Method, system, device and medium for evaluating the therapeutic effect of Parkinson's disease

Abstract

The present invention provides a method, system, device and medium for evaluating the efficacy of Parkinson's disease, and relates to the technical field of medical care informatics. The present invention generates a gait variation coefficient by comprehensive analysis of motion state data and physiological parameters, and can make full use of multiple physiological indicators by establishing a calibration coefficient to achieve dynamic adjustment and judgment of the patient's gait stability. The implementation of this method will help to achieve more efficient and personalized treatment plans in clinical practice, significantly improve the optimization ability of DBS stimulation strategies, and not only help to reduce the patient's movement disorder manifestations, but also improve their quality of life.

Description

Technical Field

[0001] The present invention relates to the technical field of medical care informatics, and in particular to a method, system, device and medium for evaluating the therapeutic effect of Parkinson's disease. Background Art

[0002] With the rapid development of neuroscience and biomedical engineering, deep brain stimulation (DBS) technology has been widely used in the treatment of movement disorders such as Parkinson's disease (PD). DBS is a method of implanting electrodes and applying electrical stimulation to specific brain areas, aiming to relieve patients' motor symptoms. Studies have shown that the efficacy of DBS is closely related to stimulation parameters (such as frequency, current intensity, and pulse width), and different combinations of stimulation parameters can have a significant impact on the patient's motor state. Therefore, how to optimize stimulation parameters to improve treatment effects is an important topic in current medical research. At the same time, advances in sensor technology enable wearable devices to monitor patients' motor states and physiological parameters in real time, thus providing the possibility for personalized treatment.

[0003] In the prior art, the publication number is CN115762720A, and the name is a system, method, electronic device and storage medium for evaluating the efficacy of Parkinson's disease. The method for evaluating the efficacy of Parkinson's disease includes: collecting a certain number of TCM insomnia samples; preprocessing the TCM insomnia samples to establish an insomnia disease characteristic information database; calculating the data in the insomnia disease characteristic information database by using the AdaBoost algorithm, and constructing a self-learning model according to the calculation results; inputting the TCM insomnia characteristic information to be identified into the self-learning model, and outputting the TCM insomnia type and prescription information corresponding to the TCM insomnia characteristic information.

[0004] At present, the existing technology still has some shortcomings in the process of evaluating the efficacy of Parkinson's disease. Most of the existing evaluation methods rely on the subjective judgment of doctors and regular clinical evaluations, which not only increases the subjectivity of the judgment, but also leads to the failure to timely reflect the changes in the patient's status during the judgment cycle. In addition, traditional methods are usually unable to effectively integrate multi-dimensional data, such as the relationship between movement status data and physiological parameters, which makes the evaluation results lack comprehensiveness and representativeness;

[0005] Existing technologies for studying stimulation parameter combinations are often limited to a single dimension, and fail to comprehensively consider the impact of multiple physiological indicators on gait stability, thus affecting the effectiveness of optimizing stimulation strategies. Especially in the treatment of Parkinson's disease patients, slight changes in gait may indicate changes in the disease, so a more refined and personalized evaluation method is urgently needed.

[0006] The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method, system, device and medium for evaluating the therapeutic effect of Parkinson's disease, so as to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for evaluating the efficacy of Parkinson's disease, which is applied to the efficacy evaluation of deep brain stimulation, hereinafter referred to as DBS, and the specific steps include:

[0010] Step S1: setting the stimulation parameter combination of the DBS device, and obtaining historical monitoring data of the sample patient under different stimulation parameter combinations within the same monitoring time T, wherein the historical monitoring data includes but is not limited to motion state data and physiological parameters; and different stimulation parameters include different combinations of different frequencies, current intensities and pulse widths; motion state data include step frequency and stride; physiological parameters include heart rate standard deviation and skin conductance value fluctuation range;

[0011] Step S2: Under different stimulation parameter combinations, the step frequency and stride length included in the motion state data are calculated and analyzed to generate a gait variation coefficient, and an initial judgment threshold of the gait variation coefficient under each stimulation parameter combination is set;

[0012] The standard deviation of the heart rate under the corresponding stimulation parameter combination and the fluctuation range of the skin conductance value are combined and analyzed to generate a calibration coefficient for providing an adjustment strategy for the initial judgment threshold;

[0013] Step S3: obtaining stimulation parameters of the DBS device currently being received by the patient to be assessed to determine the current stimulation parameter combination, and collecting the motion state data and physiological parameters of the patient to be assessed under the current stimulation parameter combination, based on the collected motion state data and physiological parameters, obtaining the gait variation coefficient and initial judgment threshold of the patient to be assessed under the current stimulation parameter combination, adjusting the initial judgment threshold according to the calibration coefficient determined by the current stimulation parameter combination, thereby finally obtaining the adjusted real-time judgment threshold;

[0014] Step S4: Compare and analyze the gait variation coefficient calculated for the patient to be assessed under the current stimulation parameter combination with the adjusted real-time judgment threshold to generate an adjustment strategy for adjusting the stimulation parameters of the current DBS device.

[0015] A system for evaluating the therapeutic effect of Parkinson's disease, wherein the system is used to execute the method for evaluating the therapeutic effect of Parkinson's disease, comprising:

[0016] Historical monitoring data acquisition module: used to set the stimulation parameter combination of the DBS device, and obtain the historical monitoring data of the sample patients under different stimulation parameter combinations within the same monitoring time T. The historical monitoring data include but are not limited to motion state data and physiological parameters; and different stimulation parameters include different combinations of different frequencies, current intensities and pulse widths; motion state data include step frequency and stride; physiological parameters include heart rate standard deviation and skin conductance value fluctuation range;

[0017] Gait variation coefficient and calibration coefficient generation module: used to calculate and analyze the step frequency and stride length included in the motion state data under different stimulation parameter combinations to generate the gait variation coefficient and set the initial judgment threshold of the gait variation coefficient under each stimulation parameter combination;

[0018] The standard deviation of the heart rate under the corresponding stimulation parameter combination and the fluctuation range of the skin conductance value are combined and analyzed to generate a calibration coefficient for providing an adjustment strategy for the initial judgment threshold;

[0019] Real-time judgment threshold determination module: used to obtain the stimulation parameters of the DBS device currently received by the patient to be assessed, so as to determine the current stimulation parameter combination, and collect the motion state data and physiological parameters of the patient to be assessed under the current stimulation parameter combination, based on the collected motion state data and physiological parameters, to obtain the gait variation coefficient and initial judgment threshold of the patient to be assessed under the current stimulation parameter combination, and adjust the initial judgment threshold according to the calibration coefficient determined by the current stimulation parameter combination, so as to finally obtain the adjusted real-time judgment threshold;

[0020] Stimulation parameter adjustment module: used to compare and analyze the gait variation coefficient calculated for the patient to be assessed under the current stimulation parameter combination with the adjusted real-time judgment threshold, and generate an adjustment strategy for adjusting the stimulation parameters of the current DBS device.

[0021] A device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for evaluating the therapeutic effect of Parkinson's disease are implemented.

[0022] A readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for evaluating the efficacy of Parkinson's disease.

[0023] Compared with the prior art, the beneficial effects of the present invention are: by comprehensively analyzing the motion state data and physiological parameters, the gait variation coefficient is generated, and by establishing the calibration coefficient, multiple physiological indicators can be fully utilized to achieve dynamic adjustment and judgment of the patient's gait stability; the implementation of this method will help to achieve more efficient and personalized treatment plans in clinical practice, significantly improve the optimization ability of DBS stimulation strategies, not only help to reduce the patient's movement disorder manifestations, but also improve their quality of life. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0025] Figure 2 It is a system module block diagram of the present invention. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0027] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0028] Embodiment 1:

[0029] See also Figure 1 , the present invention provides a technical solution:

[0030] A method for evaluating the efficacy of Parkinson's disease, which is applied to the efficacy evaluation of deep brain stimulation, hereinafter referred to as DBS, and the specific steps include:

[0031] Step S1: setting the stimulation parameter combination of the DBS device, and obtaining historical monitoring data of the sample patient under different stimulation parameter combinations within the same monitoring time T, wherein the historical monitoring data includes but is not limited to motion state data and physiological parameters; and different stimulation parameters include different combinations of different frequencies, current intensities and pulse widths; motion state data include step frequency and stride; physiological parameters include heart rate standard deviation and skin conductance value fluctuation range;

[0032] The sample patients are multiple settings, and in this embodiment, the sample patients are at least 3;

[0033] Step S2: Under different stimulation parameter combinations, the step frequency and stride length included in the motion state data are calculated and analyzed to generate a gait variation coefficient, and an initial judgment threshold of the gait variation coefficient under each stimulation parameter combination is set;

[0034] The standard deviation of the heart rate under the corresponding stimulation parameter combination and the fluctuation range of the skin conductance value are combined and analyzed to generate a calibration coefficient for providing an adjustment strategy for the initial judgment threshold;

[0035] Step S3: obtaining stimulation parameters of the DBS device currently being received by the patient to be assessed to determine the current stimulation parameter combination, and collecting the motion state data and physiological parameters of the patient to be assessed under the current stimulation parameter combination, based on the collected motion state data and physiological parameters, obtaining the gait variation coefficient and initial judgment threshold of the patient to be assessed under the current stimulation parameter combination, adjusting the initial judgment threshold according to the calibration coefficient determined by the current stimulation parameter combination, thereby finally obtaining the adjusted real-time judgment threshold;

[0036] Step S4: Compare and analyze the gait variation coefficient calculated for the patient to be assessed under the current stimulation parameter combination with the adjusted real-time judgment threshold to generate an adjustment strategy for adjusting the stimulation parameters of the current DBS device.

[0037] Further description, for the description of wearable sensors on patients, including:

[0038] Choose a multifunctional wearable sensor device that meets medical standards and contains the following sensors:

[0039] Accelerometer: can monitor the motion status in real time and record the three-axis acceleration data;

[0040] Heart rate sensor: can continuously monitor heart rate data;

[0041] Skin conductance sensor: used to record skin conductance (GSR);

[0042] Installation location: According to the patient's physical characteristics, the accelerometer and gyroscope are installed on the patient's ankle, waist and wrist, the heart rate sensor is installed on the chest, and the skin electrode sensor is attached to the inner side of the forearm;

[0043] Equipment calibration: Before starting data collection, all sensors are calibrated to ensure data accuracy;

[0044] Setting of stimulation parameters of DBS device, including:

[0045] The DBS device was set with the following stimulation parameters:

[0046] Set the increasing frequency sequence set to ;in, =1Hz, and represents the frequency value after the u1th increment, and U1 represents the total number of frequency increments; The upper limit value is determined according to the doctor's evaluation and the DBS equipment usage standard; and the difference between adjacent frequencies in the frequency sequence set is the same value, which is set to 1 Hz in this embodiment;

[0047] In this embodiment 10Hz;

[0048] Set the increasing current intensity sequence set to ;in, =1mA, and represents the current intensity value after the u2th increment, and U2 represents the total number of increments of the current intensity value; The upper limit value is determined according to the doctor's evaluation and the DBS equipment use standard; and the difference between adjacent current intensity values ​​in the current intensity sequence set is the same value, and in this embodiment, the difference is set to 0.5mA or 1mA;

[0049] In this embodiment, 3mA;

[0050] Set the pulse width sequence set to increment ;in, = 1 μs, and represents the pulse width value after the u3th increment, and U3 represents the total number of increments of the pulse width value; The upper limit value is determined according to the doctor's evaluation and the DBS equipment use standard; and the difference between adjacent pulse widths in the pulse width sequence set is the same value, and the difference is set to 2μs in this embodiment;

[0051] In this embodiment, 150μs;

[0052] Select any frequency, current intensity and pulse width from the increasing sequence sets C1, C2 and C3 respectively to form a stimulation parameter combination characteristic value. ;

[0053] Under each combination of stimulation parameters, wearable sensors were used to simultaneously record the patient’s motion status and physiological parameters;

[0054] Noise and outliers were removed, including the use of a moving average method: the window size was specifically set to 5 seconds, within which the moving averages of gait parameters and physiological parameters were calculated to remove abnormal fluctuations in a short period of time;

[0055] Implement the Kalman filter algorithm to smooth the cleaned data; use the corresponding SciPy library in MATLAB or Python;

[0056] Determine the state transfer and observation models to define the state and measurement equations of sensor data respectively;

[0057] Compare the original data with the processed data to ensure that volatility is significantly reduced and data trends are more stable;

[0058] Database design: Establish a structured database, which in this embodiment is MySQL or PostgreSQL, to store the cleaned and smoothed data. The design of the data table includes:

[0059] Patient ID, stimulation parameter combination, data type including motion state and physiological parameters, timestamp, data value;

[0060] Extract the movement status and physiological parameter data of a specific patient under different stimulation parameter combinations from the database to ensure the automation of the extraction process;

[0061] Organize the extracted data into a format that is convenient for analysis, ensuring that each record includes the patient ID, stimulation parameters, and corresponding motion / physiological data;

[0062] Set the characteristic value of each stimulus parameter combination Perform n data collections within the monitoring time T;

[0063] The sample patient's characteristic value in each stimulation parameter combination The step frequency and stride length are expressed as , ; is the stimulus parameter combination The nth step frequency measured below; is the stimulus parameter combination The nth stride length measured below;

[0064] according to , calculate the average of the step frequency and standard deviation ;

[0065] The average step frequency is recorded as , the calculation formula is as follows:

[0066] ;

[0067] in, represents the step frequency measured for the i-th time, i∈{1,2,…,n};

[0068] The standard deviation of the cadence is recorded as , the calculation formula is as follows:

[0069] ;

[0070] Combined with and , calculate the coefficient of variation of step frequency , the calculation formula is as follows:

[0071] ;

[0072] according to , calculate the average stride length and standard deviation ;

[0073] The average stride length is recorded as , the calculation formula is as follows:

[0074] ;

[0075] The standard deviation of the stride length is recorded as , the calculation formula is as follows:

[0076] ;

[0077] in represents the stride measured for the i-th time, i∈{1,2,…,n};

[0078] Combined with and , the coefficient of variation of the calculated stride is recorded as , the calculation formula is as follows:

[0079] ;

[0080] Calculate the characteristic value of the stimulation parameter combination for the sample patient Gait coefficient of variation , the calculation formula is:

[0081] ;

[0082] in Indicated in and Select the largest value among them;

[0083] And in the stimulus parameter combination characteristic value Down;

[0084] In the stimulus parameter combination characteristic value The gait variation coefficients of all sample patients are calculated, and the minimum and maximum values ​​are selected from these gait variation coefficients, which are recorded as M1 and M2 respectively, and then set The value range is [M1, M2], setting the gait variation coefficient The initial judgment threshold is ,and ∈(M1,M2);

[0085] like ≥ , it means that the gait is unstable, and the larger the value, the more unstable the gait, and the characteristic value of the stimulus parameter combination The less effective it is for Parkinson's disease;

[0086] like < , it means the gait is stable, and the smaller the value, the more stable the gait, and the characteristic value of the stimulus parameter combination The better the effect is for Parkinson's disease.

[0087] Further explanation: within the monitoring time T, according to the stimulation parameter combination characteristic value of the sample patient Calculate the standard deviation of heart rate based on the n heart rate values ​​under ; and based on the sample patient's stimulation parameter combination characteristic value n times the skin conductance value under the condition of skin conductance fluctuation range ;in are the minimum and maximum values ​​of skin conductance, respectively;

[0088] The minimum value of skin conductance is the lowest value of skin conductance measured when the individual is in a relaxed or resting state;

[0089] Skin conductance refers to the skin's ability to conduct electric current and is quantified in microSiemens (µS).

[0090] This embodiment In the range of 1µS-2µS; at this time, the skin conductance is relatively low, indicating that the individual's sympathetic nerve activity is relatively low and the emotional state is relatively stable;

[0091] The maximum skin conductance value is the highest value of skin conductance measured when an individual experiences strong emotions, stress, or physiological stimulation;

[0092] This embodiment Reaching 20µS-30µS or higher; in extreme cases, such as in states of intense anxiety, fear or excitement, the maximum value exceeds 50µS;

[0093] Defined in the stimulus parameter combination characteristic value The calibration coefficient is , the calculation formula is:

[0094] ;

[0095] The standard deviation of heart rate calculated within the monitoring time T is calculated in the same way as the standard deviation of stride length. The same, no further elaboration;

[0096] The calibration coefficients are processed by standardized data processing. The valid value range of is limited to (0,1). is a correction term used to avoid the numerator being 0, and , this embodiment The value is 0.01, and Ability to make adjustments based on experimental data according to the expert group system;

[0097] like The closer it is to 0, the more stable the gait is, the smaller the fluctuation of heart rate and skin conductance is, and the better the treatment effect is;

[0098] like The closer it is to 1, the more unstable the gait, the greater the fluctuations in heart rate and skin conductance, and the worse the treatment effect;

[0099] The adjustment strategy of the initial judgment threshold includes:

[0100] The real-time judgment threshold after the initial judgment threshold is set to , the calculation formula is as follows:

[0101] ;

[0102] in is the adjustment coefficient, To ensure ∈(M1,M2);

[0103] like ≥ , it means that the gait is unstable. If the value is positive, the initial judgment threshold needs to be increased to reduce the probability of triggering intervention by adjusting the strategy; reduce the patient's anxiety and discomfort;

[0104] like < , it means the gait is stable, Make sure that If the value is negative, the initial judgment threshold needs to be reduced to increase the probability of adjusting the strategy to trigger intervention.

[0105] Further explanation: the stimulation parameter combination currently received by the patient to be assessed is set as ;

[0106] and ;

[0107] Define the combination of stimulation parameters that the patient to be assessed is currently receiving The coefficient of variation of gait under ; Define the real-time judgment threshold after adjustment of the calibration coefficient as ;

[0108] like ≥ , it indicates that the gait is unstable, and the eigenvalue of the stimulus parameter combination The worse the effect on Parkinson's disease; for the combination of stimulation parameters The adjustment strategy is:

[0109] ;

[0110] like < , it means the gait is stable. For the combination of stimulation parameters The adjustment strategy is:

[0111] ;

[0112] in, They are the adjusted values ​​of the frequency, current intensity and pulse width of the DBS device stimulation parameters.

[0113] Embodiment 2:

[0114] See also Figure 2 , a system for evaluating the efficacy of Parkinson's disease, the system is used to execute the method for evaluating the efficacy of Parkinson's disease, comprising:

[0115] Historical monitoring data acquisition module: used to set the stimulation parameter combination of the DBS device, and obtain the historical monitoring data of the sample patients under different stimulation parameter combinations within the same monitoring time T. The historical monitoring data include but are not limited to motion state data and physiological parameters; and different stimulation parameters include different combinations of different frequencies, current intensities and pulse widths; motion state data include step frequency and stride; physiological parameters include heart rate standard deviation and skin conductance value fluctuation range;

[0116] Gait variation coefficient and calibration coefficient generation module: used to calculate and analyze the step frequency and stride length included in the motion state data under different stimulation parameter combinations to generate the gait variation coefficient and set the initial judgment threshold of the gait variation coefficient under each stimulation parameter combination;

[0117] The standard deviation of the heart rate under the corresponding stimulation parameter combination and the fluctuation range of the skin conductance value are combined and analyzed to generate a calibration coefficient for providing an adjustment strategy for the initial judgment threshold;

[0118] Real-time judgment threshold determination module: used to obtain the stimulation parameters of the DBS device currently received by the patient to be assessed, so as to determine the current stimulation parameter combination, and collect the motion state data and physiological parameters of the patient to be assessed under the current stimulation parameter combination, based on the collected motion state data and physiological parameters, to obtain the gait variation coefficient and initial judgment threshold of the patient to be assessed under the current stimulation parameter combination, and adjust the initial judgment threshold according to the calibration coefficient determined by the current stimulation parameter combination, so as to finally obtain the adjusted real-time judgment threshold;

[0119] Stimulation parameter adjustment module: used to compare and analyze the gait variation coefficient calculated for the patient to be assessed under the current stimulation parameter combination with the adjusted real-time judgment threshold, and generate an adjustment strategy for adjusting the stimulation parameters of the current DBS device.

[0120] Embodiment three:

[0121] A device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for evaluating the therapeutic effect of Parkinson's disease are implemented.

[0122] Embodiment 4:

[0123] A readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for evaluating the efficacy of Parkinson's disease.

[0124] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0125] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for evaluating the efficacy of Parkinson's disease, the method being applied to evaluating the efficacy of deep brain stimulation (hereinafter referred to as DBS), characterized in that: The specific steps include: Step S1: setting the stimulation parameter combination of the DBS device, and obtaining historical monitoring data of the sample patient under different stimulation parameter combinations within the same monitoring time T, wherein the historical monitoring data includes but is not limited to motion state data and physiological parameters; and different stimulation parameters include different combinations of different frequencies, current intensities and pulse widths; motion state data include step frequency and stride; physiological parameters include heart rate standard deviation and skin conductance value fluctuation range; Step S2: Under different stimulation parameter combinations, the step frequency and stride length included in the motion state data are calculated and analyzed to generate a gait variation coefficient, and an initial judgment threshold of the gait variation coefficient under each stimulation parameter combination is set; During the monitoring time T, the sample patient has a combination of stimulation parameter characteristic values The motion state data and physiological parameters under the condition are collected n times; The sample patient's stimulus parameter combination characteristic value The step frequency and stride length are expressed as , ; is the stimulus parameter combination The nth step frequency measured below; is the stimulus parameter combination The nth stride length measured below; according to , calculate the average of the step frequency and standard deviation ; Combined with and , calculate the coefficient of variation of step frequency , the calculation formula is as follows: according to , calculate the average stride length and standard deviation ; Combined with and , the coefficient of variation of the calculated stride is recorded as , the calculation formula is as follows: Calculate the characteristic value of the stimulation parameter combination for the sample patient Gait coefficient of variation , the calculation formula is: in Indicated in and Select the largest value among them; set up The value range is [M1, M2], setting the gait variation coefficient The initial judgment threshold is ,and ∈(M1,M2); like ≥ , it means that the gait is unstable, and the larger the value, the more unstable the gait. The less effective it is for Parkinson's disease; like < , it means the gait is stable, and the smaller the value, the more stable the gait, and the characteristic value of the stimulus parameter combination The better the effect on Parkinson's disease; The standard deviation of the heart rate under the corresponding stimulation parameter combination and the fluctuation range of the skin conductance value are combined and analyzed to generate a calibration coefficient for providing an adjustment strategy for the initial judgment threshold; During the monitoring time T, the sample patient's stimulation parameter combination characteristic value is collected. Calculate the standard deviation of heart rate based on the n heart rate values ​​under ; and collect the characteristic values ​​of the stimulation parameter combination of the sample patients n times of skin conductance values ​​under the condition of ;in are the minimum and maximum values ​​of skin conductance, respectively; Defined in the stimulus parameter combination characteristic value The calibration coefficient is , the calculation formula is: The calibration factor The valid value range of is limited to (0,1). is a correction term used to avoid the numerator being 0, and ; like The closer it is to 0, the more stable the gait is, the smaller the fluctuation of heart rate and skin conductance is, and the better the treatment effect is; like The closer it is to 1, the more unstable the gait, the greater the fluctuations in heart rate and skin conductance, and the worse the treatment effect; The adjustment strategy of the initial judgment threshold includes: The real-time judgment threshold obtained after the initial judgment threshold is adjusted is , the calculation formula is as follows: in is the adjustment coefficient, and To ensure ∈(M1,M2); like ≥ , it means that the gait is unstable. If the value is positive, the initial judgment threshold needs to be increased to reduce the probability of adjusting the strategy to trigger intervention; like < , it means the gait is stable, Make sure that If the value is negative, the initial judgment threshold needs to be reduced to increase the probability of adjusting the strategy to trigger intervention; Step S3: obtaining stimulation parameters of the DBS device currently being received by the patient to be assessed to determine the current stimulation parameter combination, and collecting the motion state data and physiological parameters of the patient to be assessed under the current stimulation parameter combination, based on the collected motion state data and physiological parameters, obtaining the gait variation coefficient and initial judgment threshold of the patient to be assessed under the current stimulation parameter combination, adjusting the initial judgment threshold according to the calibration coefficient determined by the current stimulation parameter combination, thereby finally obtaining the adjusted real-time judgment threshold; Set the current stimulation parameter combination of the patient to be assessed to ;and ; Define the combination of stimulation parameters that the patient to be assessed is currently receiving The coefficient of variation of gait under ; Define the real-time judgment threshold after adjustment of the calibration coefficient as ; Step S4: comparing and analyzing the gait variation coefficient calculated for the patient to be assessed under the current stimulation parameter combination with the adjusted real-time judgment threshold, and generating an adjustment strategy for adjusting the stimulation parameters of the current DBS device; like ≥ , it means that the gait is unstable, and the eigenvalue of the stimulus parameter combination The worse the effect on Parkinson's disease; for the combination of stimulation parameters The adjustment strategy is: like < , it means the gait is stable. For the combination of stimulation parameters The adjustment strategy is: in, They are the adjusted values ​​of the frequency, current intensity and pulse width of the DBS device stimulation parameters.

2. The method for evaluating the therapeutic effect of Parkinson's disease according to claim 1, characterized in that: Setting of stimulation parameters of DBS device, including: The DBS device was set with the following stimulation parameters: Set the increasing frequency sequence set to ;in, =1Hz, and represents the u1th frequency value, and U1 represents the total number of frequencies; Set the increasing current intensity sequence set to ;in, =1mA, and represents the u2th current intensity value, and U2 represents the total number of current intensity values; Set the incrementing pulse width sequence set to ;in, = 1 μs, and represents the u3th pulse width value, and U3 represents the total number of pulse width values; Select any frequency, current intensity and pulse width from the increasing sequence sets C1, C2 and C3 respectively to form a stimulation parameter combination characteristic value. .

3. A system for evaluating the therapeutic effect of Parkinson's disease, characterized in that: The system is used to execute the method for evaluating the efficacy of Parkinson's disease according to any one of claims 1 to 2, comprising: Historical monitoring data acquisition module: used to set the stimulation parameter combination of the DBS device, and obtain the historical monitoring data of the sample patients under different stimulation parameter combinations within the same monitoring time T. The historical monitoring data include but are not limited to motion state data and physiological parameters; and different stimulation parameters include different combinations of different frequencies, current intensities and pulse widths; motion state data include step frequency and stride; physiological parameters include heart rate standard deviation and skin conductance value fluctuation range; Gait variation coefficient and calibration coefficient generation module: used to calculate and analyze the step frequency and stride length included in the motion state data under different stimulation parameter combinations to generate the gait variation coefficient and set the initial judgment threshold of the gait variation coefficient under each stimulation parameter combination; The standard deviation of the heart rate under the corresponding stimulation parameter combination and the fluctuation range of the skin conductance value are combined and analyzed to generate a calibration coefficient for providing an adjustment strategy for the initial judgment threshold; Real-time judgment threshold determination module: used to obtain the stimulation parameters of the DBS device currently received by the patient to be assessed, so as to determine the current stimulation parameter combination, and collect the motion state data and physiological parameters of the patient to be assessed under the current stimulation parameter combination, based on the collected motion state data and physiological parameters, to obtain the gait variation coefficient and initial judgment threshold of the patient to be assessed under the current stimulation parameter combination, and adjust the initial judgment threshold according to the calibration coefficient determined by the current stimulation parameter combination, so as to finally obtain the adjusted real-time judgment threshold; Stimulation parameter adjustment module: used to compare and analyze the gait variation coefficient calculated for the patient to be assessed under the current stimulation parameter combination with the adjusted real-time judgment threshold, and generate an adjustment strategy for adjusting the stimulation parameters of the current DBS device.

4. A device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for evaluating the therapeutic effect of Parkinson's disease as claimed in any one of claims 1 to 2 are implemented.

5. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the therapeutic effect of Parkinson's disease as claimed in any one of claims 1 to 2 are implemented.

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