Stimulation parameter setting device for dyskinesia and implantable closed-loop stimulation system

Abstract

The invention provides a stimulation parameter setting device for dyskinesia and an implantable closed-loop stimulation system, and belongs to the technical field of medical instruments. The device comprises a processor, wherein the processor is used for determining a variable-frequency mode or a constant-frequency mode according to the accelerated speed of a patient; when the frequency conversion mode is determined to be adopted, outputting a stimulation signal according to a preset frequency conversion stimulation parameter; and when the constant frequency mode is determined to be adopted, extracting an individual specific biomarker according to the physiological signal, obtaining a target threshold value, and adjusting a stimulation parameter according to a relationship between the individual specific biomarker and the target threshold value. The system comprises a processor and a controller. The controller is used for outputting stimulation signals according to the stimulation parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a stimulation parameter setting device for movement disorders and an implantable closed-loop stimulation system. Background Art

[0002] Implantable neurostimulation systems include Deep Brain Stimulation (DBS), which has significant therapeutic effects on a variety of intractable neurological diseases. The pulse generator and electrodes are implanted in the body, and the pulse generator transmits electrical pulses to specific areas of the brain through the electrodes to control the symptoms of the disease. The external device can communicate with the pulse generator and adjust the stimulation parameters of the pulse generator to achieve different stimulation effects. For example, the stimulation position can be changed by adjusting the positive and negative poles of the contact point, and the amplitude, pulse width, and frequency can be modified to change the range of stimulation. The doctor can perform programming based on experience and the patient's response to determine a set of fixed parameters for continuous stimulation.

[0003] Taking Parkinson's patients as an example, the severity of their movement disorders fluctuates. Patients with different degrees of movement disorders have different stimulation needs and intensities. Moreover, continuous fixed-frequency / amplitude stimulation will affect normal neural circuits to a certain extent, and may have a certain impact on patients' cognition, behavior, psychology, etc. Summary of the invention

[0004] In view of this, the present invention provides a stimulation parameter setting device for movement disorders, comprising a processor;

[0005] The processor is used to determine the variable frequency mode or the constant frequency mode according to the acceleration of the patient; when it is determined to adopt the variable frequency mode, the stimulation parameters are adjusted to the preset variable frequency stimulation parameters; when it is determined to adopt the constant frequency mode, individual-specific biomarkers are extracted according to the physiological signals, and the target threshold is obtained, and the stimulation parameters are adjusted according to the relationship between the individual-specific biomarkers and the target threshold.

[0006] Optionally, determining the variable frequency mode or the constant frequency mode according to the acceleration of the patient includes:

[0007] Obtain the patient's three-axis acceleration;

[0008] Performing time domain feature extraction on the three-axis acceleration;

[0009] The pre-trained classifier is used to classify the extracted time domain features into postures, including body position switching, body position static, and walking state.

[0010] If the patient is in a state of switching body positions or walking, the variable frequency mode is determined;

[0011] If the patient remains still in the posture for a preset time, the constant frequency mode is adopted.

[0012] Optionally, obtain a target threshold, including:

[0013] Obtaining pre-built individual-specific biomarker time series models and time series model parameters with optimal specificity;

[0014] Using a time series model and according to time series model parameters, select the mean value of the individual specific biomarker under a predefined sliding time as the standard value at the current moment;

[0015] The current standard value is corrected according to the predefined upper and lower reference thresholds to obtain the current target threshold.

[0016] Optionally, adjusting stimulation parameters according to the relationship between the individual-specific biomarker and the target threshold comprises:

[0017] If the individual-specific biomarker is greater than the upper threshold of the target threshold at the current moment, it is determined to increase the stimulation intensity;

[0018] If the individual-specific biomarker is less than the lower threshold of the target threshold at the current moment, it is determined to reduce the stimulation intensity;

[0019] If the individual-specific biomarker is between the upper threshold and the lower threshold of the target threshold at the current moment, the current stimulation intensity is maintained.

[0020] Optionally, obtaining target thresholds and stimulation parameters includes:

[0021] Target thresholds and stimulation parameters are determined based on clinical assessment information, including a clinical assessment scale and side effect assessment.

[0022] Optionally, extracting individual-specific biomarkers based on physiological signals includes:

[0023] The local field potential (LFP) of the patient was collected when the patient was receiving the drug and when the patient was not receiving the drug.

[0024] The collected local field potential LFPs without drug administration and the local field potential LFPs with drug administration were divided into time segments, and the average power spectrum density of each segment of local field potential LFP was calculated;

[0025] Calculating a characteristic power spectral density difference according to the average power spectral density;

[0026] fitting a non-periodic component according to the characteristic power spectral density difference;

[0027] Removing non-periodic components from the characteristic power spectrum density difference, and fitting the remaining periodic components to obtain a fitting result;

[0028] Individual-specific biomarkers are obtained according to the fitting results.

[0029] Optionally, calculating a characteristic power spectral density difference according to the average power spectral density includes:

[0030] The average power spectral density of all segments without and with the drug was calculated, and the difference between the average values ​​in the two states was obtained to obtain the characteristic power spectral density difference.

[0031] Optionally, fitting a non-periodic component according to the characteristic power spectrum density difference includes:

[0032] Using a 1 / f function to fit the non-periodic component in the characteristic power spectrum density difference;

[0033] removing non-periodic components in the characteristic power spectrum density difference;

[0034] The characteristic power spectrum density difference of the non-periodic component is removed by Gaussian function fitting to obtain the periodic component;

[0035] The periodic component is subtracted from the characteristic power spectrum density difference, and the characteristic power spectrum density difference of the periodic component is re-fitted using the 1 / f function to obtain a second non-periodic component. The non-periodic component extraction operation is repeated until the non-periodic component fitting error is less than the expected error value, thereby obtaining the target non-periodic component.

[0036] Optionally, obtaining individual-specific biomarkers according to the fitting results includes:

[0037] The preset confidence interval of the peak value of the fitting result is taken to obtain the individual specific biomarker range.

[0038] The present invention also provides an implantable closed-loop stimulation system, comprising the stimulation parameter setting device and a controller;

[0039] The controller is used to output a stimulation signal according to the stimulation parameters.

[0040] The stimulation parameter setting device provided in this application can automatically select variable frequency or constant frequency mode to set the stimulation parameters according to the patient's acceleration. In variable frequency mode, the device uses preset variable frequency stimulation parameters to adapt to gait freezing and balance dysfunction during patient posture changes; while in constant frequency mode, the device extracts individual-specific biomarkers by analyzing physiological signals and compares them with the target threshold, thereby fine-tuning the stimulation parameters to match the individual's current movement disorder severity. This personalized stimulation parameter setting method combined with movement disorder severity can enhance the treatment effect.

[0041] The present invention provides an implantable closed-loop stimulation system, comprising a stimulation parameter setting device and a controller. The controller outputs a stimulation signal according to the stimulation parameters (stimulation intensity) set by the stimulation parameter setting device for movement disorders, thereby achieving precise stimulation control. Compared with the existing open-loop constant-frequency and constant-amplitude stimulation, the controller reduces the impact on the patient's normal neural circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 is a flow chart of a processor in a device for setting stimulation parameters for movement disorders in an embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of a flow chart of a processor determining a variable frequency mode or a constant frequency mode in a stimulation parameter setting device for movement disorders in an embodiment of the present invention;

[0045] Figure 3 The figure is a flow chart of a processor acquiring a target threshold value in a stimulation parameter setting device for movement disorders in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0048] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] An embodiment of the present invention provides an implantable closed-loop stimulation system, including a stimulation parameter setting device, a controller, a three-axis accelerometer and electrodes. The stimulation parameter setting device and the three-axis accelerometer can be fully integrated into an implantable pulse generator, or partially set in the implantable pulse generator and partially set in an external control device.

[0051] The three-axis accelerometer is used to collect acceleration information, the electrodes are used to collect local field potential (LFP), and the controller is used to output stimulation signals according to stimulation parameters.

[0052] The stimulation parameter setting device for movement disorders provided by the embodiment of the present invention comprises a processor, Figure 1 As shown, the processor is used to perform operations including the following contents:

[0053] S1, determine the variable frequency mode or constant frequency mode according to the patient's acceleration, the acceleration can be collected by a pulse generator implanted in the patient's body, or by other devices carried or worn by the patient. When it is determined to adopt the variable frequency mode, the processor executes step S2; when it is determined to adopt the constant frequency mode, the processor executes step S3;

[0054] S2, adjusting the stimulation parameters to the preset variable frequency stimulation parameters;

[0055] S3, extracting individual-specific biomarkers according to the physiological signals, obtaining the target threshold, and adjusting the stimulation parameters according to the relationship between the individual-specific biomarkers and the target threshold.

[0056] The stimulation parameter setting device provided in this embodiment can automatically select variable frequency or constant frequency mode to set the stimulation parameters according to the patient's acceleration. In variable frequency mode, the device uses preset variable frequency stimulation parameters to adapt to gait freezing and balance dysfunction during patient posture changes; while in constant frequency mode, the device extracts individual-specific biomarkers by analyzing physiological signals and compares them with the target threshold, thereby fine-tuning the stimulation parameters to match the individual's current movement disorder severity. This personalized stimulation parameter setting method combined with movement disorder severity can enhance the treatment effect.

[0057] like Figure 2 As shown, the processor determines the variable frequency mode or the constant frequency mode according to the acceleration of the patient in step S1, specifically including:

[0058] S11, obtaining the patient's three-axis acceleration. Specifically, the acceleration data can be collected according to a time window of any length between 20s and 60s, preferably 40s. In order to make the data more accurate, the three-axis accelerometer can be calibrated using the patient's different historical posture acceleration characteristics, such as the historical posture acceleration characteristics of upright, left side lying, right side lying, and supine, to ensure the accuracy of the three-axis accelerometer.

[0059] S12, extracting time domain features of the three-axis acceleration, including but not limited to calculating the statistical features, dynamic features and other relevant time domain characteristic parameters of the acceleration signal, for describing the changing trend and distribution characteristics of the acceleration signal. Specifically, the extracted time domain features may include but are not limited to: the mean, variance, standard deviation, extreme value difference, peak value, median, skewness, kurtosis, zero crossing rate and other statistical features describing the dynamic characteristics of the signal.

[0060] S13, using a pre-trained classifier to perform posture classification and discrimination on the extracted time domain features, including body position state switching, body position state stillness, and walking state.

[0061] The body position switching includes the switching between lying / sitting and standing, and the body position static includes the static of lying / sitting / standing. The pre-trained classifier is trained based on the patient's historical posture data of each category. The pre-trained classifier is used to extract the time domain features to determine whether the patient is in the body position switching or walking state. If the patient is in the body position switching or walking state, the processor executes step S14; if the patient is in the static body position, the processor executes step S15;

[0062] S14, determining to use the variable frequency mode. If the patient switches between lying and standing positions, between sitting and standing positions, or is in a walking state, the variable frequency mode is determined to be used, and a stimulation signal is subsequently output according to the preset variable frequency stimulation parameters.

[0063] S15, timing the static body position state, and when the preset time is reached, determining to adopt the constant frequency mode.

[0064] The preset duration t can be set at 1s<t<120s. If the patient is still in a lying / sitting / standing position and remains still for more than the preset duration t, it is determined to be a constant frequency mode. Subsequently, individual-specific biomarkers will be extracted based on physiological signals, and target thresholds and stimulation parameters will be obtained. The stimulation parameters will be adjusted based on the relationship between the individual-specific biomarkers and the target thresholds, and a stimulation signal will be output. In this embodiment, acceleration data within the first 40s is obtained, and the interval between two consecutive posture classification judgments is greater than or equal to 0.1s.

[0065] This embodiment uses the processor to identify the body position switching and walking state, and respectively switches between constant frequency and variable frequency stimulation modes for the balance difficulties and frozen gait caused by the expected posture adjustment disorder, so as to take into account the limb and axial symptoms at all times. Expected posture: By monitoring the body position switching and adjusting the stimulation, it has a certain prediction of the posture, which can alleviate the balance difficulties and frozen gait to a certain extent.

[0066] In one embodiment, the processor extracts individual-specific biomarkers according to the physiological signals in step S3, specifically including:

[0067] S311, collecting the local field potential LFP of the patient under stimulation when the patient is receiving the drug and when the patient is not receiving the drug.

[0068] Receiving medication means that the patient is taking related medications, usually drugs used to treat neurological diseases, such as Parkinson's disease-related drugs. The LFP data of the patient when taking medication and not taking medication and the deep brain stimulator is turned on, that is, the patient is receiving electrical stimulation treatment (not limited to the STN nucleus, other nuclei, cortex, etc. are considered according to needs).

[0069] S312, the collected local field potential LFP of the non-drug recipient and the local field potential LFP of the drug recipient are respectively divided into time segments, and the average power spectral density of each segment of the local field potential LFP is calculated. The average power spectral density (PSD) is in dB (10log10 of the result).

[0070] The segment length can be set according to the needs. For LFP data of non-drug and drug-treated subjects, the same segment length and segmentation method should be used to ensure consistency and comparability of analysis.

[0071] S313, calculating a characteristic power spectrum density difference according to the average power spectrum density.

[0072] S314, fitting the non-periodic component according to the characteristic power spectrum density difference.

[0073] S315, removing the non-periodic components in the characteristic power spectrum density difference, and fitting the remaining periodic components to obtain a fitting result.

[0074] The non-periodic component refers to the part of the time series that does not follow a fixed periodic law, and the periodic component refers to the part of the time series that follows a fixed periodic law.

[0075] S316, obtaining individual-specific biomarkers according to the fitting results.

[0076] In this embodiment, the processor collects the local field potential (LFP) data of patients receiving deep brain stimulation treatment with and without drugs, and performs time segmentation and average power spectrum density calculation, and further obtains individual-specific biomarkers by calculating the characteristic power spectrum density difference, fitting non-periodic components, removing non-periodic components and fitting the remaining periodic components. This method not only improves the accuracy of biomarker extraction, but also can more deeply reflect the effects of drugs on neural activity and the differences in individual responses to treatment, providing strong support for the precise treatment and personalized medicine of neurological diseases.

[0077] Further, the processor calculates the characteristic power spectrum density difference according to the average power spectrum density in step S313, specifically including:

[0078] The average power spectral density of all segments without and with the drug was calculated, and the difference between the average values ​​in the two states was obtained to obtain the characteristic power spectral density difference.

[0079] In this embodiment, the processor can more accurately evaluate the modulation effect of drugs on neural activity and the physiological response of individuals after drug treatment by accurately calculating the characteristic power spectral density difference, thereby improving the accuracy of biomarker extraction.

[0080] Further, the processor fits the non-periodic component according to the characteristic power spectrum density difference in step S314, specifically including:

[0081] S3141, fitting the non-periodic components in the characteristic power spectral density difference using 1 / f function;

[0082] S3142, remove the non-periodic components in the characteristic power spectrum density difference;

[0083] S3143, using Gaussian function fitting to remove the characteristic power spectrum density difference of non-periodic components to obtain periodic components;

[0084] S3144, subtract the periodic component from the characteristic power spectrum density difference, re-execute the operation of step S3141, use the 1 / f function to fit the characteristic power spectrum density difference subtracting the periodic component, obtain the second non-periodic component, repeat the above non-periodic component extraction operation until the non-periodic component fitting error is less than the expected error value, and obtain the target non-periodic component. The above steps are usually repeated about 20 times.

[0085] In this embodiment, the processor uses the 1 / f function and the Gaussian function for fitting iteratively, gradually and accurately extracts the non-periodic components in the characteristic power spectrum density difference, and improves the accuracy of the non-periodic component fitting. Further, in the process of extracting individual-specific biomarkers, the interference of non-periodic components in the characteristic power spectrum density difference can be more effectively removed, so as to focus on the periodic components more accurately. The accurate extraction of periodic components plays a vital role in evaluating the effect of drug treatment and revealing physiological changes. Therefore, it is more accurate to extract individual-specific biomarkers using periodic components.

[0086] Furthermore, the processor obtains individual-specific biomarkers according to the fitting results in step S316, including:

[0087] The preset confidence interval of the peak value of the fitting result is taken to obtain the individual specific biomarker range. The preset confidence interval can be 20% of the peak value, and finally obtain, for example, the β range, the γ range, etc.

[0088] In this embodiment, the processor can more accurately determine the range of individual-specific biomarkers by taking the preset confidence interval of the peak value of the fitting result, thereby avoiding errors caused by data fluctuations or noise interference.

[0089] like Figure 3 As shown, the processor obtains the target threshold in step S3, which specifically includes:

[0090] S321, obtaining a pre-constructed time series model of individual-specific biomarkers and time series model parameters with optimal specificity.

[0091] Specifically, the specific process of pre-constructing the time series model of individual-specific biomarkers is as follows: comprehensively considering the quality of the collected signals and the patient's clinical complaints, selecting the side of the feedback signal source (left brain, right brain or both sides, and selecting unilateral stimulation or bilateral stimulation according to the source measurement), collecting the historical local field potential LFP data of the patient when the brain is stimulated without receiving drugs (DBS ON MED OFF) and the historical local field potential LFP data of the patient when the brain is stimulated with drugs (DBS ON MED ON) (not limited to the STN nucleus, other nuclei, cortex, etc. are considered according to needs);

[0092] The collected physiological signals of the subjects who have not received the drug and the physiological signals of the subjects who have received the drug are collected in segments or at different time periods, and the average power spectral density (PSD) of each segment of signal data is calculated in dB (10log10 of the result);

[0093] Calculating the average value of the average power spectral density (PSD) of all segment signal data of the physiological signal that has not received the drug, and calculating the average value of the average power spectral density (PSD) of all segment signal data of the physiological signal that has received the drug;

[0094] Subtract the two average values ​​to obtain the characteristic power spectral density difference;

[0095] Using a 1 / f function to fit the non-periodic component of the characteristic power spectrum density difference, and removing the non-periodic component, using a Gaussian function to fit and remove the periodic component of the non-periodic component, removing the periodic component from the characteristic power spectrum density difference, and obtaining a non-periodic component fitting error, comparing the non-periodic component fitting error with a preset non-periodic component fitting error, if the non-periodic component fitting error is higher than the non-periodic component fitting error, continuing the fitting and removal steps until the non-periodic component fitting error is less than the preset non-periodic component fitting error;

[0096] The non-periodic component fitting error in the characteristic power spectrum density difference is removed, and the remaining periodic component is fitted using a Gaussian function. The 20% of the peak value of the Gaussian fitting result is taken as the confidence interval to calculate the range of historical individual-specific biomarkers. The data is used for time series model training.

[0097] In time series analysis, the selection of optimal model parameters (also called optimal time series model parameters) depends on the specific model, such as autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The purpose is to make the model fit the data best so as to make accurate predictions and explanations.

[0098] S322, using the time series model according to the time series model parameters, selecting the mean value of the individual specific biomarker under the predefined sliding time as the standard value at the current moment.

[0099] Specifically, the individual-specific biomarkers currently extracted from physiological signals are input into the time series model to obtain the ARIMA prediction results, and the mean of the ARIMA prediction results under the predetermined sliding time is obtained by taking the mean or low-pass filtering as the standard value at the current moment. The sliding time can be set to 1-120 minutes, preferably 15 minutes.

[0100] S323, correcting the current standard value according to the predefined upper and lower reference thresholds to obtain the current target threshold.

[0101] The individual-specific biomarkers are compared with the target threshold at the current moment, and the stimulation parameters are determined based on the comparison results.

[0102] Furthermore, the processor adjusts the stimulation parameters according to the relationship between the individual-specific biomarker and the target threshold in step S3, that is, the individual-specific biomarker is compared with the target threshold at the current moment, and the stimulation parameters are determined according to the comparison result, specifically including:

[0103] If the individual specific biomarker is greater than the upper threshold of the target threshold at the current moment, it is determined to increase the stimulation intensity. Taking the frequency of the β band signal as an example, if β is greater than the upper threshold of the target at the current moment, the stimulation intensity δAmp is increased. δAmp is the single-step adjustment step length, which can be set manually or determined by the algorithm.

[0104] If the individual specific biomarker is less than the lower threshold of the target threshold at the current moment, it is determined to reduce the stimulation intensity. If β is less than the lower threshold of the target at the current moment, the stimulation intensity δAmp is reduced.

[0105] If the individual-specific biomarker is between the upper threshold and the lower threshold of the target threshold at the current moment, the current stimulation intensity is maintained.

[0106] The target threshold of this embodiment is constantly changing. The sliding threshold controlled release mode can effectively reduce feedback stimulation hysteresis and biomarker fluctuations introduced by stimulation compared to the existing dual-threshold and single-threshold feedback control modes, while having a better effect in resisting system noise.

[0107] In another embodiment, the processor in step S3 obtains the target threshold and stimulation parameters, and may also determine the target threshold and stimulation parameters based on clinical evaluation information, where the clinical evaluation information includes a clinical evaluation scale and a side effect evaluation.

[0108] Specifically, when the implantable pulse generator is turned on for the first time, the parameters of each contact will be traversed according to the conventional method to obtain the therapeutic window range of each contact under the conventional frequency and pulse width, and the optimal contact parameters will be selected as the conventional treatment parameters; the selected target thresholds (upper and lower thresholds) must be within the therapeutic window range: when the patient's drug efficacy is the least and the drug efficacy is the most significant, the upper and lower thresholds of stimulation are tested, and the upper and lower thresholds of stimulation are determined according to the patient's MDS UPDRS III score and subjective and objective side effect evaluation to ensure: i. The severity of the patient's motor symptoms is similar when the drug efficacy is the least - the upper threshold of stimulation, and the drug efficacy is the greatest - the lower threshold of stimulation, and the MDS UPDRS III score difference is <10%; ii. The patient can tolerate the drug efficacy at the least - the lower threshold of stimulation, and the drug efficacy is the most significant - the upper threshold of stimulation, without severe akinesia, dyskinesia, etc., to ensure the safety of closed-loop stimulation.

[0109] In another embodiment, the processor adjusts the stimulation parameters in step S3 according to the relationship between the individual-specific biomarker and the target threshold, including:

[0110] If the individual-specific biomarker is greater than the upper threshold of the target threshold at the current moment, it is determined that the stimulation intensity is increased;

[0111] If the individual-specific biomarker is less than the lower threshold of the target threshold at the current moment, it is determined to reduce the intensity of stimulation;

[0112] If the individual-specific biomarker is between the upper and lower thresholds of the target threshold at the current moment, the current stimulation intensity is maintained.

[0113] The processor compares the individual-specific biomarker range obtained in step S316 with the target threshold, and determines the stimulation intensity according to the comparison result.

[0114] The processor in this embodiment dynamically adjusts the stimulation parameters according to the relationship between the patient's individual specific biomarkers and the target threshold, thereby achieving personalized customization of the treatment plan and improving the effectiveness and safety for the patient.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. 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 implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A device for setting stimulation parameters for movement disorders, characterized in that: Including processors; The processor is used to determine the variable frequency mode or the constant frequency mode according to the acceleration of the patient; when it is determined to adopt the variable frequency mode, the stimulation parameters are adjusted to the preset variable frequency stimulation parameters; when it is determined to adopt the constant frequency mode, individual-specific biomarkers are extracted according to the physiological signals, and the target threshold is obtained, and the stimulation parameters are adjusted according to the relationship between the individual-specific biomarkers and the target threshold.

2. The device according to claim 1, characterized in that Determine variable frequency mode or constant frequency mode according to the patient's acceleration, including: Obtain the patient's three-axis acceleration; Performing time domain feature extraction on the three-axis acceleration; The pre-trained classifier is used to classify the extracted time domain features into postures, including body position switching, body position static, and walking state. If the patient is in a state of switching body positions or walking, the variable frequency mode is determined; If the patient remains still in the posture for a preset time, the constant frequency mode is adopted.

3. The device according to claim 1, characterized in that Get target thresholds, including: Obtaining pre-built individual-specific biomarker time series models and time series model parameters with optimal specificity; Using a time series model and according to time series model parameters, select the mean value of the individual specific biomarker under a predefined sliding time as the standard value at the current moment; The current standard value is corrected according to the predefined upper and lower reference thresholds to obtain the current target threshold.

4. The device according to claim 3, characterized in that Adjusting stimulation parameters according to the relationship between the individual-specific biomarker and the target threshold comprises: If the individual-specific biomarker is greater than the upper threshold of the target threshold at the current moment, it is determined to increase the stimulation intensity; If the individual-specific biomarker is less than the lower threshold of the target threshold at the current moment, it is determined to reduce the stimulation intensity; If the individual-specific biomarker is between the upper threshold and the lower threshold of the target threshold at the current moment, the current stimulation intensity is maintained.

5. The device according to claim 1, characterized in that Obtain target thresholds and stimulation parameters, including: Target thresholds and stimulation parameters are determined based on clinical assessment information, including a clinical assessment scale and side effect assessment.

6. The device according to claim 1, characterized in that Extract individual-specific biomarkers based on physiological signals, including: The local field potential (LFP) of the patient was collected when the patient was receiving the drug and when the patient was not receiving the drug. The collected local field potential LFPs without drug administration and the local field potential LFPs with drug administration were divided into time segments, and the average power spectrum density of each segment of local field potential LFP was calculated; Calculating a characteristic power spectral density difference according to the average power spectral density; fitting a non-periodic component according to the characteristic power spectral density difference; Removing non-periodic components from the characteristic power spectrum density difference, and fitting the remaining periodic components to obtain a fitting result; Individual-specific biomarkers are obtained according to the fitting results.

7. The device according to claim 6, characterized in that Calculating a characteristic power spectrum density difference according to the average power spectrum density includes: The average power spectral density of all segments without and with the drug was calculated, and the difference between the average values ​​in the two states was obtained to obtain the characteristic power spectral density difference.

8. The device according to claim 6, characterized in that Fitting the non-periodic component according to the characteristic power spectrum density difference includes: Using a 1 / f function to fit the non-periodic component in the characteristic power spectrum density difference; removing non-periodic components in the characteristic power spectrum density difference; The characteristic power spectrum density difference of the non-periodic component is removed by Gaussian function fitting to obtain the periodic component; The periodic component is subtracted from the characteristic power spectrum density difference, and the characteristic power spectrum density difference of the periodic component is re-fitted using the 1 / f function to obtain a second non-periodic component. The non-periodic component extraction operation is repeated until the non-periodic component fitting error is less than the expected error value, thereby obtaining the target non-periodic component.

9. The device according to claim 6, characterized in that Obtaining individual-specific biomarkers according to the fitting results includes: The preset confidence interval of the peak value of the fitting result is taken to obtain the individual specific biomarker range.

10. An implantable closed-loop stimulation system, characterized in that: It comprises a stimulation parameter setting device and a controller as described in any one of claims 1 to 9; The controller is used to output a stimulation signal according to the stimulation parameters.

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