Adaptive DBS parameter adjustment method and device

Abstract

The invention relates to a self-adaptive DBS parameter adjusting method and device, and belongs to the technical field of biomedical signal processing.The self-adaptive DBS parameter adjusting method comprises the steps that a real-time neural signal is decoded into a real-time behavior signal based on a behavior decoding model; adjusting a DBS parameter based on the real-time behavior signal; wherein the behavior decoding model is constructed based on a training result of a linear discrete state space model, the linear discrete state space model is obtained by constructing a state equation based on a neural signal, a behavior signal and a state signal, and the state signal is a potential state for simultaneously driving the neural signal and the behavior signal. According to the invention, the interference of normal behaviors is avoided while DBS parameter adjustment is realized through the symptom marker, and the complexity of regulation and control is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical signal processing, and in particular to an adaptive DBS parameter adjustment method and device. Background Art

[0002] Deep Brain Stimulation (DBS) is a neuromodulation technology that is mainly used to treat refractory neurological and psychiatric diseases, such as Parkinson's disease, epilepsy, obsessive-compulsive disorder, and depression. DBS technology is based on a deep understanding of brain function and neural circuits. For specific diseases, it implants electrodes into specific areas of the brain and applies precise electrical stimulation to regulate abnormal neural circuit activity and relieve movement symptoms.

[0003] Although DBS technology has made great progress, accurate and robust adaptive DBS closed-loop control technology still faces huge challenges. The selection and acquisition of the best neural markers are the basis and prerequisite for adaptive DBS closed-loop control to achieve good results. However, most existing DBS technologies target different disease types and select a general indicator that is generally recognized or widely used in academia and has certain empirical and only statistical rationality as a neural marker, which is difficult to meet the robustness requirements of accurate adaptive DBS closed-loop control for the best neural marker.

[0004] In order to circumvent the selection of the best neural markers, there are also schemes to directly measure symptoms through peripheral sensors and adjust stimulation parameters based on symptom markers. For example, inertial measurement units equipped with gyroscopes and accelerometers have been successfully used to detect and measure tremors, gait freezing, bradykinesia, and dyskinesia. However, the patient's normal behavior may interfere with symptom markers and cause misjudgment. In addition, adding symptom measurement sensors and supporting data transmission and processing systems to the DBS system will also increase the overall system complexity. Summary of the invention

[0005] In view of this, it is necessary to provide an adaptive DBS parameter adjustment method and device to solve the problem that the existing scheme of adjusting DBS stimulation parameters based on symptom markers is easily interfered by the normal behavior of the patient and has a high overall complexity.

[0006] In order to solve the above problems, in a first aspect, the present invention provides an adaptive DBS parameter adjustment method, comprising: Decoding real-time neural signals into real-time behavioral signals based on a behavioral decoding model; adjusting DBS parameters based on the real-time behavioral signal; Among them, the behavior decoding model is constructed based on the training results of the linear discrete state space model, and the linear discrete state space model is obtained by constructing a state equation based on neural signals, behavioral signals and state signals, and the state signal is a potential state that simultaneously drives the neural signals and behavioral signals.

[0007] In a possible implementation, the training process of the linear discrete state space model includes: Solving the linear discrete state space model based on the preprocessed historical neural signals and historical behavioral signals; The construction process of the behavior decoding model includes: A recursive Kalman filter is constructed based on the solution result of the linear discrete state space model, and the recursive Kalman filter is used as the behavior decoding model.

[0008] In a possible implementation, the linear discrete state space model is expressed as:

[0009]

[0010] in, Represents the time index, Represents neural signals, Indicates behavioral signals, Indicates the status signal. and Representation and Independent zero-mean white noise, Indicates interference behavior signal, represents the cross-correlation operation, for The autocovariance matrix of for The autocovariance matrix of for and The covariance matrix between The output matrix representing the state signal, represents the output matrix of the neural signal, Output matrix representing the behavioral signal.

[0011] In a possible implementation, solving the linear discrete state space model based on the preprocessed historical neural signal and the historical behavior signal includes: The neural signal and behavioral signal at the target moment are projected onto the neural signal at the previous moment of the target moment, and a linear least squares solution of orthogonal projection is performed to determine the linear discrete state space model. , , , , , .

[0012] In a possible implementation, the method further includes: After completing the construction of the behavior decoding model, taking the historical neural signal as the input of the behavior decoding model, and calculating the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model; The decoding effect of the behavior decoding model is evaluated based on the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model.

[0013] In a possible implementation, adjusting the DBS parameters based on the real-time behavior signal includes: determining a symptom marker value based on the real-time behavioral signal; When the symptom marker value is not within a preset threshold range, the DBS parameter is adjusted based on the symptom marker value.

[0014] In a possible implementation, adjusting the DBS parameters based on the symptom marker values ​​includes: Based on the symptom marker value, determining a stimulation parameter value corresponding to the symptom marker value in a stimulation parameter table, wherein the stimulation parameter table is determined based on clinical data, and the stimulation parameters include stimulation amplitude, stimulation frequency, and stimulation bandwidth; The DBS parameters are adjusted according to the stimulation parameter values ​​corresponding to the symptom marker values.

[0015] On the other hand, the present invention also provides an adaptive DBS parameter adjustment device, comprising: A decoding module, used for decoding the real-time neural signal into a real-time behavior signal based on a behavior decoding model; An adjustment module, configured to adjust DBS parameters based on the real-time behavior signal; Among them, the behavior decoding model is constructed based on the training results of the linear discrete state space model, and the linear discrete state space model is obtained by constructing a state equation based on neural signals, behavioral signals and state signals, and the state signal is a potential state that simultaneously drives the neural signals and behavioral signals.

[0016] In a second aspect, the present invention further provides a DBS parameter adjustment device, including a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the adaptive DBS parameter adjustment method described in any of the above implementations.

[0017] In a third aspect, the present invention further provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the adaptive DBS parameter adjustment method described in any of the above implementations.

[0018] The beneficial effects of the present invention are as follows: the adaptive DBS parameter adjustment method and device provided by the present invention decodes neural signals into behavioral signals by constructing a behavioral decoding model, thereby avoiding the selection of neural markers, and adjusting DBS parameters through behavioral signals, thereby improving the reliability of DBS parameter adjustment. At the same time, behavioral signals are obtained by decoding neural signals, without adding additional behavioral signal acquisition equipment, thereby reducing the complexity of DBS parameter adjustment, and behavioral signals obtained by decoding neural signals will not contain normal behavioral information, thereby avoiding interference with normal behavior. The present invention avoids interference with normal behavior while realizing DBS parameter adjustment through symptom markers, and reduces the complexity of regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a flow chart of an embodiment of the adaptive DBS parameter adjustment method provided by the present invention; Figure 2 A schematic diagram of an embodiment of the DBS regulation process provided by the present invention; Figure 3 A schematic diagram of an embodiment of a process for constructing a behavior decoding model provided by the present invention; Figure 4 A schematic flow chart of an embodiment of the DBS control strategy provided by the present invention; Figure 5 A schematic diagram of the structure of an embodiment of the adaptive DBS parameter adjustment device provided by the present invention; Figure 6 A schematic diagram of the structure of an embodiment of a DBS parameter adjustment device provided by the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0022] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0023] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] Before presenting the embodiments, the following terms are explained.

[0025] DBS includes intracerebral stimulation electrodes, subcutaneous wires, pulse generators, and magnet switches. The stimulation electrodes are inserted into specific brain nuclei through stereotactic technology and microelectrode recording technology, and continuous high-frequency pulse electrical stimulation is used to suppress abnormal brain nucleus discharges to achieve therapeutic effects. DBS is mainly used to treat refractory neurological and psychiatric diseases such as Parkinson's disease, epilepsy, obsessive-compulsive disorder, and depression. DBS technology is based on a deep understanding of brain region functions and neural circuits. For specific diseases, it implants electrodes into specific areas of the brain and uses precise electrical stimulation to regulate abnormal neural circuit activity and relieve movement symptoms.

[0026] The present invention provides an adaptive DBS parameter adjustment method and device, which are described below respectively.

[0027] Figure 1 A flow chart of an embodiment of the adaptive DBS parameter adjustment method provided by the present invention is shown in FIG. Figure 1 As shown, the adaptive DBS parameter adjustment method includes: S101. Decoding the real-time neural signal into a real-time behavior signal based on a behavior decoding model.

[0028] It should be noted that the real-time neural signal can be the neural signal of the brain area corresponding to the disease type. Taking Parkinson's disease as an example, the corresponding brain area can be the subthalamic nucleus, and the neural signal can be the local field potential (LFP) signal. Real-time neural signals can be acquired by implanting DBS electrodes in the brain area corresponding to the target patient's disease type. The behavioral decoding model can be used to decode neural signals into behavioral signals, and the behavioral signals can be used as symptom markers to regulate DBS stimulation parameters, thereby avoiding the selection of neural markers.

[0029] S102: Adjust DBS parameters based on the real-time behavior signal.

[0030] It should be noted that certain numerical values ​​of symptoms corresponding to the target patient's disease type can be determined through real-time behavioral signals. Taking Parkinson's disease as an example, the corresponding symptom marker may be tremor, and the symptom marker value may be the tremor frequency.

[0031] In some embodiments of the present invention, adjusting the DBS parameters based on the real-time behavior signal includes: determining a symptom marker value based on the real-time behavioral signal; When the symptom marker value is not within a preset threshold range, the DBS parameter is adjusted based on the symptom marker value.

[0032] It should be noted that: after the symptom marker value is determined through real-time behavioral signals, it can be determined whether the target patient is in an abnormal state based on the preset threshold range, and then determine whether DBS parameters need to be adjusted. The preset threshold range can be set according to the clinical data of the clinical stage, or it can be determined according to the current disease-related research data. When the symptom marker value is not within the preset threshold range, it can be determined that the target patient is in an abnormal state. When it is determined that the target patient is in an abnormal state, the DBS parameters can be adjusted according to the symptom marker value. For example, DBS can be adjusted through a comparison table of symptom marker values ​​and stimulation parameters, or a regulatory model can be constructed through a neural network, and then DBS is adjusted.

[0033] Among them, the behavior decoding model is constructed based on the training results of the linear discrete state space model, and the linear discrete state space model is obtained by constructing a state equation based on neural signals, behavioral signals and state signals, and the state signal is a potential state that simultaneously drives the neural signals and behavioral signals.

[0034] In some embodiments of the present invention, Figure 2As shown in the figure, the training process of the behavior decoding model can be carried out during the clinical follow-up debugging stage of the target patient, and the adaptive parameter adjustment process of DBS can be carried out during the daily use stage of the target patient, that is, after completing the clinical follow-up debugging, the patient does not need to wear peripheral sensors to measure symptoms in real time. In addition, since normal behavior will not be reflected in the neural activity of the brain area corresponding to the disease symptoms, the disease symptom behavior obtained by neural signal decoding will not contain normal behavior information, thereby avoiding the interference of normal behavior.

[0035] In summary, the adaptive DBS parameter adjustment method provided in the embodiment of the present invention decodes neural signals into behavioral signals by constructing a behavioral decoding model, thereby avoiding the selection of neural markers, and adjusting DBS parameters through behavioral signals, thereby improving the reliability of DBS parameter adjustment. At the same time, behavioral signals are obtained by decoding neural signals, and there is no need to add additional behavioral signal acquisition equipment, thereby reducing the complexity of DBS parameter adjustment. Moreover, the behavioral signals obtained by decoding neural signals will not contain normal behavioral information, thereby avoiding interference with normal behavior. The present invention avoids interference with normal behavior while realizing DBS parameter adjustment through symptom markers, and reduces the complexity of regulation.

[0036] In some embodiments of the present invention, Figure 3 As shown, the behavior decoding model construction process provided by the present invention includes: 1. Data preprocessing: Process the LFP signals collected by DBS electrodes into data that is more relevant to behavior and suitable for model training. The specific process of data preprocessing includes: (1) Frequency band decomposition: The collected LFP signal can be decomposed according to the frequency band information of the universal neural markers of the disease to obtain the energy information within each frequency band. For example, for Parkinson's disease, the main relevant frequency bands are the high and low beta bands (low beta: 13-20Hz, high beta: 20-30Hz) and the gamma band (γ: 30-90Hz). The energy information can be obtained by filtering and Hilbert transform or short-time Fourier transform. The energy value obtained can be selectively logarithmized to change its numerical distribution.

[0037] (2) Data / time alignment: Neural signals and behavioral signals are collected by two systems that are time-synchronized but have different sampling rates. Before combining the two signals for model training, necessary interpolation and other operations are required to align the data and time labels and unify the time dimension for use in subsequent model training.

[0038] (3) Standardization: Neural signals and behavioral signals usually have different dimensions. After standardization using the zscore method, signals of different dimensions are transformed to the same scale, reducing dimensionality deviation during model training.

[0039] (4) Downsampling: The sampling rate of neural signals (e.g. 1kHz) is usually higher than the sampling rate of behavioral signals (e.g. 20Hz). Neural signals can usually be downsampled to the sampling rate of behavioral signals, which has a certain smoothing effect on the data and can also reduce the amount of computation.

[0040] 2. Division of training and test sets: The neural data and behavioral data collected during the clinical follow-up debugging phase are divided into training sets and test sets in a ratio (e.g. 8:2). The training set is used to determine the model structure and model parameters, and the test set is used to preliminarily confirm the model effect.

[0041] 3. Model hyperparameter debugging: The dimension of the latent state and the dimension of the behavior-related latent state are two hyperparameters that need to be debugged, which can be obtained by grid search based on the training set data.

[0042] 4. Model training: Model parameter identification uses a priority subspace identification method with a two-stage identification mechanism to prioritize the extraction of potential states related to behavior. The training process only involves matrix operations, mainly including the calculation of linear least squares solutions and singular value decomposition of orthogonal projections. During the identification process, future measurable data such as future behavior and future neural activity are projected onto past neural activity, so that the identification process can only extract potential states existing in neural activity. Normal behavior (interference) is not reflected in the neural activity of the brain area corresponding to the disease symptoms, so the behavioral signal decoded from this neural activity will not contain normal behavioral information.

[0043] 5. Behavior decoding: After the model training is completed, a recursive Kalman filter is constructed based on the identified model, and the input neural signal is used to decode, predict and output the behavior.

[0044] In some embodiments of the present invention, the training process of the linear discrete state space model includes: Solving the linear discrete state space model based on the preprocessed historical neural signals and historical behavioral signals; The construction process of the behavior decoding model includes: A recursive Kalman filter is constructed based on the solution result of the linear discrete state space model, and the recursive Kalman filter is used as the behavior decoding model.

[0045] It should be noted that the preprocessing process of historical neural signals and historical behavioral signals can refer to the above-mentioned data preprocessing process. When solving the linear discrete state space model, the preprocessed historical neural signals and historical behavioral signals can be used as known quantities to solve the linear discrete state space model, thereby determining multiple parameter matrices of the linear discrete state space model. Subsequently, a recursive Kalman filter can be constructed based on the multiple parameter matrices of the linear discrete state space model, and the recursive Kalman filter can be used as the behavioral decoding model. By constructing a behavioral decoding model, neural activity can be decoded into behavioral activity while eliminating the interference of normal behavior.

[0046] In some embodiments of the present invention, the linear discrete state space model is expressed as:

[0047]

[0048] in, Represents the time index, Represents neural signals, Indicates behavioral signals, Indicates the status signal. and Representation and Independent zero-mean white noise, Indicates interference behavior signal, represents the cross-correlation operation, for The autocovariance matrix of for The autocovariance matrix of for and The covariance matrix between The output matrix representing the state signal, represents the output matrix of the neural signal, Output matrix representing the behavioral signal.

[0049] In some embodiments of the present invention, solving the linear discrete state space model based on the preprocessed historical neural signal and the historical behavior signal includes: The neural signal and behavioral signal at the target moment are projected onto the neural signal at the previous moment of the target moment, and a linear least squares solution of orthogonal projection is performed to determine the linear discrete state space model. , , , , , .

[0050] It should be noted that when the preprocessed historical neural signals and historical behavioral signals are used as known quantities to solve the linear discrete state space model, the neural signals and behavioral signals at the target moment can be projected onto the neural signals at the previous moment of the target moment, and the linear least squares solution of orthogonal projection can be performed. At the same time, singular value decomposition can also be performed to determine the linear discrete state space model. , , , , , The target moment can be any sampling moment of the preprocessed historical neural signal and historical behavioral signal.

[0051] 6. Decoding effect evaluation: Pearson linear correlation coefficient is usually used to evaluate decoding effect.

[0052] In some embodiments of the present invention, in order to ensure the reliability of the behavior decoding model, the method further includes: After completing the construction of the behavior decoding model, taking the historical neural signal as the input of the behavior decoding model, and calculating the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model; The decoding effect of the behavior decoding model is evaluated based on the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model.

[0053] It should be noted that the value range of the Pearson linear correlation coefficient is -1 to 1, -1 indicates a complete negative correlation, 1 indicates a complete positive correlation, and 0 indicates no correlation. When evaluating the decoding effect of the behavior decoding model, it is possible to determine whether the behavior decoding model can achieve the expected decoding effect based on actual needs. For example, a correlation coefficient threshold can be set to determine whether the behavior decoding model can achieve the expected decoding effect. When the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model is within the correlation coefficient threshold, it can be considered whether the behavior decoding model can achieve the expected decoding effect. Otherwise, the parameters of the behavior decoding model can be adjusted until it can achieve the expected decoding effect.

[0054] In some embodiments of the present invention, Figure 4 Shown is the process of adaptive DBS closed-loop regulation using decoded disease symptom markers.

[0055] The adaptive mode can be started by timing or manually. The exit mode is to exit after reaching the preset condition, where the preset condition is that the number of normal collections reaches a certain value, for example, 3 times.

[0056] The parameters that need to be initialized during parameter initialization are: abnormal thresholds of symptom markers (amplitude and duration thresholds), initial effective stimulation parameters, and stimulation parameter safety boundary values.

[0057] The present invention does not limit specific setting values ​​such as the adaptive timing start time interval, the collection and recording refractory period, and the neural signal collection duration, and can be adjusted and set according to actual conditions.

[0058] In some embodiments of the present invention, adjusting DBS parameters based on the symptom marker values ​​includes: Based on the symptom marker value, determining a stimulation parameter value corresponding to the symptom marker value in a stimulation parameter table, wherein the stimulation parameter table is determined based on clinical data, and the stimulation parameters include stimulation amplitude, stimulation frequency, and stimulation bandwidth; The DBS parameters are adjusted according to the stimulation parameter values ​​corresponding to the symptom marker values.

[0059] It should be noted that: a relatively simple method for adjusting stimulation parameters is the table lookup method, in which different stimulation parameter groups are selected according to the amplitude of the symptom markers obtained by decoding. This method has a small amount of calculation; the stimulation amplitude parameters can be adjusted in combination with the symptom amplitude and the bilateral threshold mechanism, and the adjustment of the stimulation amplitude can be determined according to the standard adaptation mechanism of aDBS; the stimulation frequency and pulse width can be combined with control algorithms such as Proportional Integral Derivative (PID) and fine-tuned according to changes in symptom marker monitoring variables within a certain safety margin around the initial value.

[0060] The method provided by the present invention uses clean disease symptom markers obtained through neural activity decoding for adaptive DBS closed-loop regulation. Compared with the existing adaptive DBS closed-loop regulation technology based on neural markers, it avoids the difficulty of selecting and obtaining the best neural markers; compared with the existing adaptive DBS closed-loop regulation technology based on symptom markers, it not only utilizes neural activity information but also can effectively avoid the interference of patients' daily behaviors on symptom markers, while not increasing the complexity of the overall system during the daily life use stage of the product.

[0061] Compared with other mathematical modeling methods based on deep learning, the mathematical modeling process of the method provided by the present invention only involves matrix algebraic operations and singular value decomposition, and the amount of calculation is relatively low; the model is small and has good real-time performance in practical applications; a priority subspace identification method with a two-stage identification mechanism is adopted to preferentially extract behavior-related latent states, effectively reducing the influence of latent states in neural activities that are not related to the behavior of interest, so that the extracted symptom markers do not contain interference from normal behavior, eliminating the overhead of the artifact interference removal step.

[0062] The present invention uses clean symptom markers as control quantities for adaptive DBS closed-loop regulation. The relevant parameters of the markers, such as amplitude abnormality thresholds, etc., have more intuitive physical meanings, and clinicians can give more accurate recommended values.

[0063] In order to better implement the adaptive DBS parameter adjustment method in the embodiment of the present invention, based on the adaptive DBS parameter adjustment method, correspondingly, Figure 5 As shown, the embodiment of the present invention further provides an adaptive DBS parameter adjustment device, and the adaptive DBS parameter adjustment device 500 includes: A decoding module 501 is used to use the real-time neural signal of the target patient as the input of the behavior decoding model, and use the output of the behavior decoding model as the symptom marker value of the target patient; An adjustment module 502, configured to adaptively adjust the DBS of the target patient based on the symptom marker value of the target patient; The behavior decoding model is trained based on the historical neural signals and historical behavior signals of the target patient collected during the clinical stage.

[0064] The adaptive DBS parameter adjustment device 500 provided in the above embodiment can implement the technical solution described in the above adaptive DBS parameter adjustment method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above adaptive DBS parameter adjustment method embodiment, which will not be repeated here.

[0065] like Figure 6 As shown, the present invention also provides a DBS parameter adjustment device 600. The DBS parameter adjustment device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some components of the DBS parameter adjustment device 600 are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0066] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 602, such as the magnetic resonance image optimization method of the present invention.

[0067] In some embodiments, the processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 601 may be local or remote. In some embodiments, the processor 601 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0068] In some embodiments, the memory 602 may be an internal storage unit of the DBS parameter adjustment device 600, such as a hard disk or memory of the DBS parameter adjustment device 600. In other embodiments, the memory 602 may also be an external storage device of the DBS parameter adjustment device 600, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the DBS parameter adjustment device 600.

[0069] Furthermore, the memory 602 may include both an internal storage unit of the DBS parameter adjustment device 600 and an external storage device. The memory 602 is used to store application software installed in the DBS parameter adjustment device 600 and various data.

[0070] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an organic light-emitting diode (OLED) touch device, etc. The display 603 is used to display information on the DBS parameter adjustment device 600 and to display a visual user interface. The components 601-603 of the DBS parameter adjustment device 600 communicate with each other via a system bus.

[0071] In one embodiment, when the processor 601 executes the adaptive DBS parameter adjustment program in the memory 602, the following steps may be implemented: Decoding real-time neural signals into real-time behavioral signals based on a behavioral decoding model; adjusting DBS parameters based on the real-time behavioral signal; Among them, the behavior decoding model is constructed based on the training results of the linear discrete state space model, and the linear discrete state space model is obtained by constructing a state equation based on neural signals, behavioral signals and state signals, and the state signal is a potential state that simultaneously drives the neural signals and behavioral signals.

[0072] It should be understood that: when the processor 601 executes the adaptive DBS parameter adjustment program in the memory 602, in addition to the above functions, other functions may also be implemented, and details may refer to the description of the above corresponding method embodiments.

[0073] Furthermore, the embodiment of the present invention does not specifically limit the type of the DBS parameter adjustment device 600 mentioned. The DBS parameter adjustment device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the DBS parameter adjustment device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0074] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the adaptive DBS parameter adjustment method provided in the above-mentioned method embodiments can be implemented.

[0075] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0076] The above is a detailed introduction to the adaptive DBS parameter adjustment method and device provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An adaptive DBS parameter adjustment method, characterized in that: include: Decoding real-time neural signals into real-time behavioral signals based on a behavioral decoding model; adjusting DBS parameters based on the real-time behavioral signal; Among them, the behavior decoding model is constructed based on the training results of the linear discrete state space model, and the linear discrete state space model is obtained by constructing a state equation based on neural signals, behavioral signals and state signals, and the state signal is a potential state that simultaneously drives the neural signals and behavioral signals.

2. The method for adaptive DBS parameter adjustment according to claim 1, characterized in that: The training process of the linear discrete state space model includes: Solving the linear discrete state space model based on the preprocessed historical neural signals and historical behavioral signals; The construction process of the behavior decoding model includes: A recursive Kalman filter is constructed based on the solution result of the linear discrete state space model, and the recursive Kalman filter is used as the behavior decoding model.

3. The method for adaptive DBS parameter adjustment according to claim 2, characterized in that: The linear discrete state space model is expressed as: in, Represents the time index, Represents neural signals, Indicates behavioral signals, Indicates the status signal. and Representation and Independent zero-mean white noise, Indicates interference behavior signal, represents the cross-correlation operation, for The autocovariance matrix of for The autocovariance matrix of for and The covariance matrix between The output matrix representing the state signal, represents the output matrix of the neural signal, Output matrix representing the behavioral signal.

4. The method for adaptive DBS parameter adjustment according to claim 3, characterized in that: The solving of the linear discrete state space model based on the preprocessed historical neural signal and the historical behavior signal includes: The neural signal and behavioral signal at the target moment are projected onto the neural signal at the previous moment of the target moment, and a linear least squares solution of orthogonal projection is performed to determine the linear discrete state space model. , , , , , .

5. The method for adaptive DBS parameter adjustment according to claim 2, characterized in that: The method further comprises: After completing the construction of the behavior decoding model, taking the historical neural signal as the input of the behavior decoding model, and calculating the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model; The decoding effect of the behavior decoding model is evaluated based on the Pearson linear correlation coefficient between the historical behavior signal and the output of the behavior decoding model.

6. The method for adaptive DBS parameter adjustment according to claim 1, characterized in that: The adjusting of DBS parameters based on the real-time behavior signal includes: determining a symptom marker value based on the real-time behavioral signal; When the symptom marker value is not within a preset threshold range, the DBS parameter is adjusted based on the symptom marker value.

7. The method for adaptive DBS parameter adjustment according to claim 6, characterized in that: The adjusting of DBS parameters based on the symptom marker values ​​includes: Based on the symptom marker value, determining a stimulation parameter value corresponding to the symptom marker value in a stimulation parameter table, wherein the stimulation parameter table is determined based on clinical data, and the stimulation parameters include stimulation amplitude, stimulation frequency, and stimulation bandwidth; The DBS parameters are adjusted according to the stimulation parameter values ​​corresponding to the symptom marker values.

8. An adaptive DBS parameter adjustment device, characterized in that: include: A decoding module, used for decoding the real-time neural signal into a real-time behavior signal based on a behavior decoding model; An adjustment module, configured to adjust DBS parameters based on the real-time behavior signal; Among them, the behavior decoding model is constructed based on the training results of the linear discrete state space model, and the linear discrete state space model is obtained by constructing a state equation based on neural signals, behavioral signals and state signals, and the state signal is a potential state that simultaneously drives the neural signals and behavioral signals.

9. A DBS parameter adjustment device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps in the adaptive DBS parameter adjustment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the adaptive DBS parameter adjustment method according to any one of claims 1 to 7.

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