Closed-loop nerve regulation and control system based on medicine and motion state of Parkinson's disease patient

Abstract

The invention discloses a closed-loop nerve regulation and control system based on the drug and motion state of a Parkinson's disease patient, and the system comprises a parameter setting module which is used for determining a low beta frequency band, a high beta frequency band, an upper threshold value, and a lower threshold value; the signal acquisition module is used for acquiring local field potential signals of the STN of the Parkinson's disease patient and preprocessing the local field potential signals; the feature calculation module is used for calculating low-beta frequency band energy and high-beta frequency band energy through short-time Fourier transform, and calculating an average value of a ratio of the low-beta frequency band energy to the high-beta frequency band energy; the judgment output module is used for comparing the average value with an upper threshold value and a lower threshold value; if yes, it is judged that the patient is in a drug failure-motion state, and high-intensity stimulation is output; if the value is larger than the lower threshold value and smaller than the upper threshold value, it is judged that the patient is in a drug failure-resting state, and moderate-intensity stimulation is output; if yes, judging that the patient is in a drug effective state, and outputting low-intensity stimulation. According to the invention, accurate closed-loop deep brain electrical stimulation can be realized based on drugs and motion states.

Description

Technical Field

[0001] The present invention belongs to the field of digital medical instruments, and particularly relates to a closed-loop neuromodulation system based on the drug and movement state of Parkinson's disease patients. Background Art

[0002] Deep Brain Stimulation (DBS) has been widely used in the clinical treatment of Parkinson's disease. By precisely delivering electrical stimulation to deep brain nuclei, usually the subthalamic nucleus (STN) or the internal globus pallidus (GPi), the function of the corresponding circuit can be restored to relative normality.

[0003] The currently commonly used traditional open-loop DBS can provide continuous and fixed-parameter stimulation, which has been proven to significantly relieve the motor symptoms of Parkinson's disease patients. However, the clinical symptoms of different Parkinson's disease patients are different, and the symptoms of individual patients will also fluctuate on different time scales. Therefore, the open-loop DBS with unadjustable stimulation parameters according to symptom changes may cause many side effects, such as depression, dyskinesia, dysarthria, etc.

[0004] Using a closed-loop deep brain stimulator can dynamically adjust the stimulation parameters according to the symptom changes of Parkinson's disease patients, that is, to achieve closed-loop adaptive DBS (adaptive DBS, aDBS). The Chinese patent document with the publication number CN114462455A discloses a calculation method for evaluating the stimulation effect of closed-loop DBS in Parkinson's state based on a computational model, including: S1: Signal acquisition: Collect LFP signals based on the computational model; S2: Signal preprocessing: Filter and downsample the LFP signals; S3: Time-domain segmentation: Separate β bursts from the preprocessed LFP signals; S4: Statistical analysis: Conduct statistical analysis according to the duration of the β bursts obtained in step S3, and perform binary classification of long oscillations and short oscillations; S5: Quantification: Quantify the binary classification results obtained in step S4 to obtain an evaluation index for the stimulation effect of closed-loop DBS.

[0005] In order to make the closed-loop DBS treatment more effective, finding biomarkers that can be quantified, easily measured, and accurately reflect the severity of Parkinson's symptoms is the primary task.

[0006] Numerous studies have shown that the abnormal enhancement of beta (12 - 35Hz) activity in the subthalamic nucleus (STN) of the basal ganglia is closely associated with the severity of motor symptoms in Parkinson's disease patients. Existing studies have achieved closed-loop DBS control by analyzing the pathological beta oscillations of local field potential (LFP) signals in the STN in real-time, using the energy in the beta frequency band as a feedback signal to regulate the magnitude of the stimulation current output.

[0007] On this basis, in recent years, some studies have set the sensing frequency band of closed-loop DBS according to the electroencephalogram characteristics of Parkinson's disease patients, to the frequency band where the patient's beta energy is most concentrated. However, there are still two problems with the method of using only a single beta frequency band to control the stimulation output: First, the energy of the selected beta frequency band is often affected by non-pathological factors, such as daily movement, circadian rhythm, etc., resulting in changes in its energy or shifts in its peak. Second, the energy of a single beta frequency band often cannot accurately reflect the patient's symptoms, dopaminergic drugs, and movement at the same time, that is, it cannot distinguish between pathological and autonomous activities of STN-beta, resulting in inaccurate regulation of closed-loop DBS. Summary of the Invention

[0008] The present invention provides a closed-loop neural regulation system based on the drug and movement states of Parkinson's disease patients, which can accurately identify different medication and movement situations of patients, and precisely achieve adaptive closed-loop deep brain stimulation based on dopaminergic drugs and walking movement states.

[0009] A closed-loop neural regulation system based on the drug and movement states of Parkinson's disease patients, comprising: A parameter setting module for determining a low beta frequency band, a high beta frequency band, an upper threshold, and a lower threshold; wherein, the upper threshold is used to distinguish the movement state, and the lower threshold is used to distinguish the drug state; A signal acquisition module for real-time collecting local field potential signals of the STN of Parkinson's disease patients, and inputting them into the feature calculation module after signal preprocessing; A feature calculation module for calculating the energy of the low beta frequency band several times per second through short-time Fourier transform and the energy of the high beta frequency band , and calculating the average value of the ratio of the energy of the low beta frequency band and the energy of the high beta frequency band once every several seconds; ; A determination and output module for comparing the average value obtained by the feature calculation module with the upper threshold and the lower threshold; if Greater than the upper threshold, it is determined that the patient is in the drug failure - movement state. At this time, high-intensity stimulation is output; if Greater than the lower threshold and less than the upper threshold, it is determined that the patient is in the drug failure - resting state. At this time, medium-intensity stimulation is output; if Less than the lower threshold, it is determined that the patient is in the drug effective state. At this time, low-intensity stimulation is output.

[0010] Furthermore, the specific working process of the parameter setting module is as follows: In the three states of drug failure - resting, drug failure - movement, and drug effective, several segments of local field potential signals of the bilateral STN of Parkinson's disease patients are collected as baselines; Compare the power spectral densities of the drug failure and drug effective states and select the low beta frequency band inhibited by the drug; compare the power spectral densities of the resting and movement states and select the high beta frequency band inhibited by movement; Set the upper threshold for distinguishing the movement state and the lower threshold for distinguishing the drug state according to the baseline data.

[0011] Furthermore, in the parameter setting module, the low beta frequency band, high beta frequency band, upper threshold, and lower threshold are all determined separately for different patients and different sides of the brain.

[0012] Preferably, the sampling rate of the signal acquisition module is 256 Hz, and signal preprocessing with a 1 - 100 Hz band-pass filter is performed simultaneously.

[0013] Furthermore, in the feature calculation module, the low beta frequency band energy The calculation formula is: ; In the formula, Represents the low beta frequency band energy corresponding to the th window, And Are the ranges of the low beta frequency band, Is the signal, Is the total length of the signal, Is the window width, Is the index of the window, Is the window shift, Is the window function, Represents the index of the frequency component, Represents the index of the signal point, Represents the unit of the complex number.

[0014] Furthermore, in the feature calculation module, the high beta frequency band energy The calculation formula is: ; In the formula, is the high-beta band energy corresponding to the th window, and and is the range of the high-beta frequency band.

[0015] Furthermore, in the feature calculation module, the low-beta band energy and the high-beta band energy are calculated to obtain the average value of the ratio , and the formula is: ; In the formula, is the index of the window, and is the number of windows.

[0016] Preferably, in the feature calculation module, the low-beta band energy and the high-beta band energy are calculated 10 times per second; and the average value of the ratio of the low-beta band energy and the high-beta band energy is calculated once every 10 seconds.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Based on the phenomenon observed in Parkinson's disease patients that dopaminergic drugs can significantly inhibit the low-beta band energy in the STN and walking exercise can significantly inhibit the high-beta band energy, the present invention defines the ratio LHR of the low-beta and high-beta energies in the STN, thereby accurately identifying different medication and exercise conditions of patients. This solves the problem that the closed-loop DBS with a single beta band energy as the feedback index cannot distinguish the pathological and autonomous activities of STN-beta, resulting in inaccurate regulation.

[0018] 2. The online signal processing and feature calculation method of the present invention applied to the closed-loop deep brain stimulator only performs a simple short-time Fourier transform several times per second to calculate the energy of a specific frequency band, and calculates the average value of LHR once every several seconds and compares it with a threshold, balancing the calculation accuracy and calculation delay, so that it can be directly applied to the implantable pulse stimulator with limited performance in the closed-loop deep brain stimulator system. In addition, this calculation method also solves the problem of spontaneous fluctuations in STN activity on a short time scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 FIG. is the time-frequency diagram and power spectral density of the local field potential signal of the left STN of Parkinson's disease patient A before and after taking dopaminergic drugs in the embodiment of the present invention.

[0020] Figure 2 The time-frequency diagram and power spectral density of the local field potential signal of the left STN before and after the failure of dopamine drugs in Parkinson's disease patient B in the embodiments of the present invention.

[0021] Figure 3 The time-frequency diagram and power spectral density of the local field potential signal of the left STN in Parkinson's disease patient A in the resting and movement states in the embodiments of the present invention.

[0022] Figure 4 The time-frequency diagram and power spectral density of the local field potential signal of the left STN during the alternation of rest and movement in Parkinson's disease patient B in the embodiments of the present invention.

[0023] Figure 5 The power spectral density of the local field potential signal of the left STN in Parkinson's disease patients A and B respectively in the "off"-drug resting state, "off"-drug movement state, and "on"-drug state in the embodiments of the present invention.

[0024] Figure 6 The time-frequency diagram of the local field potential signal of the left STN in Parkinson's disease patient A in the embodiments of the present invention, the change of the low-to-high beta energy ratio LHR value, and the change of the closed-loop stimulation amplitude controlled thereby.

[0025] Figure 7 A schematic diagram of a closed-loop neuromodulation system based on the drug and movement states of Parkinson's disease patients in the embodiments of the present invention. Detailed implementation manners

[0026] The present invention will be further described in detail below with reference to the drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention and do not limit it in any way.

[0027] A closed-loop neuromodulation system based on the drug and movement states of Parkinson's disease patients provided by the present invention uses a closed-loop neuromodulation method and realizes aDBS through a closed-loop deep brain stimulator device. The closed-loop neuromodulation method meets the following requirements: First, the EEG features selected by the algorithm can accurately identify different dopamine drug and movement states of Parkinson's disease patients, so as to output a corresponding intensity of current according to the current state; second, the computational complexity of the algorithm should be moderate to avoid the phenomenon that the feature calculation method is too complex and the data volume of a single calculation is too large, resulting in exceeding the performance of the closed-loop deep brain stimulator device or increasing the delay of the closed-loop system.

[0028] By offline analyzing the LFP signals of the subthalamic nucleus (STN) in Parkinson's disease patients before and after taking medicine at rest, it is found that in the "off" (drug ineffective) state with relatively severe symptoms, there is a very obvious pathological low-beta (12 - 20 Hz) energy, and the peak value of this frequency band is different in different side brains of different patients. After taking dopaminergic drugs for about 0.5 - 1 hour, the drug gradually takes effect along with the improvement of the patients' symptoms. At this time, it is the "on" (drug effective) state, and a significant decrease in pathological low-beta energy can be detected; after 2 - 4 hours, the drug concentration decreases, resulting in the invalidation of the drug effect, and the electroencephalogram shows that the pathological low-beta energy rebounds to the level before taking medicine, along with the reappearance of the patients' symptoms.

[0029] During the above process, there is always high-beta (20 - 35 Hz) activity of the STN at different levels, and the frequency range of the energy of this frequency band is also different in different side brains of different patients. Similarly, in the "off" state of the drug, it is found that the patients' autonomous walking movement significantly inhibits the high-beta activity of the STN and attenuates the energy of the high-beta frequency band. In addition, the autonomous walking movement will also cause the peak value of the originally highly active low-beta frequency band to shift to a higher frequency, and the total energy remains basically the same. Thus, it can be seen that dopaminergic drugs and walking movement modulate the activity of the STN by attenuating low-beta and high-beta energies respectively.

[0030] Therefore, the present invention distinguishes different medication and movement states of patients by calculating the ratio of low-beta and high-beta energies (low beta-high beta ratio, LHR) in specific frequency bands of the STN in the left and right brains of Parkinson's disease patients respectively. Specifically, the "off"-movement state corresponds to higher low-beta energy and lower high-beta energy, so the LHR value is the largest; the "off"-rest state corresponds to higher low-beta and high-beta energies, so the LHR value is medium; the "on" state of the drug corresponds to lower low-beta energy and higher high-beta energy, so the LHR value is the smallest. Taking the LHR value as a feedback signal, aDBS based on dopaminergic drugs and movement states can be achieved.

[0031] The abnormal enhancement of beta (12 - 35 Hz) activity in the STN nucleus of the basal ganglia is closely related to the severity of the motor symptoms of Parkinson's disease patients. Figure 1 It is the time-frequency spectrum diagram and power spectral density (PSD) of the local field potential signal of the left STN in Parkinson's disease patient A before and after taking dopaminergic drugs. As Figure 1As shown, the top is a time-frequency spectrum diagram, with the horizontal axis representing time (in minutes) and the vertical axis representing frequency (in hertz). The color in the diagram represents the power spectral density value (in decibels) of a certain frequency component at a certain moment. The closer the color is to red, the higher the energy; the closer it is to blue, the lower the energy. Signals for 5 minutes before and after taking the medicine (marked in white) are intercepted for power spectral density analysis, and the power spectral density diagram shown at the bottom is obtained, where the horizontal axis is frequency and the vertical axis is the power spectral density value. It can be seen that when the patient's symptoms were more severe before taking the medicine, there was obvious pathological low-beta energy with a peak frequency of 13 Hz and some high-beta energy. After the medicine took effect, this pathological low-beta energy was quickly suppressed, leaving only some high-beta energy, and at this time the patient's symptoms were relieved and the patient felt comfortable.

[0032] Figure 2 Time-frequency diagram and power spectral density of the local field potential signal of the left STN before and after the failure of B dopamineergic drugs in Parkinson's disease patients. The top is the time-frequency spectrum diagram. Signals for 5 minutes before and after the failure of the medicine effect (marked in white) are intercepted for power spectral density analysis, and the following power spectral density diagram is obtained. It can be analyzed that, different from Parkinson's disease patient A, patient B has more high-beta energy in the electroencephalogram when the medicine takes effect; after the medicine effect fails, the pathological low-beta energy with a peak frequency of 18 Hz increases significantly, and at this time the high-beta energy also drops to a lower level, and the patient's symptoms reappear. In summary, dopamineergic drugs can significantly inhibit the activity of pathological low-beta in the STN.

[0033] Nowadays, most studies, based on the above electroencephalogram characteristics, set the induction frequency band of closed-loop DBS as the frequency band where the pathological low-beta energy of Parkinson's disease patients is most concentrated. However, there are still the following problems when only using the energy of a single beta frequency band to control the stimulation output: First, the energy of the selected beta frequency band is often affected by non-pathological factors, such as daily movement, circadian rhythm, etc., resulting in changes in its energy or peak shift; Second, the energy of a single beta frequency band often cannot accurately reflect the patient's symptoms, dopamineergic drugs, and movement at the same time, that is, it cannot distinguish the pathological activity and autonomous activity of STN-beta, resulting in inaccurate regulation of closed-loop DBS.

[0034] According to the latest research and observations of Parkinson's disease patients, it is found that under the open-loop stimulation parameters of daily programming, the fluctuations of dopamineergic drugs (such as Figure 1 and Figure 2As shown, it will lead to the emergence of negative symptoms such as abnormal movements. Therefore, the above-mentioned closed-loop stimulation that is dynamically adjusted according to the changes in pathological low-beta energy is necessary. Moreover, more importantly, it is found that the current magnitude of daily programming cannot meet the needs of the voluntary movements of Parkinson's disease patients, especially the walking movements. The results of clinical tests show that on the basis of the drug-off state and daily programming parameters, increasing the stimulation intensity within a certain range can improve the patient's motor performance to a certain extent, especially further alleviating the symptoms of bradykinesia. Therefore, it is also important to dynamically adjust the stimulation parameters according to the patient's motor state.

[0035] Figure 3 Figure 4 is the time-frequency diagram and power spectral density of the local field potential signals of the left STN of Parkinson's disease patient A at rest and during movement after drug failure. The top is the time-frequency diagram, and the signals of the marked rest and walking movement states are intercepted for power spectral density analysis to obtain the power spectral density diagram at the bottom. It can be seen that in the patient's drug-off state, after changing from the rest state to the walking movement, the energy in the high-beta frequency band (20 - 35 Hz) is significantly suppressed, and the peak frequency of the pathological low-beta energy shifts from 13 Hz to 14 Hz to the right. In addition, the low-frequency energy in the range of 2 - 10 Hz also increases significantly.

[0036] Another example is Parkinson's disease patient B. Figure 4 Figure 5 is the time-frequency diagram and power spectral density of the local field potential signals of the left STN of this patient during the alternation between rest and movement. The top is the time-frequency diagram, and the signals of the marked rest and walking movement states are intercepted respectively for power spectral density analysis to obtain the power spectral density diagram at the bottom. It can be seen that in the patient's drug-off state, after changing from the rest state to the walking movement, the energy in the high-beta frequency band (24 - 35 Hz) is significantly suppressed, and the peak frequency of the pathological low-beta energy shifts from 18 Hz to 20 Hz.

[0037] In summary, dopaminergic drugs can significantly inhibit the energy in the low-beta frequency band in the STN, while walking movements can significantly inhibit the energy in the high-beta frequency band. Therefore, the ratio of the low-beta and high-beta energies in the STN is defined as the LHR, and the change in this value is used as the feedback control signal for closed-loop DBS. Based on accurately identifying the different medication and movement states of the patient, appropriate stimulation is given.

[0038] As Figure 7 shown, a closed-loop neuromodulation system based on the medication and movement states of Parkinson's disease patients includes: A parameter setting module for determining the low-beta frequency band, high-beta frequency band, upper threshold, and lower threshold; among them, the upper threshold is used to distinguish the movement state, and the lower threshold is used to distinguish the drug state; The signal acquisition module is used to collect the local field potential signals of the STN of Parkinson's disease patients in real time, and input them into the feature calculation module after signal preprocessing; The feature calculation module is used to calculate the energy of the low-beta frequency band several times per second through short-time Fourier transform and the energy of the high-beta frequency band , and calculate the energy of the low-beta frequency band once every several seconds and the energy of the high-beta frequency band and the average value of the ratio ; The determination and output module is used to compare the average value obtained by the feature calculation module with the upper threshold and the lower threshold; if it is greater than the upper threshold, it is judged that the patient is in the drug-off - movement state, and at this time, a high-intensity stimulus is output; if it is greater than the lower threshold and less than the upper threshold, it is judged that the patient is in the drug-off - resting state, and at this time, a medium-intensity stimulus is output; if it is less than the lower threshold, it is judged that the patient is in the drug-on state, and at this time, a low-intensity stimulus is output.

[0039] Specifically, for the closed-loop neuromodulation system of the present invention, the specific algorithm process is as follows: S1. In the three states of drug "off" (failure)-resting, drug "off" (failure)-movement, and drug "on" (effective), several segments of local field potential signals of the bilateral STN of Parkinson's disease patients are collected as baselines respectively.

[0040] S2. Calculate the average power spectral density of each of the above states offline, Figure 5 which are the power spectral densities of the local field potential signals of the left STN of Parkinson's disease patients A and B in the drug "off"-resting, drug "off"-movement, and drug "on" states respectively. By comparing the power spectral densities of the drug "off" and "on" states, the low-beta frequency band significantly inhibited by the drug is selected (11 - 17 Hz for patient A on the left, 14 - 22 Hz for patient B on the right); by comparing the power spectral densities of the resting and movement states, the high-beta frequency band significantly inhibited by movement is selected (18 - 34 Hz for patient A on the left, 24 - 34 Hz for patient B on the right).

[0041] S3. Use the ratio LHR of the low-beta and high-beta frequency band energies as the electroencephalogram marker for identifying the drug and movement states of Parkinson's disease patients. Taking Parkinson's disease patient A as an example, according to the LHR baseline values corresponding to its three states respectively, the upper threshold TH_h for distinguishing the movement state is set to 5.0, and the lower threshold TH_l for distinguishing the drug state is set to 1.0.

[0042] S4. Real-time collect the local field potential signals of the STN of Parkinson's disease patients at a sampling rate of 256 Hz, and perform signal preprocessing with a 1 - 100 Hz band-pass filter simultaneously. Figure 6 The top is the time-frequency diagram of the left STN-LFP collected in real time from patient A, which includes the process of the drug taking effect on the patient and the switching process between the resting state / walking movement. The time period corresponding to the walking movement is marked in blue, the time period of the drug taking effect is marked in orange, and the remaining unmarked time period is the resting state before the drug takes effect. Taking this section of the signal as an example, further illustrate the detection of the change in the ratio LHR of low-high beta energy and the corresponding closed-loop stimulation intensity control.

[0043] S5. Through short-time Fourier transform, calculate the low-beta band energy 10 times per second (set the sliding window width to 1 second and the coverage rate to 90%) and high-beta band energy , and the calculation formula is: ; ; where and are the ranges of the low-beta frequency band, and are the ranges of the high-beta frequency band, is the signal, is the total length of the signal, is the window width, is the index of the window, is the window shift, is the window function, represents the index of the frequency component, represents the index of the signal point, represents the unit of the complex number. Divide the low-beta energy by the high-beta energy to obtain the LHR value, and its change over time is as shown by the Figure 6 black curve in the middle.

[0044] S6. Calculate the average value of the ratio LHR of the high-low beta band energy within 10 seconds, and the calculation formula is: ; where is the number of windows. The change in the average value of LHR is as shown by the Figure 6 blue line segment in the middle.

[0045] S7. Compare the average LHR value with the upper and lower thresholds: If LHR > TH_h, it indicates that the patient is in the "off"-medication and movement state. At this time, a higher-intensity stimulation (2.4 mA) is output to relieve the movement symptoms to the greatest extent; if TH_h > LHR > TH_l, it indicates that the patient is in the "off"-medication and resting state. At this time, a moderate daily programmed intensity stimulation (2.2 mA) is output to meet the need for basic symptom relief; if LHR < TH_l, it indicates that the patient is in the "on"-medication state. At this time, a lower-intensity stimulation (2.0 mA) is output to avoid over-stimulation symptoms such as dyskinesia and dizziness. Specifically, as shown in Figure 6 the current change curve at the bottom.

[0046] S8. Analyze the operation results of the personalized aDBS algorithm based on the medication and movement states, that is Figure 6 the time-frequency diagram of the local field potential signal of the left STN of Parkinson's disease patient A, the change of the low-high beta energy ratio LHR value, and the change of the closed-loop stimulation amplitude controlled thereby. It can be seen that before the drug effect takes effect, the LHR value is always greater than the lower threshold, and the LHR value during the vast majority of walking movements exceeds the upper threshold. At this time, the output current is adjusted to the maximum gear of 2.4 mA; while the LHR value during rest often lies between the upper and lower thresholds. At this time, the output current is adjusted to the intermediate gear of 2.2 mA for daily programming; after the drug effect takes effect, the LHR value is basically lower than the lower threshold. At this time, the output current is adjusted to the lowest gear of 2.0 mA.

[0047] Therefore, the present invention can distinguish the electroencephalogram signal characteristics of Parkinson's disease patients in different medication and movement states, use the low-high beta energy ratio LHR as the feedback control signal for closed-loop DBS, and achieve adaptive on-demand stimulation in the three states of the patient's "off"-medication and movement, "off"-medication and rest, and "on"-medication.

[0048] The above-described embodiments have detailed the technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modification, supplement, and equivalent replacement made within the scope of the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A closed-loop neural regulation system based on Parkinson's disease patients' medication and movement status, characterized in that: include: A parameter setting module, used to determine a low beta frequency band, a high beta frequency band, an upper threshold and a lower threshold; wherein the upper threshold is used to distinguish a motion state, and the lower threshold is used to distinguish a drug state; A signal acquisition module, used to collect the local field potential signal of STN of Parkinson's disease patients in real time, and input the signal into the feature calculation module after signal preprocessing; Feature calculation module, used to calculate the low beta band energy several times per second through short-time Fourier transform and high beta band energy , and calculates the low beta band energy every several seconds and high beta band energy The average value of the ratio ; The judgment output module is used to calculate the average value obtained by the feature calculation module Compare with the upper and lower thresholds; if If the value is greater than the upper threshold, the patient is judged to be in a drug failure-exercise state, and a high-intensity stimulus is output; If the value is greater than the lower threshold and less than the upper threshold, the patient is judged to be in a drug failure-resting state. At this time, a medium-intensity stimulus is output; if If the value is less than the lower threshold, it is judged that the patient is in a drug-effective state, and at this time, a low-intensity stimulation is output.

2. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement state according to claim 1, characterized in that: The specific working process of the parameter setting module is: In the three states of drug failure-rest, drug failure-exercise, and drug effectiveness, several segments of local field potential signals of bilateral STN of Parkinson's disease patients were collected as baselines; Compare the power spectral density between the drug-ineffective and drug-effective states and select the low beta frequency band of drug inhibition; compare the power spectral density between the resting and exercise states and select the high beta frequency band of exercise inhibition; The upper threshold for distinguishing the exercise state and the lower threshold for distinguishing the drug state were set according to the baseline data.

3. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement status according to claim 2, characterized in that: In the parameter setting module, the low beta frequency band, high beta frequency band, upper threshold and lower threshold are determined individually according to different patients and different sides of the brain.

4. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement status according to claim 1, characterized in that: The sampling rate of the signal acquisition module is 256 Hz, and the signal preprocessing of 1-100 Hz bandpass filtering is performed simultaneously.

5. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement status according to claim 1, characterized in that: In the feature calculation module, the low beta band energy The calculation formula is: ； In the formula, Indicates The low beta band energy corresponding to the window, and is the range of the low beta band, For signal, is the total length of the signal, is the window width, is the window index, To shift the window, is the window function, represents the index of the frequency component, represents the index of the signal point, A unit that represents a complex number.

6. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement state according to claim 5, characterized in that: In the feature calculation module, the high beta band energy The calculation formula is: ； In the formula, For the The high beta band energy corresponding to the window, and It is the range of high beta frequency band.

7. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement state according to claim 6, characterized in that: In the feature calculation module, calculate the low beta band energy and high beta band energy The average value of the ratio , the formula is: ； In the formula, is the window index, is the number of windows.

8. The closed-loop neural regulation system based on Parkinson's disease patient's drug and movement state according to claim 7, characterized in that: In the feature calculation module, the low beta band energy is calculated 10 times per second and high beta band energy ; Calculate low beta band energy every 10 seconds and high beta band energy The average value of the ratio .

Images