaluation system based on HD-sEMG and neuron analysis to evaluate the improvement of tremor symptoms before and after DBS treatment and help doctors formulate and optimize treatment plans.

[0012] The movement disorder treatment effect evaluation system based on HD-sEMG and neuron analysis provided by the present invention induces pathological tremor symptoms in the hands of patients with movement disorders, collects HD surface electromyography data of the forearm, and analyzes its micro-neuron characteristics, so as to accurately evaluate the improvement effect of pathological tremor symptoms in patients with movement disorders before and after DBS treatment. Specifically, it includes: a data acquisition module for pathological tremor data of the hands of patients with movement disorders, a data preprocessing module, a motor unit decomposition and identification module, and a micro-neuron characteristic analysis module; wherein:

[0013] (i) The data acquisition platform is a HD-sEMG acquisition device used to record 256-channel surface electromyography data of the forearm flexor and extensor muscle groups of patients with movement disorders. This high-density data acquisition capability allows for more accurate analysis of muscle activity patterns and motor unit characteristics.

[0014] Specifically, 4 electrode arrays are used to collect HD-sEMG signals from a single forearm; each electrode array consists of 64 gel circular electrodes, and the direction of the electrode array is roughly aligned with the muscle fibers of patients with movement disorders. Among them, 2 electrode arrays are placed on the flexor muscle group, located above the radial extensor carpi and the extensor digitorum respectively; 2 electrode arrays are placed on the extensor muscle group; located above the radial extensor carpi and the extensor digitorum respectively; the collected electromyographic signals will be stored in the built-in SD card of each acquisition device to ensure the integrity and stability of the data.

[0015] (ii) The data preprocessing module preprocesses the original electromyographic signal through steps such as preliminary filtering, LMS filter, baseline drift correction and signal normalization. Preliminary filtering is used to remove power frequency interference and retain the effective frequency range, the LMS filter adaptively adjusts the coefficient to eliminate dynamic noise, the baseline drift correction removes chronic drift through polynomial fitting, and finally normalization is used to ensure the consistency of signal amplitude, thereby improving the clarity and accuracy of the signal.

[0016] Specifically:

[0017] (1) Preliminary filtering: Apply low-pass filter and band-pass filter to perform preliminary filtering (10 Hz to 500 Hz) on the original EMG signal to remove power frequency interference and retain the effective signal frequency range. The low-pass filter is used to remove high-frequency noise above the signal frequency range. The band-pass filter is used to retain the main components of the EMG signal and remove irrelevant low-frequency and high-frequency noise.

[0018] (2) LMS filter: After preliminary filtering, the least mean square (LMS) adaptive filter is used to adjust the filter coefficients in real time to minimize the error between the output signal and the desired signal, thereby eliminating dynamic noise and improving signal quality. The error signal is determined by the difference between the desired signal and the filter output. The core of LMS is to gradually adjust the filter coefficients ω based on the gradient descent method. i (n) to minimize the mean square value of the error signal. By using the objective function J(n) to control the filter coefficient ω i (n) Calculate the partial derivative, obtain the gradient, and use the gradient descent method to update the filter coefficients.

[0019] (3) Baseline drift correction: Baseline drift is a low-frequency component caused by changes in the impedance of the electrode-skin interface or other chronic interference. The nonlinear trend of the signal can be corrected and the baseline drift can be removed by polynomial fitting technology. The given signal is fitted using the least squares method to obtain the polynomial coefficients, and then the drift signal is subtracted from the original signal.

[0020] (4) Signal normalization: The signal amplitude is normalized to the range of [0, 1] to ensure the scale consistency of the signal and reduce the impact of the impedance difference of the electrode-skin interface.

[0021] The above-mentioned preprocessing module can effectively cope with complex signal environments, improve the clarity and accuracy of signals while removing noise and drift, and provide reliable input for subsequent signal analysis and processing.

[0022] (III) The motor unit decomposition and identification module is used to perform motor unit decomposition on the flexor muscle groups and extensor muscle groups of patients with movement disorders, respectively, including:

[0023] (1) De-meaning, radial basis function transformation and principal component analysis submodules: De-meaning eliminates the baseline offset of the signal to ensure the accuracy of the analysis; radial basis function transformation is used to map the signal to a nonlinear high-dimensional space to better capture complex signal characteristics; principal component analysis is used to reduce the dimensionality of high-dimensional data, extract the main features, reduce the impact of noise and improve the efficiency and accuracy of subsequent analysis;

[0024] (2) The motor unit extraction submodule uses the FastICA algorithm combined with the q-non-Gaussianity metric to decompose the reduced-dimensional electromyographic signal. The q-non-Gaussianity metric provides better robustness by introducing Tsallis entropy and adjusting parameters, and can effectively handle noise and outliers. The weight vector is optimized through fixed-point iteration and updated to maximize the q-non-Gaussianity metric. In each iteration, Gram-Schmidt orthogonalization is performed to ensure the orthogonality between independent components and maintain the validity of the decomposition results.

[0025] (3) An effective motor unit identification submodule uses Morlet wavelet to decompose the electromyographic signal according to the decomposed independent components, and generates a 01 value time series of the motor unit discharge moment by detecting the significant local extreme values ​​in the wavelet coefficients and binarizing them;

[0026] (4) Screening and post-processing submodule, i.e., screening and post-processing the obtained discharge time sequence; including extracting the main frequency components in the frequency range of 4 Hz to 35 Hz by Fourier transform and retaining the motor unit sources that meet this frequency range; identifying the motor unit source pairs with synchronous discharge by calculating the cross-correlation function and constructing the synchronization matrix; for the identified synchronization sources, comprehensively scoring them through signal energy and stability evaluation, and finally retaining the synchronization source with the highest score, thereby ensuring the quality and stability of the selected motor unit source.

[0027] Further:

[0028] (1) De-meaning, radial basis function transformation and principal component analysis submodules; where:

[0029] The main purpose of de-averaging is to eliminate the baseline drift in the signal so that the signal data is concentrated on 0. Specifically, the mean of each channel is calculated, and the mean of each channel is subtracted from each data point of each channel to obtain the de-averaged sEMG signal.

[0030] The radial basis function (RBF) transform maps the de-averaged sEMG signal to a high-dimensional feature space, thereby capturing the complex nonlinear characteristics of the signal, making the nonlinear relationship between independent components more significant, and better reversely solving the discharge pulse signal of motor neurons, so that subsequent processing (such as principal component analysis or motor unit decomposition) can more effectively identify and separate different motor units.

[0031] Principal component analysis, including standardizing the data after RBF transformation so that each feature has zero mean and unit variance to ensure that different features have the same scale. Calculate the covariance matrix of the standardized data matrix and add regularization terms to the covariance matrix to improve its stability and reduce the impact of noise.

[0032] Perform eigenvalue decomposition on the regularized covariance matrix to obtain eigenvalues ​​and eigenvectors. Select the first k eigenvectors with the largest eigenvalues ​​as principal components, and these eigenvectors form a new feature space. Use the selected principal components to project the standardized data to complete dimensionality reduction.

[0033] (2) The motor unit extraction submodule uses the FastICA algorithm combined with the q-non-Gaussianity metric to reversely solve the reduced-dimensional electromyographic signals to separate the individual motor units.

[0034] Traditional FastICA algorithms usually use negentropy as a measure of non-Gaussianity. Negentropy uses the difference in entropy to measure the non-Gaussianity of a signal. However, negentropy is sensitive to outliers and noise, which may lead to a decrease in the accuracy of independent component extraction in the presence of noise. The q-non-Gaussianity measure provides a more generalized non-Gaussianity measure by introducing Tsallis entropy. Compared with traditional non-Gaussianity measures (such as measures based on negentropy), the q-non-Gaussianity measure introduces an adjustable parameter q, which can be adjusted to control the robustness to outliers. The weight vector is gradually updated through a fixed-point iterative algorithm to maximize the q-non-Gaussianity measure. In each iteration, in order to ensure the orthogonality between the independent components, the updated weight vector is orthogonalized through the Gram-Schmidt orthogonalization process.

[0035] (3) An effective motor unit identification submodule uses the Morlet wavelet function to decompose the electromyographic signal into wavelet coefficients of different scales based on the decomposed independent components, so as to extract significant features for the detection of discharge events.

[0036] Specifically, significant local maxima are detected in the wavelet coefficients as potential discharge events. The local maximum can be determined by comparing the values ​​in the neighborhood of the wavelet coefficients. The local extreme values ​​in the wavelet coefficients are binarized, and the part exceeding the threshold is marked as 1 (discharge moment), and the rest is marked as 0 (background noise). The binarization results of all discharge events are integrated to generate a 01 value time series marking the discharge moment of the motor unit.

[0037] (4) Screening and post-processing submodule, i.e., screening and post-processing the obtained discharge time sequence, including extracting the main frequency components between 4 Hz and 35 Hz by Fourier transform, retaining the motor unit sources that meet this frequency range; identifying the motor unit source pairs with synchronous discharge by calculating the cross-correlation function and constructing the synchronization matrix; for the identified synchronization sources, comprehensively scoring them through signal energy and stability evaluation, and finally retaining the synchronization source with the highest score, thereby ensuring the quality and stability of the selected motor unit source.

[0038] Specifically, Fourier transform is applied to the discharge time sequence of each motor unit to obtain the spectrum and calculate the power spectral density of the signal. The main frequency components in the frequency range of 4Hz to 35Hz are selected, and the motor unit sources in this range are retained. For each pair of motor unit sources A and B, the cross-correlation function between their discharge sequences is calculated to evaluate the degree of synchronization of the two signal sequences. The synchronization matrix is ​​constructed with the synchronization calculation results of all motor unit sources. The synchronization matrix is ​​statistically tested to identify the synchronous discharge phenomenon between different motor unit sources. The motor unit source pairs with significant synchronization are screened out, and these sources are marked as sources with significant synchronous discharge phenomenon. For each synchronization source, the quality of each synchronization source is evaluated by calculating the signal energy and signal stability (standard deviation). The comprehensive score of each synchronization source is calculated, and the synchronization source with the highest score is selected according to the comprehensive score. The synchronization source with the highest score is selected as the motor unit source that is finally retained, so that the most representative source can be effectively selected from multiple synchronization sources. This method ensures that the retained sources have the best quality and stability, providing a reliable data basis for subsequent analysis.

[0039] (IV) The microscopic neuron characteristic analysis module includes analyzing the discharge characteristics and laws of neurons at the microscopic level by calculating the instantaneous discharge rate of motor units and counting the discharge patterns of motor units. By analyzing the differential changes in the microscopic motion characteristics of electromyographic signals before and after DBS, the improvement effect of tremor symptoms in patients with movement disorders before and after deep brain stimulation treatment can be more accurately evaluated. Specifically including:

[0040] (1) Motor unit instantaneous discharge rate analysis submodule. The calculation formula of the instantaneous discharge rate between motor unit discharge pulses is:

[0041]

[0042] Among them, Δn is the number of sampling points between two consecutive discharge moments, and fs is the sampling rate. Based on this, the motor unit decomposition results and instantaneous discharge rates of different muscle groups in patients with movement disorders under three conditions, preoperatively, DBS-on, and DBS-off, were analyzed, and the changes in neuronal activity in patients with movement disorders under different conditions were evaluated. These changes directly reflect the regulatory effect of DBS on neuronal activity. Through microscopic analysis, the discharge pattern of motor units can be understood and adjusted, so as to achieve the goal of improving symptoms by regulating DBS.

[0043] (2) Motor unit discharge pattern analysis submodule. By counting the instantaneous discharge rates of all motor units and plotting them into a histogram, the intervention effect of DBS on the discharge pattern is demonstrated. The statistics and drawing of histograms can help researchers and clinicians observe the changes in neural activity after DBS interference and adjust pathological neural activity to a pattern that is more consistent with normal physiological rhythms. By optimizing these discharge patterns, tremor symptoms can be effectively improved.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] (1) Comprehensiveness and accuracy of high-density surface electromyography acquisition

[0046] Compared with existing movement disorder assessment techniques such as invasive lumbar puncture to obtain cerebrospinal fluid samples, it is non-invasive and more comfortable and safe for patients. Compared with the simple application of tremor assessment scales for manual empirical naked eye observation scoring, the present invention significantly improves the comprehensiveness, accuracy and objectivity of data collection by using a 256-channel HD-sEMG acquisition device. The forearm flexor and extensor muscle groups closely related to hand movements are selected as target muscle groups, and high-density surface electromyography data are collected by inducing tremor symptoms, ensuring the breadth and detail of data coverage. This enables the present invention to obtain richer and more reliable electromyography data, which has obvious advantages over traditional monopolar or bipolar electrode technology.

[0047] (2) Accuracy of microscopic neuron characteristics analysis

[0048] By calculating the instantaneous discharge rate and discharge pattern of motor units, the discharge characteristics and laws of neurons are deeply analyzed, and the condition and treatment effect of pathological tremor in patients with movement disorders are evaluated from a microscopic level. This precise microscopic analysis method enables the present invention to not only accurately capture the dynamic changes of neuronal activity, but also systematically count and analyze the discharge pattern of motor units. The results show that before and after DBS treatment, neuronal activity has changed significantly at the microscopic level. DBS can reduce the rhythmic discharge of motor units, reduce the synchronization of discharges between motor units, and inhibit the activity of pathological motor units. This precise microscopic analysis not only improves the accuracy of diagnosis and treatment, but also provides a scientific basis for the formulation of personalized treatment plans, helping doctors to formulate and adjust treatment plans more effectively, thereby improving the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Illustration of the HD-sEMG data acquisition platform.

[0050] Figure 2 Figure 1 is a diagram of the data acquisition software.

[0051] Figure 3 is the motor unit discharge pulse and its instantaneous discharge rate.

[0052] Figure 4 Distribution of motor unit discharge patterns. DETAILED DESCRIPTION

[0053] The present invention is further described below with reference to the accompanying drawings. A system for evaluating the therapeutic effect of deep brain electrical stimulation on hand movement disorder symptoms in patients with movement disorders based on HD-sEMG specifically comprises:

[0054] 1. HD-sEMG data acquisition system

[0055] HD-sEMG signals were acquired using a portable electromyography collector Sessantaquattro system (OT Bioelectronica, Torino, Italy). The device can transmit signals wirelessly without the need to connect to fixed equipment, allowing the experimenter to move the experimental equipment during the experiment to match the subject's movements, which provides convenience for the experiment. The sampling rate of the electromyography signal is 2000 Hz. The electromyography acquisition device can collect electromyography data of up to 64 channels. Four electromyography collectors were used for data acquisition at the same time in the experiment, and each collector was powered by a built-in battery. To reduce the impedance between the skin and the electrode, the forearm skin of the hand on the acquisition side was cleaned with scrub and cleaned with alcohol before data acquisition. Four 5×13 HD electrode arrays (Adhesive Matrix ELSCH064NM2 Pin Out, OT Bioelectronica, Torino, Italy) were used to collect high-density electromyography signals from the unilateral forearm. Each electrode array consists of 64 gel circular electrodes, one of which has an electrode channel missing in one corner. The center distance between adjacent electrode channels was 8 mm, and the electrode diameter was 3 mm. The electrode array orientation was roughly aligned with the tested muscle fibers. Two electrode arrays were placed on the flexor muscle groups and two electrode arrays were placed on the extensor muscle groups. A soaked wristband was wrapped around the forearm as a reference electrode.

[0056] Data collection platform such as Figure 1 As shown in the figure. The directed line segments in the figure show the transmission flow of data signals. The platform uses a laptop computer for the overall control of the host computer. A portable router is used to establish a network hotspot and connect the host computer and 4 electromyographic collectors through the TCP protocol. In the local area network, the laptop computer and the electromyographic collector can exchange signals with each other to realize the functions of parameter reading and configuration, the start and end of acquisition, online reading and offline storage of data. Since there is a time delay between each device, and the analysis of electromyographic signals has a high requirement for the time delay (on the order of ten milliseconds), this platform uses a hardware synchronization module to synchronize each signal. A mechanical button is used as the input end of the synchronization circuit, and the output end of the hardware circuit is connected to the input ports of the synchronization auxiliary signal of 4 HD-sEMGs. Pressing the button twice in a row starts the acquisition, and pressing the button three times in a row ends the acquisition. Through the above steps, the HD-sEMG data acquisition system can comprehensively and accurately record the high-density electromyographic signals of the forearm flexion and extension muscle groups of patients with movement disorders, providing a high-quality data basis for the subsequent analysis of micro-neuron characteristics.

[0057] Data acquisition is controlled by software. The software control block diagram is as follows: Figure 2As shown in the figure, it includes sensor module connection (displaying and managing the connection status of HD-sEMG sensor modules), start and stop acquisition (providing control buttons for starting and ending acquisition), acquisition timing (displaying the duration of data reading and storage), single module sEMG data display (displaying real-time data of a specific HD-sEMG module), data saving (managing the file saving settings of acquired data) and other functions. The connection status of the four HD-sEMG sensor modules is displayed below the timing display. When the host computer initiates the acquisition command, the four indicator lights use green and red to display the connection success and failure of the corresponding module respectively. The indicator lights can be used to check the status of the corresponding module when the connection fails to ensure the smooth progress of data acquisition. The two input boxes below are used from left to right to fill in the file name prefix for saving the data file and the number of the sEMG module that displays the data. The "Start" and "End" buttons at the bottom are used to initiate the acquisition start or end instructions. Due to the large amount of electromyographic data, the electromyographic signal will be sent to the host computer only when the signal is displayed. During synchronous acquisition, the electromyographic signal will be stored in the built-in SD card of each collector to ensure the integrity and stability of the data. Through the above functions, the data acquisition software can effectively manage and control the acquisition process of HD-sEMG data, monitor the connection status of the sensor module in real time, and provide precise time reference and data storage functions to ensure the accuracy and integrity of the collected data.

[0058] 2. Experimental Paradigm

[0059] Eight patients with movement disorders were recruited as subjects in this experiment (4 males and 4 females; age range 50-66 years, average age 61.6±5.3 years), and data were collected in a special EMI / EMC shielded room of the Functional Neurosurgery Department of a hospital in Shanghai. The experiment included three conditions: preoperative, postoperative DBS-on, and postoperative DBS-off. The subjects sat in a comfortable chair with armrests, placed the arm on the side with the most severe tremor symptoms on the armrest, and kept the body relaxed and stable. Before data collection, the experimenter guided the subjects to perform a countdown task with an interval of 2 to distract the subjects' attention and increase their mental burden, thereby inducing or strengthening tremor symptoms. When the patient's tremor symptoms became visible to the naked eye, or the countdown task lasted for one minute, data collection officially began. In each experimental condition, the subjects continued the countdown task for at least 45 seconds while collecting HD-sEMG signals. Multiple experimental data were collected under each experimental condition. The number of trials for each subject varied due to factors such as discomfort caused by drug withdrawal, electrode contact problems, and interference from the surrounding environment. The average number of trials under the three experimental conditions was 10.6±4.3, 7.7±3.4, and 7.3±3.2, respectively. Through this method, HD-sEMG data of PD patients can be comprehensively collected under different experimental conditions, so as to analyze the effect of DBS on the hand tremor symptoms of patients and provide a reliable data basis for subsequent data processing and analysis.

[0060] 3. Data preprocessing module

[0061] (1) Preliminary filtering: Apply low-pass filter and band-pass filter to perform preliminary filtering (10 Hz to 500 Hz) on the original EMG signal to remove power frequency interference and retain the effective signal frequency range. The low-pass filter is used to remove high-frequency noise above the signal frequency range, and the cutoff frequency is set to 500 Hz. The band-pass filter is used to retain the main components of the EMG signal, with a frequency range of 10 Hz to 500 Hz, and remove irrelevant low-frequency and high-frequency noise.

[0062] (2) LMS filter: After preliminary filtering, the least mean square (LMS) adaptive filter is used to adjust the filter coefficients in real time to minimize the error between the output signal and the desired signal, thereby eliminating dynamic noise and improving signal quality. The LMS filter is based on the finite impulse response (FIR) filter structure, and its output y(n) is expressed as:

[0063]

[0064] Where y(n) is the output of the filter, M is the filter order, ω i (n) is the filter coefficient and x(n) is the input signal. The error signal is determined by the difference between the desired signal and the filter output:

[0065] e(n)=d(n)-y(n)

[0066] Where: e(n) is the error signal, d(n) is the error signal. The core of the LMS algorithm is to gradually adjust the filter coefficient ω based on the gradient descent method. i (n) to minimize the mean square value of the error signal:

[0067] J(n)=E[e 2 (n)] = E[(d(n)-y(n)) 2 ]

[0068] Where E[·] represents the expected value. By using the objective function J(n) to calculate the filter coefficient ω i (n) Take the partial derivative and get the gradient:

[0069]

[0070] And update the filter coefficients using gradient descent:

[0071] ω i (n+1)=ω i (n)+2μe(n)·x(ni)

[0072] Among them, μ is the step factor (learning rate), which determines the amplitude of the coefficient update. e(n)·x(ni) is the product of the error signal and the input signal, which is used to adjust the coefficient. If the step factor μ is too large, the algorithm may diverge, and if it is too small, the convergence speed will be too slow. Therefore, the selection of the step factor needs to speed up the convergence speed as much as possible while ensuring stability. To ensure the stability of the algorithm, the step factor must meet the following conditions:

[0073]

[0074] λ max is the largest eigenvalue of the autocorrelation matrix of the input signal.

[0075] (3) Baseline drift correction: Baseline drift is a low-frequency component caused by changes in the impedance of the electrode-skin interface or other chronic interference. Through polynomial fitting technology, the nonlinear trend of the signal can be corrected to remove the baseline drift. The baseline drift of the signal can be expressed by a low-order polynomial:

[0076] b(t)=a 0 +a 1 t+a 2 t 2 +…+a n t n

[0077] Where b(t) represents the drift signal, a 0 ,a 1 ,…,a n is the fitting coefficient. Use the least squares method to fit the given signal x(t) and get the polynomial coefficient a i . This drift signal is then subtracted from the original signal:

[0078] x corrected =x(t)-b(t)

[0079] (4) Signal normalization: The signal amplitude is normalized to the range of [0, 1] to ensure the scale consistency of the signal and reduce the impact of the impedance difference of the electrode-skin interface.

[0080] The above-mentioned preprocessing module can effectively cope with complex signal environments, improve the clarity and accuracy of signals while removing noise and drift, and provide reliable input for subsequent signal analysis and processing.

[0081] 4: Motion unit decomposition module

[0082] The patient's flexor and extensor muscle groups are decomposed into motor units. Specifically, it includes:

[0083] (1) De-meaning, radial basis function transformation and principal component analysis submodules. The main purpose of de-meaning is to eliminate the baseline drift in the signal so that the signal data is concentrated at zero. The mean is calculated for the signal of each channel and subtracted from each data point. The original sEMG signal X is an N×T matrix, where N is the number of signal channels and T is the number of time points. Each row represents the time series signal of a channel:

[0084]

[0085] For each channel (each row of data), calculate its mean:

[0086]

[0087] in, is the mean of the jth channel, x jt is the signal value of the jth channel at time point t. Subtract the mean of each channel from each data point of that channel:

[0088]

[0089] Among them, x′ jtis the signal value after de-averaging. The de-averaged signals of all channels are combined into a new matrix X′. sEMG is a linear combination of the motor neuron input pulse signal and its delay superimposed with noise. In order to better reversely solve the discharge pulse signal of the motor neuron, it is necessary to apply the radial basis function (RBF) transformation to the de-averaged sEMG signal and map it to a high-dimensional feature space, so as to capture the complex nonlinear characteristics of the signal and make the nonlinear relationship between independent components more significant. Using Gaussian RBF, its form is:

[0090]

[0091] μ is the center of the RBF, and σ is the width (standard deviation) of the Gaussian function, which controls the width and shape of the function. Perform RBF transformation on the de-averaged signal to obtain the RBF feature matrix:

[0092]

[0093] Where, M is the number of RBFs, φ m (x t ) is the value of the mth RBF at time point t. RBF transformation can capture the nonlinear characteristics of the original signal and map the signal to a higher-dimensional space, so that subsequent processing (such as principal component analysis or motion unit decomposition) can more effectively identify and separate different motion units.

[0094] The RBF transformed data is standardized so that each feature has zero mean and unit variance to ensure that different features have the same scale:

[0095] X″=X′ RBF -mean(X′ RBF )

[0096] Among them, X″ is the standardized data matrix, mean(X′ RBF ) is the mean of each column feature. Calculate the covariance matrix C of the standardized data matrix:

[0097]

[0098] Where N is the number of samples. Add a regularization term to the covariance matrix C to improve its stability and reduce the impact of noise:

[0099] C reg =C+λI

[0100] Where λ is the regularization parameter and I is the identity matrix. The regularization term λI helps to avoid problems that occur during eigenvalue decomposition, such as eigenvalues ​​that are too small or matrices that are close to being singular.reg Perform eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors:

[0101] C reg =VΛ

[0102] Among them, V is the eigenvector matrix, Λ is a diagonal matrix, and the elements on the diagonal are eigenvalues. The first k eigenvectors with the largest eigenvalues ​​are selected as principal components, and these eigenvectors constitute a new feature space:

[0103] W=[v 1 v 2 …v k ]

[0104] Among them, v i is the eigenvector corresponding to the first k eigenvalues. Project the standardized data using the selected principal components to complete the dimensionality reduction:

[0105] X PCA =X″W

[0106] Among them, X PCA is the data matrix after dimensionality reduction, and W is the eigenvector matrix containing the first k principal components.

[0107] (2) Use the FastICA algorithm submodule combined with the q-non-Gaussianity metric to reversely solve the reduced dimensionality electromyographic signal to separate each independent motor unit. The traditional FastICA algorithm usually uses negative entropy as a measure of non-Gaussianity. Negative entropy uses the difference in entropy to measure the non-Gaussianity of a signal. However, negative entropy is sensitive to outliers and noise, which may lead to a decrease in the accuracy of independent component extraction in the presence of noise. The q-non-Gaussianity metric provides a more generalized non-Gaussianity metric by introducing Tsallis entropy. Compared with traditional non-Gaussianity metrics (such as metrics based on negative entropy), the q-non-Gaussianity metric introduces an adjustable parameter q, which can be adjusted to control the robustness to outliers. The q-non-Gaussianity metric formula for the signal is:

[0108]

[0109] w is the weight vector of the independent component to be estimated, x is the observation data matrix, and q is the adjustment parameter. The weight vector w is gradually updated through the fixed point iteration algorithm to maximize the q-non-Gaussianity measure. The iteration formula is:

[0110] w new =Ε[xg(w T x)]-Ε[g′(w T x)]w

[0111] Where g(·) is a nonlinear function and w is the weight vector of the current iteration. In each iteration, in order to ensure the orthogonality between the independent components, the updated weight vector is orthogonalized by the Gram-Schmidt orthogonalization process.

[0112] (3) The effective motor unit identification submodule decomposes the electromyographic signal into wavelet coefficients of different scales based on the Morlet wavelet function to extract significant features for the detection of discharge events:

[0113]

[0114] Among them, W(a,b) is the wavelet coefficient at scale a and position b, is the wavelet basis function. Detect significant local maxima in the wavelet coefficients as potential discharge events. The local maximum can be determined by comparing the values ​​in the neighborhood of the wavelet coefficients. Binarize the local extreme values ​​in the wavelet coefficients, and mark the part exceeding the threshold as 1 (discharge moment) and the rest as 0 (background noise). Integrate the binarization results of all discharge events to generate a 01 value time series marking the discharge moment of the motor unit.

[0115] (4) Screening and post-processing submodule, which screens and post-processes the obtained discharge time sequence. Apply Fourier transform to the discharge time sequence of each motor unit, obtain the spectrum and calculate the power spectral density of the signal. Select the main frequency components between 4Hz and 35Hz, and retain the motor unit sources within this range. This frequency range is concentrated in the main frequency band of the electromyographic signal, which can effectively extract the most representative discharge frequency while reducing the interference of high-frequency noise. For each pair of motor unit sources A and B, calculate the cross-correlation function between their discharge sequences to evaluate the degree of synchronization of the two signal sequences. Construct a synchronization matrix with the synchronization calculation results of all motor unit sources. Perform a statistical test on the synchronization matrix to identify the synchronous discharge phenomenon between different motor unit sources. Screen out pairs of motor unit sources with significant synchronization, and mark these sources as sources with significant synchronous discharge phenomenon. For each synchronization source, the quality of each synchronization source is evaluated by calculating the signal energy and signal stability (standard deviation). The signal energy formula is:

[0116]

[0117] Among them, A i (t) is the signal value of the ith synchronization source at time t, and T is the total number of time points. The signal stability formula is:

[0118]

[0119] Among them A iis the mean value of the ith synchronization source signal. Calculate the comprehensive score of each synchronization source, and select the synchronization source with the highest score based on the comprehensive score. The comprehensive score S of each synchronization source is:

[0120]

[0121] The synchronization source with the highest score is selected as the final retained motor unit source, which can effectively select the most representative source from multiple synchronization sources. This method ensures that the retained source has the best quality and stability, providing a reliable data basis for subsequent analysis.

[0122] 5. Micro-neuron Analysis Module

[0123] (1) Motor unit instantaneous discharge rate analysis submodule. The calculation formula of the instantaneous discharge rate between motor unit discharge pulses is:

[0124]

[0125] Where Δn is the number of sampling points between two consecutive discharge moments, and fs is the sampling rate. Figure 3 The results of motor unit decomposition and transient discharge rates of different muscle groups in three experimental conditions of representative subjects before surgery, DBS-on and DBS-off are shown in the figure. The results of motor unit discharge pulse sequence showed that the motor units were highly rhythmic, accompanied by double or multiple continuous spike discharges under preoperative and DBS-off conditions. The transient discharge rate before surgery was concentrated around a stable low frequency of about 5Hz, corresponding to the typical PD tremor frequency. Some motor units showed higher transient discharge rates, which corresponded to double or triple peaks in the tremor action potential. Under DBS-on conditions, the transient discharge rates of motor units were distributed around 10Hz, corresponding to the typical physiological tremor frequency. Under DBS-off conditions, the transient discharge rates of motor units fluctuated in the range of 3-12Hz. Compared with preoperative conditions, more motor units spread to a higher discharge rate range, and more motor units showed more active short-term continuous discharges.

[0126] (2) Motor unit discharge pattern analysis submodule: The analysis of the motor unit discharge pattern is described by counting the instantaneous discharge rates of all motor units and drawing them into a histogram. Figure 4The distribution of motor unit discharge patterns of 8 patients is shown. In the preoperative state, the discharge pattern was clustered in the pathological tremor band (about 5Hz) with a relatively high and sharp peak, indicating that a large number of motor units were synchronously discharged in this frequency range. This high peak discharge pattern corresponds to the typical tremor symptoms of dyskinesia and reflects pathological neural activity. In the DBS-on state, the discharge pattern showed two higher-frequency diffuse peaks (about 10Hz and about 15Hz, respectively), and the peak profile was flatter and wider, indicating that the discharge of motor units was more dispersed and unfocused at these frequencies. These frequencies correspond to the normal physiological tremor band, indicating that DBS effectively adjusts the pathological motor unit discharge pattern to a closer normal physiological state. In the DBS-off state, some discharged motor units re-clustered in the symptomatic tremor band (about 5Hz) with a sharp peak, but the peak value was lower than the preoperative state. Other motor units were clustered in the normal physiological tremor band (about 12Hz), indicating that the discharge pattern of some motor units remained in the higher frequency range. This shows that after DBS was turned off, some motor units resumed their pathological discharge rhythm, but the overall situation was still better than before the operation. Specifically, DBS improves tremor symptoms by interfering with pathological neural activity and adjusting the discharge pattern of motor units to a pattern that is more in line with normal physiological rhythms.

[0127] References:

[0128] [1]. Rissanen SM, Ruonala V, Pekkonen E, et al. Signal features of surface electromyography in advanced Parkinson's disease during different settings of deep brain stimulation [J]. Clinical Neurophysiology, 2015, 126(12): 2290-2298.

[0129] [2]. Rissanen SM, M,Tarvainen MP,et al.Analysis of EMG andacceleration signals for quantifying the effects of deep brain stimulation inParkinson'sdisease[J].IEEE transactions on biomedical engineering,2011,58(9):2545-2553.

Claims

1. A movement disorder treatment effect evaluation system based on HD-sEMG and neuron analysis, characterized in that: By inducing pathological tremor symptoms in the hands of patients with movement disorders, collecting forearm HD-sEMG data, and analyzing its micro-neuron characteristics, the improvement effect of pathological tremor symptoms in patients with movement disorders before and after DBS treatment can be accurately evaluated; specifically, it includes a pathological tremor data acquisition platform for patients with movement disorders, a data preprocessing module, a motor unit decomposition and identification module, and a micro-neuron characteristic analysis module; among which: (i) The data acquisition platform is a HD-sEMG acquisition device used to record 256-channel surface electromyography data of the flexor and extensor muscle groups of the forearm of patients with movement disorders; (ii) the data preprocessing module preprocesses the original electromyographic signal through preliminary filtering, LMS filter, baseline drift correction and signal normalization; the preliminary filtering is used to remove power frequency interference and retain the effective frequency range, the LMS filter adaptively adjusts the coefficient to eliminate dynamic noise, and the baseline drift correction removes chronic drift through polynomial fitting; the normalization ensures the consistency of signal amplitude, thereby improving the clarity and accuracy of the signal; (III) The motor unit decomposition and identification module is used to perform motor unit decomposition on the patient's flexor muscle group and extensor muscle group respectively; specifically includes: (1) De-meaning, radial basis function transformation and principal component analysis submodules: De-meaning eliminates the baseline offset of the signal to ensure the accuracy of the analysis; radial basis function transformation is used to map the signal to a nonlinear high-dimensional space to better capture complex signal characteristics; principal component analysis is used to reduce the dimensionality of high-dimensional data, extract the main features, reduce the impact of noise and improve the efficiency and accuracy of subsequent analysis; (2) The motor unit extraction submodule uses the FastICA algorithm combined with the q-non-Gaussianity metric to decompose the reduced-dimensional electromyographic signal. The q-non-Gaussianity metric provides better robustness by introducing Tsallis entropy and adjusting parameters, and can effectively handle noise and outliers. The weight vector is optimized through fixed-point iteration and updated to maximize the q-non-Gaussianity metric. In each iteration, Gram-Schmidt orthogonalization is performed to ensure the orthogonality between independent components and maintain the validity of the decomposition results. (3) An effective motor unit identification submodule uses Morlet wavelet to decompose the electromyographic signal according to the decomposed independent components, and generates a 01 value time series of the motor unit discharge moment by detecting the significant local extreme values ​​in the wavelet coefficients and binarizing them; (4) Screening and post-processing submodule, i.e., screening and post-processing the obtained discharge time sequence; including extracting the main frequency components in the frequency range of 4 Hz to 35 Hz by Fourier transform, retaining the motor unit sources that meet this frequency range; identifying the motor unit source pairs with synchronous discharge by calculating the cross-correlation function and constructing the synchronization matrix; for the identified synchronization sources, comprehensively scoring them through signal energy and stability evaluation, and finally retaining the synchronization source with the highest score, thereby ensuring the quality and stability of the selected motor unit source; (iv) The micro-neuron characteristic analysis module includes analyzing the discharge characteristics and laws of neurons at the micro level by calculating the instantaneous discharge rate of motor units and counting the discharge patterns of motor units; and accurately evaluating the improvement effect of tremor symptoms in patients with movement disorders before and after deep brain stimulation treatment by analyzing the changes in micro-neuron characteristics of electromyographic signals before and after deep brain stimulation treatment.

2. The evaluation system according to claim 1, characterized in that In the data acquisition platform, a 4-electrode array is used to collect high-density electromyographic signals of a unilateral forearm; each electrode array is composed of 64 gel circular electrodes; the direction of the electrode array is aligned with the patient's muscle fibers; among them, 2 electrode arrays are placed on the flexor muscle group, respectively located above the wrist flexor and the superficial flexor of the digitorum; 2 electrode arrays are placed on the extensor muscle group, respectively located above the radial extensor carpi and the extensor digitorum; the collected electromyographic signals will be stored in the built-in SD card of each acquisition device to ensure the integrity and stability of the data.

3. The evaluation system according to claim 1, characterized in that In the data preprocessing module: (1) Preliminary filtering: Apply low-pass filter and band-pass filter to perform preliminary filtering on the original electromyographic signal to remove power frequency interference and retain the effective signal frequency range; low-pass filter is used to remove high-frequency noise higher than the signal frequency range; band-pass filter is used to retain the main components of the electromyographic signal and remove irrelevant low-frequency and high-frequency noise; (2) LMS filter: After preliminary filtering, the least mean square (LMS) adaptive filter is used to adjust the filter coefficients in real time to minimize the error between the output signal and the desired signal, thereby eliminating dynamic noise and improving signal quality. The error signal is determined by the difference between the desired signal and the filter output. LMS is based on the gradient descent method to gradually adjust the filter coefficients ω. i (n) to minimize the mean square value of the error signal; by subjecting the objective function J(n) to the filter coefficient ω i (n) Calculate the partial derivative, obtain the gradient, and use the gradient descent method to update the filter coefficients; (3) Baseline drift correction: Baseline drift is a low-frequency component caused by changes in the impedance of the electrode-skin interface or other chronic interference. The nonlinear trend of the signal is corrected and the baseline drift is removed by polynomial fitting technology. The given signal is fitted using the least squares method to obtain the polynomial coefficients, and then the drift signal is subtracted from the original signal. (4) Signal normalization: The signal amplitude is normalized to the range of [0, 1] to ensure the scale consistency of the signal and reduce the impact of the impedance difference of the electrode-skin interface.

4. The evaluation system according to claim 3, characterized in that: In the motor unit decomposition module, the specific process of the screening and post-processing submodule is as follows: applying Fourier transform to the discharge time sequence of each motor unit, obtaining the spectrum and calculating the power spectrum density of the signal; selecting the main frequency components between 4 Hz and 35 Hz, and retaining the motor unit sources within this range; for each pair of motor unit sources A and B, calculating the cross-correlation function between their discharge sequences, which is used to evaluate the degree of synchronization of the two signal sequences; The synchronization matrix is ​​constructed by calculating the synchronization degree of all motion unit sources; Perform statistical tests on the synchronization matrix to identify the synchronous discharge phenomenon between different motor unit sources; screen out pairs of motor unit sources with significant synchronization, and mark these sources as sources with significant synchronous discharge phenomenon; for each synchronization source, evaluate the quality of each synchronization source by calculating signal energy and signal stability; calculate the comprehensive score of each synchronization source, and select the synchronization source with the highest score based on the comprehensive score; select the synchronization source with the highest score as the motor unit source that is finally retained to provide a reliable data basis for subsequent analysis.

5. The evaluation system according to claim 4, characterized in that: The micro-neuron characteristic analysis module specifically includes: (1) Motor unit instantaneous discharge rate analysis submodule; the calculation formula of the instantaneous discharge rate between motor unit discharge pulses is: Where Δn is the number of sampling points between two consecutive discharge moments, and fs is the sampling rate. Based on this, the motor unit decomposition results and instantaneous discharge rates of different muscle groups in patients with movement disorders under three conditions, namely, before surgery, DBS-on, and DBS-off, were analyzed, and the changes in neuronal activity in patients with movement disorders under different conditions were evaluated. The discharge pattern of motor units was understood and adjusted through microscopic analysis, so as to achieve the goal of improving symptoms by regulating DBS. (2) Motor unit discharge pattern analysis submodule: By counting the instantaneous discharge rates of all motor units and plotting them into a histogram, the intervention effect of DBS on the discharge pattern is demonstrated; the statistics and drawing of the histogram can help researchers and clinicians observe the changes in neural activity after DBS interference and adjust pathological neural activity to a pattern that is more consistent with normal physiological rhythms; by optimizing these discharge patterns, tremor symptoms can be effectively improved.

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