Parkinson's disease sample feature typing method and system based on local field potential and sample classification model training method

Abstract

The invention discloses a Parkinson's disease sample feature typing method, a Parkinson's disease sample feature typing system and a sample classification model training method based on local field potential. The typing method comprises the following steps: performing power spectral density calculation on a local field potential Beta frequency band signal of a sample to obtain an average power spectral density curve, and performing peak analysis on the average power spectral density curve to obtain a peak power spectral density; modeling is carried out on the periodic component and the aperiodic component of the average power spectrum, and a periodic power spectrum and an aperiodic component are extracted; calculating phase-amplitude coupling characteristics of the local field potential Beta frequency band signal of the sample; and performing feature clustering analysis by using the peak power spectrum density, the periodic power spectrum, the non-periodic component and the phase amplitude coupling feature, and outputting a sample typing result. According to the method, the multi-dimensional data model is constructed by extracting and integrating the peak power spectral density, the periodic oscillation feature, the non-periodic background feature, the phase-amplitude coupling and other high-level nerve physiological features, and the classification accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention belongs to the field of neuroscience, and more specifically, relates to a Parkinson's disease sample feature typing method based on local field potential, a feature typing system and a sample classification model training method. Background Art

[0002] Parkinson's disease (PD) is a common neurodegenerative disease that mainly affects middle-aged and elderly people. Clinically, Parkinson's disease is characterized by its typical motor symptoms - tremor, muscle rigidity, bradykinesia and postural instability, accompanied by non-motor symptoms such as cognitive dysfunction, depression, and sleep disorders. Despite similar clinical manifestations, Parkinson's disease shows significant heterogeneity among different individuals, which makes early diagnosis, disease classification and personalized treatment a difficult problem in medical research. The aging of the global population has caused the prevalence of Parkinson's disease to increase year by year, bringing a heavy burden to society and families. Therefore, exploring the different subtypes of Parkinson's disease and achieving more effective intervention and treatment through precise classification has become an important research direction in the current field of neuroscience.

[0003] At present, the diagnosis of Parkinson's disease mainly relies on clinical assessment tools. However, the clinical manifestations of Parkinson's disease are highly individual, and patients may present completely different combinations of symptoms at different stages of the disease, which makes it very difficult to classify and predict based solely on clinical symptoms. Studies have shown that Beta-band local field potentials (LFP) are closely related to motor symptoms, especially during motor control and motor planning. Changes in Beta wave activity can reflect the patient's motor function status. Although EEG signals have made some progress in Parkinson's disease research, most existing studies focus on the correlation analysis between EEG features and a single clinical indicator, and lack comprehensive modeling of multiple clinical features and neural activity data.

[0004] The invention patent with the prior art publication number CN116458898A proposes a method and system for extracting features of Parkinson's disease depression, including: acquiring EEG signals, processing the EEG signals to obtain local field potentials, preprocessing the local field potentials to obtain local field potentials to be processed; performing continuous wavelet transform on the local field potentials to be processed, retaining alpha, lowbeta, highbeta and beta frequency bands; extracting burst features of the above-mentioned frequency bands, extracting burst features of alpha, lowbeta, highbeta and beta frequency bands after processing the local field potentials using continuous wavelet transform, and using the Parkinson's disease depression feature extraction system to achieve high-precision classification of Parkinson's depression degree based on the extracted burst features. This solution only uses wavelet transform to extract burst features, and the feature extraction is not comprehensive enough. Summary of the invention

[0005] In order to overcome the deficiencies in the prior art, including the failure to comprehensively model multiple clinical characteristics and neural activity data, and the problem of incomplete feature extraction, the present invention provides a Parkinson's disease sample feature typing method based on local field potential, a feature typing system and a sample classification model training method.

[0006] The primary purpose of the present invention is to solve the above technical problems. The technical solutions of the present invention are as follows:

[0007] The first aspect of the present invention provides a method for characteristic typing of Parkinson's disease samples based on local field potential, comprising the following steps:

[0008] The power spectrum density of the Beta frequency band signal of the local field potential of the sample is calculated to obtain the average power spectrum density curve;

[0009] Perform peak analysis on the average power spectrum density curve to obtain the peak power spectrum density;

[0010] Model the periodic component and non-periodic component of the average power spectrum density curve separately, and extract the periodic power spectrum and non-periodic component;

[0011] Calculate the phase-amplitude coupling characteristics of the Beta frequency band signal of the local field potential of the sample;

[0012] The peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling characteristics are used to perform feature clustering analysis and output the sample classification results.

[0013] Furthermore, the method for calculating the power spectral density of the Beta frequency band signal of the local field potential of the sample is the Welch method, which specifically includes the following steps:

[0014] The original local field potential signal x[n] is framed and the windowed signal is calculated by combining the Hanning window function w[n]. The expression is as follows:

[0015] x i [n] = x[n+i·N]·w[n]

[0016] Where n represents the index of the sample within the frame, n∈[1,N-1], x i [n] represents the windowed signal of the i-th frame, x[n] represents the original signal, w[n] represents the Hanning window, and N is the length of the frame;

[0017] Apply discrete Fourier transform to each frame of windowed signal and calculate the frequency domain signal X i [f], the expression is as follows:

[0018] X i [f] = FFT(xi [n])

[0019] Where f stands for frequency, FFT stands for discrete Fourier transform, and x i [n] is the windowed signal of the i-th frame;

[0020] The frequency domain signal of each frame is squared to obtain the power spectrum density P of the i-th frame. i [f], the expression is as follows:

[0021] P i [f]=|X i [f]| 2

[0022] The power spectra of all frames are averaged to obtain the average power spectrum density curve P avg [f], the expression is as follows:

[0023]

[0024] Where N is the number of frames.

[0025] Furthermore, the periodic component and the non-periodic component of the average power spectrum density curve are modeled respectively to extract the periodic power spectrum and the non-periodic component, which specifically includes the following steps:

[0026] The periodic oscillation components of the power spectrum are fitted using Gaussian functions to obtain the periodic power spectrum density P periodic (f), the expression is as follows:

[0027]

[0028] Among them, f represents frequency, A represents the amplitude of oscillation, CF represents the center frequency of oscillation, and BW represents the bandwidth of oscillation;

[0029] The 1 / f-like function is used to fit the non-periodic component of the power spectrum to obtain the non-periodic power spectrum density P aperiodic (f), the expression is as follows:

[0030]

[0031] Among them, f represents frequency, offset represents the offset of the non-periodic component, and exponent represents the exponent of the non-periodic component;

[0032] The periodic power spectrum density and the non-periodic power spectrum density are combined to construct a complete power spectrum density model. The model parameters are optimized by the least square method to minimize the mean square error and obtain the spectrum curve of the fitted power spectrum density.

[0033] The non-periodic power spectrum density is subtracted from the fitted power spectrum to obtain the periodic power spectrum, and the non-periodic component is extracted from the non-periodic fitting curve.

[0034] Further, the phase-amplitude coupling characteristics of the local field potential Beta signal are calculated, specifically including the following steps:

[0035] The instantaneous phase of the low-frequency band of the Beta signal is obtained by Hilbert transform; the instantaneous amplitude of the high-frequency band of the Beta signal is obtained by Hilbert transform;

[0036] The probability distribution of high-frequency amplitude and the connection between low-frequency phase are established by using the preset method to obtain the phase-amplitude coupling characteristics.

[0037] Furthermore, the preset method for establishing the connection between the probability distribution of the high-frequency amplitude and the low-frequency phase specifically includes the following steps:

[0038] Divide the low-frequency phase into n equal-width intervals;

[0039] In each phase interval, the average value of the high-frequency signal amplitude is calculated, the distribution histogram of the amplitude in the phase is constructed and the distribution histogram is normalized. The expression is as follows:

[0040]

[0041] Among them, P norm,i is the normalized probability distribution of the ith phase interval, P i is the amplitude histogram frequency in the ith interval, ∑ j P j is the sum of the amplitude histograms in all phase intervals;

[0042] The Kullback-Leibler distance is used to calculate the degree to which the distribution deviates from the uniform distribution. KL , the expression is as follows:

[0043]

[0044] Among them, n represents the number of intervals, and U represents the uniform distribution probability;

[0045] The phase-amplitude coupling feature MI is calculated by KL distance normalization, and the expression is as follows:

[0046]

[0047] Furthermore, feature clustering analysis is performed using peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling features, specifically including the following steps:

[0048] Perform dimensionality reduction on the extracted peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling features to obtain the reduced-dimensional features;

[0049] Input the reduced-dimensional features into n clustering models respectively and output the sample typing results;

[0050] Calculate the quality indicators of sample typing results, evaluate clustering quality, and select the best clustering scheme.

[0051] A second aspect of the present invention provides a Parkinson's disease sample characteristic typing system based on local field potential, comprising a memory and a processor, wherein the memory comprises a Parkinson's disease sample characteristic typing method program based on local field potential, and when the Parkinson's disease sample characteristic typing method program based on local field potential is executed by the processor, the steps of a Parkinson's disease sample characteristic typing method based on local field potential are implemented.

[0052] A third aspect of the present invention provides a Parkinson's disease sample classification model training method, which uses a Parkinson's disease sample feature classification method based on local field potential to obtain a classification result for training, comprising the following steps:

[0053] Using the sample rating scale as input features, respectively training m classification models, classifying the samples according to the classification results, and searching for the optimal model configuration through hyperparameter optimization;

[0054] The leave-one-out cross-validation method was used to evaluate the model performance, output the best classification model and verify its interpretability.

[0055] Furthermore, the method for hyperparameter optimization search is Bayesian optimization.

[0056] Furthermore, the method for verifying the interpretability of the model includes the following steps:

[0057] Use the SHAP method to calculate the impact of each input feature on the model classification results and output the SHAP importance ranking diagram;

[0058] The SHAP importance ranking diagram was verified by combining statistical analysis, and the inter-group difference significance test was used to evaluate the impact of features on the classification results to ensure the interpretability of the model.

[0059] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0060] This paper proposes a comprehensive analysis method that integrates the Beta frequency band characteristics of EEG local field potentials with multidimensional clinical characteristics to achieve accurate classification of Parkinson's disease samples. By extracting and integrating advanced neurophysiological features such as peak power spectral density, periodic oscillation characteristics, non-periodic background characteristics, and phase-amplitude coupling (PAC), a multidimensional data model is constructed to improve the accuracy and reliability of classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to make the purpose and technical solution of the present invention clearer, the present invention provides the following drawings and descriptions:

[0062] Figure 1 A flow chart of a typing method provided by an embodiment of the present invention;

[0063] Figure 2 A schematic diagram of peak power spectrum extraction provided by an embodiment of the present invention;

[0064] Figure 3 A schematic diagram of extracting periodic power spectrum and non-periodic components provided by an embodiment of the present invention;

[0065] Figure 4 A comparison chart of indicators of different clustering methods provided by the embodiment of the present invention;

[0066] Figure 5 A comparison chart of clustering results provided by an embodiment of the present invention;

[0067] Figure 6 A comparison chart of machine learning prediction results provided by an embodiment of the present invention;

[0068] Figure 7 The SHAP model provided for the embodiment of the present invention can explain the analysis diagram. DETAILED DESCRIPTION

[0069] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0071] Embodiment 1:

[0072] The present invention provides a method for characterizing Parkinson's disease samples based on local field potentials, such as Figure 1The figure shows a flow chart of a method for characteristic typing of Parkinson's disease samples based on local field potentials. The specific steps are as follows:

[0073] S1: Calculate the power spectral density of the Beta frequency band signal of the local field potential (LFP) of the sample to obtain the average power spectral density curve.

[0074] More specifically, the method for calculating the power spectral density of the Beta frequency band signal of the local field potential (LFP) of the sample is the Welch method, and the specific process is as follows:

[0075] The original local field potential signal x[n] is framed, with each frame length of 1 second, and the windowed signal is calculated by combining the Hanning window function w[n]. The expression is as follows:

[0076] x i [n] = x[n+i·N]·w[n]

[0077] Where n represents the index of the sample within the frame, n∈[1,N-1], x i [n] represents the windowed signal of the i-th frame, x[n] represents the original signal, w[n] represents the Hanning window, and N is the length of the frame;

[0078] Apply discrete Fourier transform (FFT) to each frame of windowed signal to calculate the frequency domain signal X i [f], the expression is as follows:

[0079] X i [f] = FFT(x i [n])

[0080] Where f represents frequency, x i [n] is the windowed signal of the i-th frame;

[0081] The frequency domain signal of each frame is squared to obtain the power spectrum density P of the i-th frame. i [f], the expression is as follows:

[0082] P i [f]=|X i [f]| 2

[0083] The power spectra of all frames are averaged to obtain the average power spectrum density curve P avg [f], the expression is as follows:

[0084]

[0085] Where N is the number of frames.

[0086] S2: Perform peak analysis on the average power spectral density curve to obtain the peak power spectral density.

[0087] In this embodiment, the peak of the Beta frequency band is first searched, and then the frequency band of 5 Hz around the peak is calculated to calculate the peak power spectrum density. The reason for choosing this indicator is that it is a parameter that is tracked in the long term in a set of sensor systems and has potential applications in adaptive DBS. The schematic diagram is shown in FIG. Figure 2 shown.

[0088] S3: Model the periodic component and non-periodic component of the average power spectral density curve separately, and extract the periodic power spectrum and non-periodic components.

[0089] The specific process is:

[0090] The periodic oscillation components of the power spectrum are fitted using Gaussian functions to obtain the periodic power spectrum density P periodic (f), the expression is as follows:

[0091]

[0092] Among them, f represents frequency, A represents the amplitude of oscillation, CF represents the center frequency of oscillation, and BW represents the bandwidth of oscillation;

[0093] The 1 / f-like function is used to fit the non-periodic component of the power spectrum to obtain the non-periodic power spectrum density P aperiodic (f), the expression is as follows:

[0094]

[0095] Among them, f represents frequency, offset represents the offset of the non-periodic component, and exponent represents the exponent of the non-periodic component.

[0096] The periodic power spectral density and the non-periodic power spectral density are combined to construct a complete power spectral density model. The model parameters are optimized by the least square method to minimize the mean square error and obtain the spectral curve of the fitted power spectral density. The expression is as follows:

[0097]

[0098] Among them, P i is the actual power spectrum, P(f i ) is the preliminary fitting power spectrum of the model, and N is the number of frequency points;

[0099] Then the feature extraction of power spectrum parameterization is performed. After parameterizing the power spectrum, the spectrum curve of the fitted power spectrum density can be obtained, and the non-periodic fitting curve can also be drawn, such as Figure 3As shown, the periodic power spectrum (the orange shaded area in the figure) is obtained by subtracting the non-periodic power spectral density (the area under the non-periodic fitting curve) from the fitted power spectrum, and the non-periodic components (offset and index) are extracted from the non-periodic fitting curve.

[0100] S3: Calculate the phase-amplitude coupling characteristics of the Beta frequency band signal of the local field potential (LFP) of the sample;

[0101] The specific process is:

[0102] The instantaneous phase φ(t) obtained by Hilbert transform in the low frequency band of the Beta signal is expressed as follows:

[0103] φ(t)=arg[Hilbert(low-frequency Beta signal)]

[0104] The Hilbert transform is used to obtain the instantaneous amplitude of the high frequency band of the Beta signal (300-400Hz, 400-500Hz). h (t)|, the expression is as follows:

[0105] |A h (t)|=|Hilbert (high frequency HFO signal)|

[0106] The probability distribution of high-frequency amplitude is connected with the low-frequency phase by using a preset method to obtain the phase-amplitude coupling (PAC) feature, which specifically includes the following steps:

[0107] The low-frequency phase is divided into n equal-width intervals, 18 in this embodiment, corresponding to every 20° in [0,2π]. In each phase interval, the average value of the high-frequency signal amplitude is calculated, and a distribution histogram of the amplitude in the phase is constructed and the distribution histogram is normalized. The expression is as follows:

[0108]

[0109] Among them, P norm,i is the normalized probability distribution of the ith phase interval, P i is the amplitude histogram frequency in the ith interval, ∑ j P j is the sum of the amplitude histograms in all phase intervals;

[0110] The Kullback-Leibler (KL) distance is used to calculate the degree to which the distribution deviates from the uniform distribution. KL , the expression is as follows:

[0111]

[0112] Where n represents the number of intervals, and U represents the uniform distribution probability, which is usually 1 / n;

[0113] The phase-amplitude coupling feature MI is calculated by KL distance normalization, and the expression is as follows:

[0114]

[0115] S4: Use peak power spectrum density, periodic power spectrum, non-periodic components and phase-amplitude coupling features to perform feature clustering analysis and output sample classification results.

[0116] In this embodiment, the specific process is:

[0117] The peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling characteristics of the extracted 55 patient samples were subjected to principal component analysis (PCA) dimensionality reduction to obtain the reduced-dimensional features;

[0118] The features after dimension reduction are input into n clustering models respectively. In this embodiment, four clustering methods are considered, including hierarchical clustering (HAC), K-Means, partitioned K-Means (BiKMeans) and hierarchical DBSCAN (HDBSCAN). The number of clusters k is set to 2, and the sample typing results are output;

[0119] The silhouette coefficient and Davies-Bouldin index of the sample typing results were calculated to evaluate the clustering quality and select the best clustering scheme.

[0120] Silhouette measures the quality of a cluster by calculating how similar a point is to its own cluster, and then averaging the result over the entire dataset. A score close to -1 indicates a meaningless grouping, while a score close to +1 indicates a completely independent and compact grouping, as shown below:

[0121]

[0122] Where a(i) is the average distance between the ith data point and other points in the same cluster, b(i) is the minimum average distance between the ith data point and other points in different clusters, and N is the total number of points.

[0123] The Davies-Bouldin index (DB index) is also an indicator for measuring clustering performance. It reflects the balance between intra-class compactness and inter-class separation. The expression is as follows:

[0124]

[0125] Where k is the number of clusters, σ i , σj are the average intra-class distances of cluster i and cluster j, respectively, indicating the average distance from the sample to the centroid in the cluster; d ij is the Euclidean distance between the centroids of cluster i and cluster j. By calculating the maximum similarity index between each cluster and other clusters and taking the average value, the closeness and separation between clusters are evaluated. The smaller the DB index, the closer the samples within the class are, the greater the degree of separation between classes is, and the better the clustering effect is. The larger the DB index, the worse the clustering performance is.

[0126] By comparing the clustering effects of various methods, the best clustering method is selected. The clustering index results of the present invention are as follows: Figure 4 As shown in Figure 2, K-Means performs best overall, and the final clustering result is as follows: Figure 5 As shown, one cluster is named CL0 (Cluster0) and the other cluster is named CL1 (Cluster1).

[0127] Embodiment 2:

[0128] The present embodiment provides a Parkinson's disease sample characteristic typing system based on local field potential, comprising a memory and a processor, wherein the memory comprises a Parkinson's disease sample characteristic typing method program based on local field potential, and when the Parkinson's disease sample characteristic typing method program based on local field potential is executed by the processor, the steps of a Parkinson's disease sample characteristic typing method based on local field potential as described in Example 1 are implemented.

[0129] Embodiment 3:

[0130] This embodiment provides a Parkinson's disease sample classification model training method, which uses the classification results obtained by the Parkinson's disease sample feature classification method based on local field potential described in Example 1 for training, including the following steps:

[0131] The first, second, third and fourth parts of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), the Montreal Cognitive Assessment (MMSE) and the Hamilton Depression Rating Scale (HAMD-24) were used as input features to train different classification models, including Logistic Regression, AdaBoost, SVM, Naive Bayes, XGBoost, CatBoost, KNN and MLP. The samples were classified according to the new types CL1 and CL0 mentioned in the above clustering. In order to ensure that each model can achieve the best performance, the optimal hyperparameters of each classifier were searched through Bayesian optimization to make full use of the prediction ability of the model.

[0132] The leave-one-out cross-validation method is used to evaluate model performance, calculate accuracy, recall, and F1 scores, output the best performing classification model, and verify its interpretability. This validation method is particularly effective when dealing with small medical datasets because it ensures that the model is tested on completely unseen sample data, thereby providing a more realistic and reliable assessment of the model's generative ability. The evaluation results of multiple models are shown in Figure 2. Figure 6 shown.

[0133] The model with the best overall effect, Naive Bayes, was selected, with accuracy, precision, recall score and F1 score reaching 87%.

[0134] More specifically, the method for verifying the interpretability of the model includes the following steps:

[0135] The SHAP method is used to calculate the impact of each input feature on the model classification results, including the World Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Mini-Mental State Examination (MMSE), Hamilton Depression Rating Scale 24 (HAMD-24), Non-Motor Symptoms in Daily Life (MDS-UPDRSI), Motor Symptoms in Daily Life (MDS-UPDRSII), Motor Function Examination (MDS-UPDRSIII), Motor Complications (MDS-UPDRSIV), and Montreal Cognitive Assessment (MoCA). The SHAP importance ranking diagram is output to quantify the contribution of different features.

[0136] Combined with statistical analysis to verify the SHAP importance ranking diagram, the SHAP analysis results are as follows Figure 7 As shown in the figure, the contribution of each feature in the model is clearly shown. Finally, all clinical features of the two clusters were statistically analyzed to further explore the relationship between clustering results and clinical variables. Through the inter-group difference test of each feature, it was found that there were significant differences in many clinical features of the two groups of samples. These differences are highly consistent with the feature importance ranking obtained by the previous SHAP analysis. Table 1 shows the inter-group differences in clinical scores in the new classification.

[0137] Table 1

[0138] ClinicFeatures CLO CL1 P value MDS-UPDRSIII 38.703±11.813 54.167±18.573 <0.001*** MDS-UPDRSIV 3.649±3.482 8.222±3.813 <0.001*** HAMD-24 8.054±4.66 12.167±6.373 0.009** MMSE 25.322±2.224 26.782±1.957 0022* MDS-UPDRSII 18.622±8.281 20.444±6.07 0.410 MDS-UPDRSI 12.676±5.647 13.056±6.159 0.821

[0139] Specifically, features that showed high importance in the SHAP analysis, such as MDS-UPDRSIII and MDS-UPDRSIV, also showed highly significant differences between groups in the statistical analysis. This suggests that the key variables that the model focuses on when making predictions are mutually validated by the differences between the actual clinical features. In addition, the differences between the HAMA and MMSE groups also reached statistical significance, which further supports the contribution of these variables to the model's classification predictions.

[0140] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for characterizing Parkinson's disease samples based on local field potential, characterized in that: The steps include: The power spectrum density of the Beta frequency band signal of the local field potential of the sample is calculated to obtain the average power spectrum density curve; Perform peak analysis on the average power spectrum density curve to obtain the peak power spectrum density; Model the periodic component and non-periodic component of the average power spectrum density curve separately, and extract the periodic power spectrum and non-periodic component; Calculate the phase-amplitude coupling characteristics of the Beta frequency band signal of the local field potential of the sample; The peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling characteristics are used to perform feature clustering analysis and output the sample classification results.

2. A method for characteristic typing of Parkinson's disease samples based on local field potential according to claim 1, characterized in that: The method for calculating the power spectral density of the Beta frequency band signal of the local field potential of the sample is the Welch method, which includes the following steps: The original local field potential signal x[n] is framed and the windowed signal is calculated by combining the Hanning window function w[n]. The expression is as follows: x i [n]=x[n+i·N]·w[n] Where n represents the index of the sample within the frame, n∈[1,N-1], x i [n] represents the windowed signal of the i-th frame, x[n] represents the original signal, w[n] represents the Hanning window, and N is the length of the frame; Apply discrete Fourier transform to each frame of windowed signal and calculate the frequency domain signal X i [f], the expression is as follows: X i [f]=FFT(x i [n]) Where f stands for frequency, FFT stands for discrete Fourier transform, and x i [n] is the windowed signal of the i-th frame; The frequency domain signal of each frame is squared to obtain the power spectrum density P of the i-th frame. i [f], the expression is as follows: P i [f]=|X i [f]| 2 The power spectra of all frames are averaged to obtain the average power spectrum density curve P avg [f], the expression is as follows: Where N is the number of frames.

3. A method for characteristic typing of Parkinson's disease samples based on local field potential according to claim 1, characterized in that: Modeling the periodic component and non-periodic component of the average power spectrum density curve separately, extracting the periodic power spectrum and non-periodic component, including the following steps: The periodic oscillation components of the power spectrum are fitted using Gaussian functions to obtain the periodic power spectrum density P periodic (f), the expression is as follows: Among them, f represents frequency, A represents the amplitude of oscillation, CF represents the center frequency of oscillation, and BW represents the bandwidth of oscillation; The 1 / f-like function is used to fit the non-periodic component of the power spectrum to obtain the non-periodic power spectrum density P aperiodic (f), the expression is as follows: Among them, f represents frequency, offset represents the offset of the non-periodic component, and exponent represents the exponent of the non-periodic component; The periodic power spectrum density and the non-periodic power spectrum density are combined to construct a complete power spectrum density model. The model parameters are optimized by the least square method to minimize the mean square error and obtain the spectrum curve of the fitted power spectrum density. The non-periodic power spectrum density is subtracted from the fitted power spectrum to obtain the periodic power spectrum, and the non-periodic component is extracted from the non-periodic fitting curve.

4. The method for characteristic typing of Parkinson's disease samples based on local field potential according to claim 1, characterized in that: Calculating the phase-amplitude coupling characteristics of the local field potential Beta signal includes the following steps: The instantaneous phase of the low-frequency band of the Beta signal is obtained by Hilbert transform; the instantaneous amplitude of the high-frequency band of the Beta signal is obtained by Hilbert transform; The probability distribution of high-frequency amplitude and the connection between low-frequency phase are established by using the preset method to obtain the phase-amplitude coupling characteristics.

5. The method for characteristic typing of Parkinson's disease samples based on local field potential according to claim 4, characterized in that: The preset method for establishing the relationship between the probability distribution of high frequency amplitude and low frequency phase comprises the following steps: Divide the low-frequency phase into n equal-width intervals; In each phase interval, the average value of the high-frequency signal amplitude is calculated, the distribution histogram of the amplitude in the phase is constructed and the distribution histogram is normalized. The expression is as follows: Among them, P norm,i is the normalized probability distribution of the ith phase interval, P i is the amplitude histogram frequency in the ith interval, ∑ j P j is the sum of the amplitude histograms in all phase intervals; The Kullback-Leibler distance is used to calculate the degree to which the distribution deviates from the uniform distribution. KL , the expression is as follows: Among them, n represents the number of intervals, and U represents the uniform distribution probability; The phase-amplitude coupling feature MI is calculated by KL distance normalization, and the expression is as follows: 。 6. The method for characteristic typing of Parkinson's disease samples based on local field potential according to claim 1, characterized in that: The peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling characteristics are used to perform feature clustering analysis, which specifically includes the following steps: Perform dimensionality reduction on the extracted peak power spectrum density, periodic power spectrum, non-periodic component and phase-amplitude coupling features to obtain the reduced-dimensional features; Input the reduced-dimensional features into n clustering models respectively and output the sample typing results; Calculate the quality indicators of sample typing results, evaluate clustering quality, and select the best clustering scheme.

7. A method for characteristic typing of Parkinson's disease samples based on local field potential according to claim 6, characterized in that: The quality indicators of sample typing results included silhouette coefficient and Davies-Bouldin index.

8. A Parkinson's disease sample characteristic classification system based on local field potential, characterized in that: The system comprises: a memory and a processor, wherein the memory comprises a method program for characteristic typing of Parkinson's disease samples based on local field potentials, and when the method program for characteristic typing of Parkinson's disease samples based on local field potentials is executed by the processor, the steps of a method for characteristic typing of Parkinson's disease samples based on local field potentials as claimed in any one of claims 1 to 7 are implemented.

9. A method for training a Parkinson's disease sample classification model based on local field potential, using the classification results of the Parkinson's disease sample feature classification method based on local field potential according to any one of claims 1 to 7 for training, characterized in that: The following steps are involved: Using the sample scoring scale as an input feature, respectively train m classification models to classify the samples according to the classification results, and search for optimal hyperparameters; The leave-one-out cross-validation method was used to evaluate the model performance, output the best classification model and verify its interpretability.

10. The method for training a Parkinson's disease sample classification model based on local field potential according to claim 9, characterized in that: The method to verify the model interpretability includes the following steps: Use the SHAP method to calculate the impact of each input feature on the model classification results and output the SHAP importance ranking diagram; The SHAP importance ranking diagram was verified by combining statistical analysis, and the inter-group difference significance test was used to evaluate the impact of features on the classification results to ensure the interpretability of the model.

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