Method and system for processing electroencephalogram signals in real time based on vagus nerve stimulation

Abstract

The invention discloses an electroencephalogram signal real-time processing method and system based on vagus nerve stimulation, and the method comprises the steps: obtaining first channel data of an electroencephalogram signal channel of a target user, carrying out the recognition of the first channel data according to a pre-trained recognition model, and determining the state information of the target user; when it is determined that the target user is abnormal according to the state information of the target user, a corresponding diagnosis and treatment strategy is determined, the diagnosis and treatment strategy comprises stimulation current information based on vagus nerve stimulation, and the stimulation current information comprises the magnitude of stimulation current and the stimulation time period; stimulation current is adopted for stimulation according to the diagnosis and treatment strategy, and second channel data of the electroencephalogram signal channel of the target user in the stimulation time period are obtained; and performing user state analysis according to the second channel data and a pre-trained identification model, and determining a diagnosis and treatment strategy of the next stimulation period. According to the scheme, the electroencephalogram signals of the user can be analyzed, diagnosed and treated.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a real-time processing method and system for electroencephalogram (EEG) signals based on vagus nerve stimulation. Background Art

[0002] Brain diseases are difficult to detect and can easily occur suddenly. For example, epilepsy is a serious neurological disease. Different types of epilepsy may correspond to different brain regions, and epileptic seizures are complex and diverse. As an important tool for recording brain activity, EEG signals are widely used in the detection of brain diseases.

[0003] The existing solution usually involves acquiring EEG signals and then performing abnormal monitoring. If an abnormality occurs, a prompt is issued, but there is no diagnosis and treatment plan for the corresponding disease. Summary of the invention

[0004] The present invention provides a real-time processing method and system for EEG signals based on vagus nerve stimulation, which can analyze and diagnose the EEG signals of a user, and provide continuous diagnosis and treatment based on the changes produced by the diagnosis and treatment.

[0005] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0006] In the first aspect, the present application provides a real-time processing method for EEG signals based on vagus nerve stimulation, the method comprising: obtaining first channel data of the EEG signal channel of the target user, and identifying the first channel data based on a pre-trained recognition model to determine the status information of the target user; when it is determined that the target user has an abnormality based on the status information of the target user, determining a corresponding diagnosis and treatment strategy, the diagnosis and treatment strategy comprising stimulation current information based on vagus nerve stimulation, the stimulation current information comprising the magnitude of the stimulation current and the stimulation period; stimulating with the stimulation current according to the diagnosis and treatment strategy, and obtaining second channel data of the EEG signal channel of the target user within the stimulation period; performing user status analysis based on the second channel data and a pre-trained recognition model to determine the diagnosis and treatment strategy for the next stimulation period.

[0007] Preferably, the recognition model is obtained after training based on training data, and the method also includes the step of determining the training data: obtaining historical channel data of the EEG channel of the target user, and converting the historical channel data to obtain first intermediate data; matching the first intermediate data with second intermediate data of multiple data providers to determine target intermediate data in the second intermediate data that matches the first intermediate data, the second intermediate data being obtained after conversion of historical channel data of related users of the target user of the data provider; extracting historical channel data corresponding to the target intermediate data to determine training data to train the recognition model.

[0008] Preferably, the data conversion is used to divide the historical channel data into corresponding levels according to corresponding level division rules, and replace them with corresponding numbers; after the data conversion of the historical channel data, the first intermediate data is obtained, including: sending the level division rules to the user side, the level division rules include four groups of interval information, so as to divide the historical channel data into four levels, the four levels include the first level, the second level, the third level and the fourth level, the first level, the second level, the third level and the fourth level are replaced by the numbers 0, 1, 2, and 3; the user side divides the historical channel data of the target user into levels according to the level division rules, and converts them into corresponding numbers to form the first intermediate data, which is then uploaded to the server for analysis.

[0009] Preferably, after the data provider performs data conversion on the historical channel data, the step of obtaining the second intermediate data includes: sending a grade classification rule to the data provider; the data provider grades the historical channel data of the relevant users of the local target user according to the grade classification rule, and converts it into corresponding numbers to form the second intermediate data, which is uploaded to the server for analysis.

[0010] Preferably, the step of determining the related users of the target user includes: performing statistics based on the first intermediate data of the target user to determine the data volume of each level of the target user in the level division rule as the first statistic; obtaining the data volume of each level of all users of each data provider in the level division rule as the second statistic; determining the difference between the first statistic and the second statistic, and determining and screening out the related users of the target user from all users according to the difference threshold.

[0011] Preferably, the step of determining the second statistic includes: sending screening and division rules to the data provider, the screening and division rules include at least eight groups of interval information to divide the historical channel data into eight levels, and each group of interval information in the level division rules includes at least two groups of interval information of the screening and division rules; the data provider divides the historical channel data of all local users into corresponding levels according to the screening and division rules, and counts the data volume corresponding to each level; receives the data volume of each level corresponding to the screening and division rules, and determines the data volume of each level in the level division rules based on the data volume of each level in the screening and division rules, so as to determine the second statistic.

[0012] Preferably, the extracting of historical channel data corresponding to the target intermediate data to determine the training data includes: setting up a sandbox environment, and obtaining the historical channel data corresponding to the target intermediate data in the sandbox environment; in the sandbox environment, similarly matching the historical channel data of the target user with the historical channel data corresponding to the target intermediate data to determine the training data.

[0013] In the second aspect, the present application provides a real-time EEG signal processing system based on vagus nerve stimulation, the system comprising: a first data acquisition module, used to acquire first channel data of the EEG signal channel of the target user, and identify the first channel data based on a pre-trained recognition model to determine the status information of the target user; a diagnosis and treatment strategy acquisition module, used to determine the corresponding diagnosis and treatment strategy when it is determined that the target user has an abnormality based on the status information of the target user, the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the magnitude of the stimulation current and the stimulation period; a second data acquisition module, used to stimulate with the stimulation current according to the diagnosis and treatment strategy, and acquire second channel data of the EEG signal channel of the target user within the stimulation period; a second data analysis module, used to perform user status analysis based on the second channel data and a pre-trained recognition model, and determine the diagnosis and treatment strategy for the next stimulation period.

[0014] In a third aspect, the present application provides an electronic device comprising: a memory and at least one processor; the memory is used to store computer-executable instructions; the at least one processor is used to execute the computer-executable instructions stored in the memory, so that the at least one processor executes the method described in the first aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0016] The present application can be applied in scenarios where patients' symptoms are managed based on EEG signals. A personalized recognition model for users can be pre-trained to identify the channel data of the user's EEG signal channel, and to identify whether the user has abnormalities based on the channel data, thereby forming a diagnosis and treatment plan. The diagnosis and treatment strategy for the next stage can be adjusted based on whether the user's symptoms are gradually alleviated or aggravated after diagnosis and treatment, thereby providing treatment for the user. Specifically, this solution can obtain the user's historical data, and match similar users based on the user's historical data, and then use the data of the user and related users to train the recognition model. After that, the first channel data of the user's EEG signal channel can be collected, and the first channel data can be recognized according to the pre-trained recognition model to determine the user's status information; when it is determined that the target user has an abnormality based on the target user's status information, the corresponding diagnosis and treatment strategy is determined, and the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the size of the stimulation current and the stimulation period; then, the stimulation current is used for stimulation according to the diagnosis and treatment strategy, and the second channel data of the target user's EEG signal channel in the stimulation period is obtained; the user status is analyzed based on the second channel data and the pre-trained recognition model, and the diagnosis and treatment strategy for the next stimulation period is determined to form continuous treatment for the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 It is a flowchart of real-time processing of EEG signals based on vagus nerve stimulation according to one embodiment of the present application;

[0019] Figure 2 It is a structural schematic diagram of a real-time EEG signal processing system based on vagus nerve stimulation according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0021] The present application can be applied in the scenario of managing the symptoms of patients based on EEG signals. The user's personalized recognition model can be pre-trained to identify the channel data of the user's EEG signal channel, and to identify whether the user has abnormalities according to the channel data, to form a diagnosis and treatment plan, and to adjust the diagnosis and treatment strategy for the next stage according to whether the user's symptoms are gradually alleviated or aggravated after diagnosis and treatment, so as to form treatment for the user. This solution provides a real-time EEG signal processing system based on vagus nerve stimulation, which involves a user, a data provider and a server. The user can provide historical data to the server, and the server interacts with the data provider based on the historical data to obtain users similar to the user, thereby forming training data and training the recognition model. After that, the server deploys the recognition model to the user to identify the user's EEG signal on the user side and perform corresponding diagnosis and treatment. Among them, the training data may include signal data of multiple EEG signal channels and corresponding annotations (such as whether they are abnormal and the corresponding symptoms). And the server can match the recognition results of the recognition model with corresponding diagnosis and treatment strategies.

[0022] Specifically, this solution can obtain the user's historical data, and match similar users based on the user's historical data, and then use the data of the user and related users to train the recognition model. After that, the first channel data of the user's EEG signal channel can be collected, and the first channel data can be recognized according to the pre-trained recognition model to determine the user's status information; when it is determined that the target user has an abnormality based on the target user's status information, the corresponding diagnosis and treatment strategy is determined, and the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the size of the stimulation current and the stimulation period; then, the stimulation current is used for stimulation according to the diagnosis and treatment strategy, and the second channel data of the target user's EEG signal channel in the stimulation period is obtained; the user status is analyzed based on the second channel data and the pre-trained recognition model, and the diagnosis and treatment strategy for the next stimulation period is determined to form continuous treatment for the user.

[0023] Specifically, the present application embodiment provides a method for real-time processing of EEG signals based on vagus nerve stimulation, such as Figure 1 As shown, the method includes:

[0024] Step 102: Acquire the first channel data of the EEG signal channel of the target user, and recognize the first channel data according to a pre-trained recognition model to determine the state information of the target user.

[0025] Step 104: When it is determined that an abnormality occurs in the target user based on the state information of the target user, a corresponding diagnosis and treatment strategy is determined, wherein the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the magnitude of the stimulation current and the stimulation period.

[0026] Step 106: Stimulate with a stimulation current according to the diagnosis and treatment strategy, and obtain the second channel data of the EEG signal channel of the target user during the stimulation period.

[0027] Step 108: Perform user status analysis based on the second channel data and the pre-trained recognition model to determine the diagnosis and treatment strategy for the next stimulation period.

[0028] The present application can be applied in scenarios where patients' symptoms are managed based on EEG signals. A personalized recognition model for users can be pre-trained to identify the channel data of the user's EEG signal channel, and to identify whether the user has abnormalities based on the channel data, thereby forming a diagnosis and treatment plan. The diagnosis and treatment strategy for the next stage can be adjusted based on whether the user's symptoms are gradually alleviated or aggravated after diagnosis and treatment, thereby providing treatment for the user. Specifically, this solution can obtain the user's historical data, and match similar users based on the user's historical data, and then use the data of the user and related users to train the recognition model. After that, the first channel data of the user's EEG signal channel can be collected, and the first channel data can be recognized according to the pre-trained recognition model to determine the user's status information; when it is determined that the target user has an abnormality based on the target user's status information, the corresponding diagnosis and treatment strategy is determined, and the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the size of the stimulation current and the stimulation period; then, the stimulation current is used for stimulation according to the diagnosis and treatment strategy, and the second channel data of the target user's EEG signal channel in the stimulation period is obtained; the user status is analyzed based on the second channel data and the pre-trained recognition model, and the diagnosis and treatment strategy for the next stimulation period is determined to form continuous treatment for the user.

[0029] This solution can obtain the user's historical data, and match similar users based on the user's historical data, and then use the data of the user and related users to train the recognition model. Compared with the solution of directly deploying a large model, this solution deploys fewer model parameters and is an identification model suitable for user symptoms. The model of this solution can adopt a neural network model. In order to protect the data of the user and the data provider, this solution can convert the data and then match it to determine the data of related users associated with the target user for training. Specifically, as an optional embodiment, the recognition model is obtained after training based on the training data, and the method also includes the step of determining the training data: obtaining the historical channel data of the EEG channel of the target user, and converting the historical channel data to obtain the first intermediate data; matching the first intermediate data with the second intermediate data of multiple data providers, and determining the target intermediate data in the second intermediate data that matches the first intermediate data, and the second intermediate data is obtained after conversion based on the historical channel data of the related users of the target user of the data provider; extracting the historical channel data corresponding to the target intermediate data to determine the training data to train the recognition model.

[0030] The historical data is arranged in time. This solution can divide each historical data (historical channel data) (of the user and the data provider) into corresponding levels and convert them into numbers to form a digital string. After that, it is only necessary to match whether the digital strings are the same or similar to determine whether the historical data of the user and the data provider's user data are similar, and then filter out the data that can be used for model training. Specifically, as an optional embodiment, the data conversion is used to divide the historical channel data into corresponding levels according to the corresponding level division rules, and replace them with corresponding numbers; after the historical channel data is converted, the first intermediate data is obtained, including: sending the level division rules to the user side, the level division rules include four groups of interval information, so as to divide the historical channel data into four levels, the four levels include the first level, the second level, the third level and the fourth level, and the first level, the second level, the third level and the fourth level are replaced by the numbers 0, 1, 2, and 3; the user side divides the historical channel data of the target user according to the level division rules, and converts them into corresponding numbers to form the first intermediate data, which is uploaded to the server for analysis. As an optional embodiment, after the data provider performs data conversion on the historical channel data, the step of obtaining the second intermediate data includes: sending a level classification rule to the data provider; the data provider classifies the historical channel data of the relevant users of the local target user according to the level classification rule, and converts it into corresponding numbers to form the second intermediate data, which is uploaded to the server for analysis. If the data is divided into numbers from 0 to 3, two bytes can be used to transmit the data, and the amount of data exchanged is small.

[0031] In addition, some data of the data provider are quite different from the data of the user. In order to filter out this part of the data, this solution can count the amount of data at each level in the digital string, such as counting the number corresponding to the numbers 0, 1, 2, and 3. Then analyze whether the number of users is close to the number of data of the data provider, and then remove the data in the data provider that is significantly different from the user's data. Specifically, as an optional embodiment, the step of determining the relevant users of the target user includes: performing statistics based on the first intermediate data of the target user, determining the amount of data of the target user at each level in the level division rule as the first statistic; obtaining the amount of data of all users of each data provider at each level in the level division rule as the second statistic; determining the difference between the first statistic and the second statistic, and determining and filtering out the relevant users of the target user from all users according to the difference threshold. The step of determining the relevant users is completed before obtaining the second intermediate data.

[0032] In the statistical stage, this solution can divide the data of the data provider into more levels, so that when matching other level division rules later, statistics can be obtained directly by data combination. For example, the level division rule is 0-3, which can be further divided into 8 levels (such as levels 4-11). As the screening division rule, level 0 corresponds to levels 4 and 5; the data provider uploads 8 levels of data, and the server can combine the 8 levels to obtain the data corresponding to 4 levels for matching. Specifically, as an optional embodiment, the step of determining the second statistic includes: sending the screening division rule to the data provider, the screening division rule includes at least eight groups of interval information to divide the historical channel data into eight levels, and each group of interval information in the level division rule includes at least two groups of interval information of the screening division rule; the data provider divides the historical channel data of all local users into corresponding levels according to the screening division rule, and counts the data corresponding to each level; receives the data volume of each level corresponding to the screening division rule, and determines the data volume of each level in the level division rule according to the data volume of each level in the screening division rule, so as to determine the second statistic.

[0033] After the target intermediate data corresponding to the user is determined under the premise of protecting the data privacy of all parties, the original data can be obtained and matched in a sandbox, which is a safe environment that is not affected by external factors, thereby ensuring data security. Specifically, as an optional embodiment, the extraction of historical channel data corresponding to the target intermediate data to determine the training data includes: setting a sandbox environment, and obtaining the historical channel data corresponding to the target intermediate data in the sandbox environment; in the sandbox environment, similarly matching the historical channel data of the target user with the historical channel data corresponding to the target intermediate data to determine the training data.

[0034] On the basis of the above embodiments, the embodiments of the present application also provide a real-time EEG signal processing system based on vagus nerve stimulation, such as Figure 2 As shown, the system comprises:

[0035] The first data acquisition module 202 is used to acquire the first channel data of the EEG signal channel of the target user, and recognize the first channel data according to a pre-trained recognition model to determine the state information of the target user.

[0036] The diagnosis and treatment strategy acquisition module 204 is used to determine the corresponding diagnosis and treatment strategy when it is determined that the target user has an abnormality based on the status information of the target user. The diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the size of the stimulation current and the stimulation period.

[0037] The second data acquisition module 206 is used to adopt the stimulation current for stimulation according to the diagnosis and treatment strategy, and obtain the second channel data of the EEG signal channel of the target user during the stimulation period.

[0038] The second data analysis module 208 is used to analyze the user status based on the second channel data and the pre-trained recognition model to determine the diagnosis and treatment strategy for the next stimulation period.

[0039] The implementation of the embodiment of the present application is similar to the implementation of the embodiment of the method described above. The specific implementation can refer to the specific implementation of the method described above, which will not be repeated here.

[0040] The present application can be applied in scenarios where patients' symptoms are managed based on EEG signals. A personalized recognition model for users can be pre-trained to identify the channel data of the user's EEG signal channel, and to identify whether the user has abnormalities based on the channel data, thereby forming a diagnosis and treatment plan. The diagnosis and treatment strategy for the next stage can be adjusted based on whether the user's symptoms are gradually alleviated or aggravated after diagnosis and treatment, thereby providing treatment for the user. Specifically, this solution can obtain the user's historical data, and match similar users based on the user's historical data, and then use the data of the user and related users to train the recognition model. After that, the first channel data of the user's EEG signal channel can be collected, and the first channel data can be recognized according to the pre-trained recognition model to determine the user's status information; when it is determined that the target user has an abnormality based on the target user's status information, the corresponding diagnosis and treatment strategy is determined, and the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the size of the stimulation current and the stimulation period; then, the stimulation current is used for stimulation according to the diagnosis and treatment strategy, and the second channel data of the target user's EEG signal channel in the stimulation period is obtained; the user status is analyzed based on the second channel data and the pre-trained recognition model, and the diagnosis and treatment strategy for the next stimulation period is determined to form continuous treatment for the user.

[0041] Based on the above embodiments, the present application also provides an electronic device, comprising: a memory and at least one processor; the memory is used to store computer execution instructions; the at least one processor is used to execute the computer execution instructions stored in the memory, so that the at least one processor executes the method described in the above embodiments.

[0042] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned data processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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 1The steps for the functions specified in one or more boxes.

[0047] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0048] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0049] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0050] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0051] It will be appreciated by those skilled in the art 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 codes.

[0052] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A real-time processing method of EEG signals based on vagus nerve stimulation, characterized in that: The method comprises: Acquire the first channel data of the EEG signal channel of the target user, and identify the first channel data according to the pre-trained recognition model to determine the state information of the target user; When it is determined that the target user has an abnormality according to the state information of the target user, a corresponding diagnosis and treatment strategy is determined, wherein the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the magnitude of the stimulation current and the stimulation period; Stimulate with stimulation current according to the diagnosis and treatment strategy, and obtain the second channel data of the EEG signal channel of the target user during the stimulation period; The user status is analyzed based on the second channel data and the pre-trained recognition model to determine the diagnosis and treatment strategy for the next stimulation period.

2. The method according to claim 1, characterized in that The recognition model is obtained after training based on the training data, and the method further comprises the step of determining the training data: Acquire historical channel data of the target user's EEG channel, and perform data conversion on the historical channel data to obtain first intermediate data; Matching the first intermediate data with the second intermediate data of multiple data providers, determining target intermediate data in the second intermediate data that matches the first intermediate data, the second intermediate data being obtained by converting the historical channel data of the relevant users of the target user of the data provider; The historical channel data corresponding to the target intermediate data is extracted to determine the training data for training the recognition model.

3. The method according to claim 2, characterized in that The data conversion is used to classify the historical channel data into corresponding levels according to corresponding level classification rules, and replace them with corresponding numbers; after the historical channel data is converted, the first intermediate data is obtained, including: Sending the level division rules to the user, the level division rules include four groups of interval information, so as to divide the historical channel data into four levels, the four levels include the first level, the second level, the third level and the fourth level, the first level, the second level, the third level and the fourth level are represented by the numbers 0, 1, 2, 3; The user side classifies the historical channel data of the target user according to the class classification rules and converts them into corresponding numbers to form the first intermediate data, which is then uploaded to the server side for analysis.

4. The method according to claim 3, characterized in that The step of obtaining the second intermediate data after the data provider performs data conversion on the historical channel data includes: Sending grading rules to data providers; The data provider classifies the historical channel data of the local target user's related users according to the classification rules, and converts them into corresponding numbers to form the second intermediate data, which is then uploaded to the server for analysis.

5. The method according to claim 4, characterized in that The steps to identify relevant users of the target user include: Performing statistics based on the first intermediate data of the target user to determine the data volume of each level of the target user in the level classification rule as the first statistical amount; Obtaining the data volume of all users of each data provider at each level in the level classification rule as a second statistic; The difference between the first statistic and the second statistic is determined, and users related to the target user are screened out from all users according to a difference threshold.

6. The method according to claim 5, characterized in that The step of determining a second statistic comprises: Sending a screening and division rule to a data provider, the screening and division rule including at least eight groups of interval information to divide the historical channel data into eight levels, each group of interval information in the level division rule including at least two groups of interval information of the screening and division rule; The data provider divides the historical channel data of all local users into corresponding levels according to the screening and division rules, and counts the data volume corresponding to each level; The data amount of each level corresponding to the screening and classification rule is received, and the data amount of each level in the classification rule is determined according to the data amount of each level in the screening and classification rule to determine the second statistic.

7. The method according to claim 6, characterized in that The extracting historical channel data corresponding to the target intermediate data to determine the training data includes: Set up a sandbox environment and obtain historical channel data corresponding to the target intermediate data in the sandbox environment; In the sandbox environment, the historical channel data of the target user and the historical channel data corresponding to the target intermediate data are similarly matched to determine the training data.

8. A real-time EEG signal processing system based on vagus nerve stimulation, characterized in that: The system comprises: A first data acquisition module is used to acquire first channel data of an EEG signal channel of a target user, and recognize the first channel data according to a pre-trained recognition model to determine the state information of the target user; A diagnosis and treatment strategy acquisition module, used to determine a corresponding diagnosis and treatment strategy when it is determined that the target user has an abnormality according to the state information of the target user, wherein the diagnosis and treatment strategy includes stimulation current information based on vagus nerve stimulation, and the stimulation current information includes the magnitude of the stimulation current and the stimulation period; The second data acquisition module is used to stimulate the target user by using the stimulation current according to the diagnosis and treatment strategy, and to obtain the second channel data of the EEG signal channel of the target user during the stimulation period; The second data analysis module is used to analyze the user status based on the second channel data and the pre-trained recognition model to determine the diagnosis and treatment strategy for the next stimulation period.

9. An electronic device, characterized in that: include: memory and at least one processor; The memory is used to store computer-executable instructions; The at least one processor is configured to execute computer-executable instructions stored in the memory, so that the at least one processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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