Stimulation parameter setting apparatus for a movement disorder and implantable closed-loop stimulation system
By using an implantable closed-loop stimulation system, combined with acceleration and local field potential signal analysis, and dynamically adjusting stimulation parameters, the inadequacy of existing technologies for treating movement disorders in Parkinson's patients has been solved, achieving personalized and precise treatment results.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2025-12-26
- Publication Date
- 2026-07-09
Smart Images
Figure CN2025146301_09072026_PF_FP_ABST
Abstract
Description
Stimulation parameter setting device and implantable closed-loop stimulation system for movement disorders Technical Field
[0001] This invention relates to the field of medical device technology, specifically to a device for setting stimulation parameters for movement disorders and an implantable closed-loop stimulation system. Background Technology
[0002] Implantable neurostimulation systems, including deep brain stimulation (DBS), have shown significant efficacy in treating various refractory neurological diseases. A pulse generator and electrodes are implanted in the body. The pulse generator delivers electrical pulses to specific areas of the brain via the electrodes to control disease symptoms. External devices can communicate with the pulse generator, adjusting its stimulation parameters to achieve different stimulation effects. For example, the stimulation location can be changed by adjusting the polarity of the contact points, and the range of influence can be altered by modifying the amplitude, pulse width, and frequency. Physicians program these parameters based on experience and the patient's response, establishing a fixed set for continuous stimulation.
[0003] Taking Parkinson's patients as an example, the severity of their motor impairment fluctuates. Patients with different degrees of motor impairment will have different needs and intensities for stimulation. Moreover, continuous stimulation at a fixed frequency / amplitude can affect normal neural circuits to some extent, which may have a certain impact on the patient's cognition, behavior, and psychology. Summary of the Invention
[0004] In view of this, the present invention provides a device for setting stimulation parameters for movement disorders, including a processor;
[0005] The processor is used to determine the variable frequency mode or constant frequency mode based on the patient's acceleration; when the variable frequency mode is determined, the stimulation parameters are adjusted to the preset variable frequency stimulation parameters; when the constant frequency mode is determined, individual-specific biomarkers are extracted based on physiological signals, and a target threshold is obtained, and the stimulation parameters are adjusted based on the relationship between the individual-specific biomarkers and the target threshold.
[0006] Optionally, the variable frequency mode or constant frequency mode is determined based on the patient's acceleration, including:
[0007] Obtain the patient's triaxial acceleration;
[0008] Temporal features were extracted from the triaxial accelerations.
[0009] A pre-trained classifier is used to classify and discriminate the extracted temporal features, including body position switching, static body position, and walking state.
[0010] If the patient is changing positions or walking, then the variable frequency mode will be used.
[0011] If the patient remains stationary in a position for the preset duration, the constant frequency mode will be selected.
[0012] Optionally, obtaining the target threshold includes:
[0013] Obtain pre-constructed time series models of individual-specific biomarkers and the parameters of the time series models with optimal specificity;
[0014] Using a time series model, the mean value of the individual-specific biomarker under a predefined sliding time is selected as the standard value at the current time based on the parameters of the time series model.
[0015] The target threshold at the current time is obtained by correcting the standard value at the current time based on the predefined upper and lower reference thresholds.
[0016] Optionally, adjusting the stimulation parameters based on the relationship between the individual-specific biomarker and the target threshold includes:
[0017] If the individual-specific biomarker is greater than the upper threshold of the target threshold at the current time, then it is determined to increase the stimulus intensity;
[0018] If the individual-specific biomarker is less than the lower threshold of the target threshold at the current time, then it is determined to reduce the stimulation intensity.
[0019] If the individual-specific biomarker is between the upper and lower thresholds of the target threshold at the current time, the current stimulus intensity is maintained.
[0020] Optionally, the target threshold and stimulation parameters are obtained, including:
[0021] The target threshold and stimulation parameters are determined based on clinical assessment information, which includes clinical assessment scales and side effect assessments.
[0022] Optionally, individual-specific biomarkers are extracted based on physiological signals, including:
[0023] Local field potentials (LFPs) were collected from patients under stimulation in both drug-treated and drug-free states.
[0024] The collected local field potentials (LFP) of untreated and drug-treated individuals were segmented over time, and the average power spectral density of each segment of the local field potentials (LFP) was calculated.
[0025] The characteristic power spectral density difference is calculated based on the average power spectral density.
[0026] Fit the aperiodic component based on the characteristic power spectral density difference;
[0027] Remove the non-periodic components from the characteristic power spectral density difference and fit the remaining periodic components to obtain the fitting result;
[0028] Individual-specific biomarkers are obtained based on the fitting results.
[0029] Optionally, calculating the characteristic power spectral density difference based on the average power spectral density includes:
[0030] The average power spectral density of all segments is calculated for both the untreated and treated states, and the difference between the average values in the two states is obtained to obtain the characteristic power spectral density difference.
[0031] Optionally, fitting aperiodic components based on the characteristic power spectral density difference includes:
[0032] The non-periodic components in the characteristic power spectral density difference are fitted using the 1 / f function;
[0033] Remove the non-periodic components from the characteristic power spectral density difference;
[0034] The periodic components are obtained by fitting the characteristic power spectral density difference to remove the non-periodic components using a Gaussian function.
[0035] The periodic component is subtracted from the characteristic power spectral density difference, and the characteristic power spectral density difference after subtracting the periodic component is refitted using the 1 / f function to obtain the second aperiodic component. The aperiodic component extraction operation is repeated until the fitting error of the aperiodic component is less than the expected error value, and the target aperiodic component is obtained.
[0036] Optionally, individual-specific biomarkers are obtained based on the fitting results, including:
[0037] The range of individual-specific biomarkers is obtained by taking the preset confidence interval of the peak value of the fitting result.
[0038] The present invention also provides an implantable closed-loop stimulation system, including 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 between variable frequency and constant frequency modes to set stimulation parameters based on 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 analyzes physiological signals to extract individual-specific biomarkers and compares them with target thresholds, thereby finely adjusting the stimulation parameters to match the individual's current severity of motor impairment. This personalized stimulation parameter setting method, which combines the severity of motor impairment, can enhance treatment effectiveness.
[0041] This invention provides an implantable closed-loop stimulation system, including 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 motor disorders, which can achieve precise stimulation control. Compared with existing open-loop constant frequency and constant amplitude stimulation, it reduces the impact on the patient's normal neural circuits. Attached Figure Description
[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0043] Figure 1 is a flowchart of the processor in the stimulation parameter setting device for movement disorders in an embodiment of the present invention.
[0044] Figure 2 is a schematic diagram of the process by which the processor determines the frequency conversion mode or the constant frequency mode in the stimulation parameter setting device for movement disorders in an embodiment of the present invention.
[0045] Figure 3 is a schematic diagram of the process by which the processor obtains the target threshold in the stimulation parameter setting device for movement disorders in an embodiment of the present invention. Detailed Implementation
[0046] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0047] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0049] Furthermore, 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] This invention provides an implantable closed-loop stimulation system, including a stimulation parameter setting device, a controller, a triaxial accelerometer, and electrodes. The stimulation parameter setting device and the triaxial accelerometer can be fully integrated into the implantable pulse generator, or they can be partially located in the implantable pulse generator and partially located in the external control device.
[0051] The triaxial 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 the stimulation parameters.
[0052] The stimulation parameter setting device for movement disorders provided in this embodiment of the invention includes a processor, as shown in FIG1. The processor is used to perform operations including the following:
[0053] S1: Determine whether to use a variable frequency mode or a constant frequency mode based on the patient's acceleration. Acceleration can be acquired through a pulse generator implanted in the patient's body or through other devices worn or carried by the patient. When a variable frequency mode is determined, the processor executes step S2; when a constant frequency mode is determined, the processor executes step S3.
[0054] S2, adjust the stimulation parameters to the preset variable frequency stimulation parameters;
[0055] S3 extracts individual-specific biomarkers based on physiological signals and obtains target thresholds, then adjusts stimulation parameters based on the relationship between individual-specific biomarkers and target thresholds.
[0056] The stimulation parameter setting device provided in this embodiment can automatically select between variable frequency and constant frequency modes to set stimulation parameters based on 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 analyzes physiological signals to extract individual-specific biomarkers and compares them with target thresholds, thereby finely adjusting the stimulation parameters to match the individual's current severity of motor impairment. This personalized stimulation parameter setting method, which combines the severity of motor impairment, can enhance the treatment effect.
[0057] As shown in Figure 2, in step S1, the processor determines the frequency conversion mode or the constant frequency mode based on the patient's acceleration, specifically including:
[0058] S11, acquire the patient's triaxial acceleration. Acceleration data can be acquired within any time window between 20s and 60s, preferably 40s. To improve data accuracy, the triaxial accelerometer can be calibrated using the patient's historical postural acceleration characteristics, such as upright, left lateral decubitus, right lateral decubitus, and supine positions, to ensure the accuracy of the triaxial accelerometer.
[0059] S12 extracts time-domain features from the triaxial acceleration, including but not limited to the calculation of statistical features, dynamic features, and other relevant time-domain characteristic parameters of the acceleration signal, to describe the changing trend and distribution characteristics of the acceleration signal. Specifically, the extracted time-domain features may include, but are not limited to: the signal's mean, variance, standard deviation, extreme value difference, peak value, median, skewness, kurtosis, zero crossover rate, and other statistical features describing the signal's dynamic characteristics.
[0060] S13 uses a pre-trained classifier to classify and discriminate the extracted temporal features, including body position switching, body position stillness, and walking state.
[0061] Postural state transitions include switching between lying / sitting and standing positions, while static postural states include remaining still in lying / sitting / standing positions. The pre-trained classifier is trained based on the patient's historical posture data for each category. The pre-trained classifier is used to determine whether the patient is in a postural state transition or walking state based on the extracted temporal features. If the patient is in a postural state transition or walking state, the processor executes step S14; if the patient is in a static postural state, the processor executes step S15.
[0062] S14, Variable frequency mode is selected. If the patient changes between lying and standing positions, sitting and standing positions, or is walking, variable frequency mode is selected, and stimulation signals will be output according to the preset variable frequency stimulation parameters.
[0063] S15, the static body position is timed, and when the preset duration is reached, the constant frequency mode is selected.
[0064] The preset duration t can be set to 1s < t < 120s. If the patient remains still in a supine / sitting / standing position 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 according to 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 acquired, and the interval between two consecutive posture classification judgments is greater than or equal to 0.1s.
[0065] This embodiment utilizes a processor to recognize body position changes and walking states, addressing balance difficulties and frozen gait caused by anticipated postural adjustment impairments by switching between constant-frequency and variable-frequency stimulation modes. This achieves comprehensive coverage of limb and axial symptoms throughout the entire timeframe. Anticipated Posture: By monitoring body position changes and adjusting stimulation accordingly, it provides some prediction of posture, thus alleviating balance difficulties and frozen gait to a certain extent.
[0066] In one embodiment, the processor extracts individual-specific biomarkers based on physiological signals in step S3, specifically including:
[0067] S311, collects local field potentials (LFPs) of patients under stimulation in both drug-treated and drug-free states.
[0068] "Receiving medication" refers to patients currently taking relevant medications, typically those used to treat neurological disorders, such as those related to Parkinson's disease. LFP data is collected from patients while they are taking medication and not taking medication, with the deep brain stimulator (DBS) on, i.e., while the patient is receiving electrical stimulation therapy (not limited to STN nuclei; other nuclei, cortical areas, etc., are considered as needed).
[0069] S312, the collected local field potentials (LFP) of both untreated and treated areas are segmented over time, and the average power spectral density (PSD) of each segment is calculated. The average power spectral density (PSD) is expressed in dB (10log10 of the result).
[0070] The segment duration can be customized as needed. For LFP data from both non-medicated and drug-treated individuals, the same segment length and segmentation method should be used to ensure consistency and comparability of the analysis.
[0071] S313, calculate the characteristic power spectral density difference based on the average power spectral density.
[0072] S314, fitting aperiodic components based on characteristic power spectral density differences.
[0073] S315 removes the non-periodic components from the characteristic power spectral density difference and fits the remaining periodic components to obtain the fitting result.
[0074] Non-periodic components refer to the parts of a time series that do not follow a fixed periodic pattern, while periodic components refer to the parts of a time series that follow a fixed periodic pattern.
[0075] S316, obtain individual-specific biomarkers based on the fitting results.
[0076] In this embodiment, the processor collects local field potential (LFP) data from patients undergoing deep brain stimulation (DBS) therapy in both drug-treated and drug-free states. It then performs time-segmentation and calculates the average power spectral density. Further steps include calculating the characteristic power spectral density difference, fitting non-periodic components, removing non-periodic components, and fitting the remaining periodic components to ultimately obtain individual-specific biomarkers. This method not only improves the accuracy of biomarker extraction but also provides a deeper reflection of the effects of drugs on neural activity and individual differences in treatment responses, offering strong support for precision medicine and personalized healthcare for neurological diseases.
[0077] Further, in step S313, the processor calculates the characteristic power spectral density difference based on the average power spectral density, specifically including:
[0078] The average power spectral density of all segments is calculated for both the untreated and treated states, and the difference between the average values in the two states is obtained to obtain the characteristic power spectral density difference.
[0079] In this embodiment, the processor can more accurately assess the modulatory effect of drugs on neural activity and the physiological response of individuals after receiving drug treatment by precisely calculating the characteristic power spectral density difference, thereby improving the accuracy of biomarker extraction.
[0080] Further, in step S314, the processor fits the aperiodic component based on the characteristic power spectral density difference, specifically including:
[0081] S3141, using the 1 / f function to fit the non-periodic component in the characteristic power spectral density difference;
[0082] S3142, removes the non-periodic components from the characteristic power spectral density difference;
[0083] S3143, using Gaussian function fitting to remove the characteristic power spectral density difference of non-periodic components to obtain periodic components;
[0084] S3144: Subtract the periodic component from the characteristic power spectral density difference, and repeat step S3141. Use the 1 / f function to fit the characteristic power spectral density difference after subtracting the periodic component to obtain the second aperiodic component. Repeat the above aperiodic component extraction operation until the fitting error of the aperiodic component is less than the expected error value to obtain the target aperiodic component. This process is typically repeated approximately 20 times.
[0085] In this embodiment, the processor iteratively uses the 1 / f function and a Gaussian function for fitting, gradually and accurately extracting the aperiodic component from the characteristic power spectral density difference, thus improving the accuracy of the aperiodic component fitting. Furthermore, this allows for more effective removal of aperiodic interference from the characteristic power spectral density difference during the extraction of individual-specific biomarkers, thereby more accurately focusing on the periodic component. Accurate extraction of the periodic component is crucial for evaluating drug treatment effects and revealing physiological changes; therefore, using the periodic component to extract individual-specific biomarkers is more accurate.
[0086] Further, in step S316, the processor obtains individual-specific biomarkers based on the fitting results, including:
[0087] By taking the pre-set confidence interval of the peak value of the fitted result, the range of individual-specific biomarkers can be obtained. The pre-set confidence interval can be 20% of the peak value, and the final result can be, for example, the β range, 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, thus avoiding errors caused by data fluctuations or noise interference.
[0089] As shown in Figure 3, the processor obtains the target threshold in step S3, specifically including:
[0090] S321, Obtain the pre-constructed time series model of individual-specific biomarkers and the parameters of the time series model with optimal specificity.
[0091] Specifically, the process of pre-constructing a time series model of individual-specific biomarkers is as follows: taking into account the quality of the acquired signals and the patient's clinical complaints, the side of the feedback signal source is selected (left brain, right brain, or both sides; depending on the source, unilateral or bilateral stimulation can be selected). Historical local field potential (LFP) data of brain stimulation in patients without medication (DBS ON MED OFF) and historical local field potential (LFP) data of brain stimulation with medication (DBS ON MED ON) are collected (not limited to STN nuclei; other nuclei, cortex, etc. are considered as needed).
[0092] The collected physiological signals of patients who did not receive the drug and those who received the drug were collected in segments or at different time periods, and the average power spectral density (PSD) of each signal segment was calculated in dB (10log10 of the result).
[0093] Calculate the average power spectral density (PSD) of all segments of physiological signal data for those who have not received the drug, and calculate the average power spectral density (PSD) of all segments of physiological signal data for those who have received the drug.
[0094] Subtract the two average values to obtain the characteristic power spectral density difference;
[0095] The aperiodic component of the characteristic power spectral density difference is fitted using the 1 / f function and then removed. The periodic component after removing the aperiodic component is fitted using the Gaussian function. The periodic component is removed from the characteristic power spectral density difference to obtain the aperiodic component fitting error. The aperiodic component fitting error is compared with the preset aperiodic component fitting error. If the aperiodic component fitting error is higher than the preset aperiodic component fitting error, the fitting and removal steps continue until the aperiodic component fitting error is less than the preset aperiodic component fitting error.
[0096] The fitting error of the non-periodic component in the characteristic power spectral density difference is removed, and the remaining periodic component is fitted using a Gaussian function. The 20% confidence interval of the Gaussian fitting result is then used to calculate the range of historical individual-specific biomarkers. This data is then used to train a time series model.
[0097] Using the relative moving mean / low-pass filter deviation (RMSE) as an evaluation metric, the optimal time series model parameters are selected. In time series analysis, the selection of optimal model parameters (also known as optimal time series model parameters) depends on the specific model, such as autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The goal is to enable the model to best fit the data, thereby making accurate predictions and interpretations.
[0098] S322, using a time series model, the mean value of the individual-specific biomarker under a predefined sliding time is selected as the standard value at the current time based on the parameters of the time series model.
[0099] Specifically, the individual-specific biomarkers extracted from physiological signals are input into the time series model to obtain ARIMA prediction results. The mean of the ARIMA prediction results at a predetermined sliding time is obtained by means of averaging or low-pass filtering, and used as the standard value at the current time. The sliding time can be set from 1 to 120 minutes, preferably 15 minutes.
[0100] S323, correct the current time standard value according to the predefined upper and lower reference thresholds to obtain the current time target threshold.
[0101] Individual-specific biomarkers are compared with the target threshold at the current time, and stimulation parameters are determined based on the comparison results.
[0102] Furthermore, in step S3, the processor adjusts the stimulation parameters based on the relationship between the individual-specific biomarker and the target threshold. Specifically, it compares the individual-specific biomarker with the target threshold at the current time and determines the stimulation parameters based on the comparison result. This includes:
[0103] If an individual-specific biomarker exceeds the upper threshold of the current target threshold, the stimulus intensity is increased. Taking the frequency of a β-band signal as an example, if β is greater than the upper threshold of the current target threshold, the stimulus intensity is increased. , The step size can be adjusted step by step, either manually or determined by an algorithm.
[0104] If the individual-specific biomarker is below the lower threshold of the current target threshold, the stimulus intensity is reduced. If β is below the lower threshold of the current target threshold, the stimulus intensity is reduced. .
[0105] If the individual-specific biomarker is between the upper and lower thresholds of the target threshold at the current time, the current stimulus intensity is maintained.
[0106] The target threshold in this embodiment is constantly changing. By using the sliding threshold controlled release mode, compared with the existing dual-threshold and single-threshold feedback control modes, it can effectively reduce feedback stimulus lag, reduce the fluctuation of biomarkers introduced by the stimulus, and at the same time has a better resistance to system noise.
[0107] In another embodiment, the processor acquiring the target threshold and stimulation parameters in step S3 can also be: determining the target threshold and stimulation parameters based on clinical assessment information, including clinical assessment scales and side effect assessments.
[0108] Specifically, upon initial activation of the implantable pulse generator, the parameters of each contact point will be iterated using standard methods to obtain the treatment window range of each contact point at standard frequency and pulse width, and the optimal contact point parameters will be selected as the standard treatment parameters. The selected target thresholds (upper and lower thresholds) must be within the treatment window range. Stimulation upper and lower threshold tests will be performed at both the minimum and maximum drug efficacy levels. The stimulation upper and lower thresholds will be determined based on the patient's MDS UPDRS III score and subjective and objective side effect assessments to ensure that: i. the severity of motor symptoms is similar at the minimum drug efficacy - upper threshold and the maximum drug efficacy - lower threshold, with a difference of <10% in MDS UPDRS III scores; ii. the patient can tolerate the stimulation at both the minimum drug efficacy - lower threshold and the maximum drug efficacy - upper threshold, without severe motor instability or dyskinesia, thus ensuring the safety of closed-loop stimulation.
[0109] In another embodiment, the processor adjusts the stimulation parameters in step S3 based on the relationship between individual-specific biomarkers and target thresholds, including:
[0110] If the individual-specific biomarker is greater than the upper threshold of the target threshold at the current time, then it is determined to increase the stimulus intensity.
[0111] If the individual-specific biomarker is lower than the lower threshold of the target threshold at the current time, then the stimulus intensity is reduced.
[0112] If the individual-specific biomarker is between the upper and lower thresholds of the target threshold at the current moment, then the current stimulus intensity is maintained.
[0113] The processor compares the range of individual-specific biomarkers obtained in step S316 with the target threshold and determines the stimulus intensity based on the comparison result.
[0114] In this embodiment, the processor dynamically adjusts the stimulation parameters based on the relationship between the patient's individual-specific biomarkers and the target threshold, thereby achieving personalized treatment plans and improving the effectiveness and safety for patients.
[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.
[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.
[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.
[0119] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A stimulation parameter setting device for dyskinesia, characterized by, Including the processor; The processor is used to determine the variable frequency mode or constant frequency mode based on the patient's acceleration; when the variable frequency mode is determined, the stimulation parameters are adjusted to the preset variable frequency stimulation parameters; when the constant frequency mode is determined, individual-specific biomarkers are extracted based on physiological signals, and a target threshold is obtained, and the stimulation parameters are adjusted based on the relationship between the individual-specific biomarkers and the target threshold.
2. The apparatus according to claim 1, characterized in that, The variable frequency mode or constant frequency mode is determined based on the patient's acceleration, including: Obtain the patient's triaxial acceleration; Temporal features were extracted from the triaxial accelerations. A pre-trained classifier is used to classify and discriminate the extracted temporal features, including body position switching, static body position, and walking state. If the patient is changing positions or walking, then the variable frequency mode will be used. If the patient remains stationary in a position for the preset duration, the constant frequency mode will be selected.
3. The apparatus according to claim 1, characterized in that, Obtain the target threshold, including: Obtain pre-constructed time series models of individual-specific biomarkers and the parameters of the time series models with optimal specificity; Using a time series model, the mean value of the individual-specific biomarker under a predefined sliding time is selected as the standard value at the current time based on the parameters of the time series model. The target threshold at the current time is obtained by correcting the standard value at the current time based on the predefined upper and lower reference thresholds.
4. The apparatus according to claim 3, characterized in that, Adjusting stimulation parameters based on the relationship between the individual-specific biomarker and the target threshold includes: If the individual-specific biomarker is greater than the upper threshold of the target threshold at the current time, then it is determined to increase the stimulus intensity; If the individual-specific biomarker is less than the lower threshold of the target threshold at the current time, then it is determined to reduce the stimulation intensity. If the individual-specific biomarker is between the upper and lower thresholds of the target threshold at the current time, the current stimulus intensity is maintained.
5. The apparatus according to claim 1, characterized in that, Obtain the target threshold and stimulation parameters, including: The target threshold and stimulation parameters are determined based on clinical assessment information, which includes clinical assessment scales and side effect assessments.
6. The apparatus according to claim 1, characterized in that, Individual-specific biomarkers are extracted based on physiological signals, including: Local field potentials (LFPs) were collected from patients under stimulation in both drug-treated and drug-free states. The collected local field potentials (LFP) of untreated and drug-treated individuals were segmented over time, and the average power spectral density of each segment of the local field potentials (LFP) was calculated. The characteristic power spectral density difference is calculated based on the average power spectral density. Fit the aperiodic component based on the characteristic power spectral density difference; Remove the non-periodic components from the characteristic power spectral density difference and fit the remaining periodic components to obtain the fitting result; Individual-specific biomarkers are obtained based on the fitting results.
7. The apparatus according to claim 6, characterized in that, Calculating the characteristic power spectral density difference based on the average power spectral density includes: The average power spectral density of all segments is calculated for both the untreated and treated states, and the difference between the average values in the two states is obtained to obtain the characteristic power spectral density difference.
8. The apparatus according to claim 6, characterized in that, Fitting aperiodic components based on the characteristic power spectral density difference includes: The non-periodic components in the characteristic power spectral density difference are fitted using the 1 / f function; Remove the non-periodic components from the characteristic power spectral density difference; The periodic components are obtained by fitting the characteristic power spectral density difference to remove the non-periodic components using a Gaussian function. The periodic component is subtracted from the characteristic power spectral density difference, and the characteristic power spectral density difference after subtracting the periodic component is refitted using the 1 / f function to obtain the second aperiodic component. The aperiodic component extraction operation is repeated until the fitting error of the aperiodic component is less than the expected error value, and the target aperiodic component is obtained.
9. The apparatus according to claim 6, characterized in that, Based on the fitting results, individual-specific biomarkers are obtained, including: The range of individual-specific biomarkers is obtained by taking the preset confidence interval of the peak value of the fitting result.
10. An implantable closed-loop stimulation system, characterized in that, Includes the stimulation parameter setting device and controller as described in any one of claims 1-9; The controller is used to output a stimulation signal according to the stimulation parameters.