Electrical stimulation system, nerve regulation and control equipment and epilepsy treatment equipment

Abstract

The invention belongs to the technical field of electrophysiological signal detection, and particularly relates to an electrical stimulation system, nerve regulation and control equipment and epilepsy treatment equipment. The electrical stimulation system includes: a classifier; the input end of the classifier is input characteristics of signals after electrical stimulation, and the output end of the classifier is classification results of electrical stimulation effectiveness; wherein the input characteristics comprise the stimulation channel proportion of the attack energy change before and after the electrical stimulation and / or the condition that the stimulation channel covers the strong connection node. The stimulation mode and the stimulation effect are associated and bound according to the data characteristics of the electroencephalogram signals, the effectiveness of the stimulation mode can be effectively predicted in vitro or before equipment implantation, and then the stimulation mode is adjusted.

Description

Technical Field

[0001] The present invention belongs to the technical field of physiological electrical signal detection, and specifically relates to an electrical stimulation system, a neural regulation device, and an epilepsy treatment device. Background Art

[0002] Neuromodulation (such as RNS / DBS) is an important treatment for intractable epilepsy, which can detect EEG signals in real time or apply electrical stimulation to the brain in a fixed pattern. In particular, in the process of treating epilepsy with implantable devices, the following steps are mainly divided into: the first step is the precise positioning of the epileptogenic focus (SOZ). Some patients will undergo a stereo electroencephalogram (sEEG) examination and use cortical electrical stimulation (ECS) to locate the functional area and SOZ; the second step is to preset the stimulation mode, and use cortical electrical stimulation or deep electrical stimulation to perform electrical stimulation on the epileptogenic focus (SOZ) to obtain the stimulation parameters and stimulation channels of the electrical stimulation; the third step is closed-loop detection and stimulation, using the preset stimulation mode to achieve closed-loop detection and stimulation after formal implantation. In related technologies, neuromodulation treatment devices generally use seizure data to judge the stimulation effect and then adjust the stimulation mode. Due to individual differences, the seizure frequency of different individuals under the same stimulation mode is also unstable. Therefore, it is difficult to form a stable association between the stimulation mode and the electrical stimulation effect, which leads to the stimulation mode based on historical data being unsuitable for online stimulation. If the stimulation mode is adjusted online, it will take a long time for online training. Summary of the invention

[0003] The present invention provides an electrical stimulation system, a neural regulation device, and an epilepsy treatment device to obtain a stable correlation between a stimulation mode and an electrical stimulation effect, thereby reducing the training time for online adjustment of the stimulation mode.

[0004] In order to solve the above technical problems, the present invention provides an electrical stimulation system, including: a classifier; the input end of the classifier is the input feature of the signal after electrical stimulation, and the output end is the classification result of the effectiveness of electrical stimulation; wherein the input features include the proportion of stimulation channels with energy changes before and after electrical stimulation and\or the situation where the stimulation channels cover strongly connected nodes.

[0005] Furthermore, obtaining the input features of the signal after electrical stimulation includes: constructing a data group, i.e., taking the seizure condition of the subject as the first data group, and taking any data feature of the signal as the second data group; calculating the correlation coefficient between the first data group and the second data group; and sorting the correlation coefficients of the data features to select the data features related to the seizure condition as the input features of the classifier.

[0006] Furthermore, obtaining the proportion of stimulation channels with changes in seizure energy before and after electrical stimulation includes calculating the mean and variance of the energy characteristics of the seizure period before and after electrical stimulation to obtain the proportion of stimulation channels with increased or decreased seizure energy, that is, for signals that do not contain electrical stimulation: directly calculate the mean and variance of the energy characteristics of the seizure period before and after electrical stimulation; for signals that contain electrical stimulation: calculate the mean and variance of the energy characteristics of the seizure period before and after electrical stimulation after processing the stimulation artifacts.

[0007] Furthermore, the processing of stimulation artifacts includes: identifying electrical stimulation, including: differentiating the signal, identifying the position where the differential value exceeds the threshold as an abnormal point, classifying the abnormal points whose adjacent intervals are less than the threshold as the same electrical stimulation, and removing all data from the electrical stimulation period; removing the baseline, including: dividing the original data of the attack period into multiple segments, using a monotonic fitting method to fit the baseline to each segment of the data, and subtracting the fitted baseline from the original data.

[0008] Furthermore, obtaining the situation in which the stimulation channels cover the strongly connected nodes includes constructing a network: taking the stimulation channels as network nodes, the connections between the nodes as the edges of the network, calculating the phase lag index between the stimulation channels as the connection strength of the edge during the attack period; identifying the strongly connected nodes: for each attack period, retaining the edges whose connection strength exceeds the threshold, marking the nodes connected by the edges, and counting the number of times each node appears in all attack periods; marking the nodes whose number of appearances exceeds the threshold as strongly connected nodes; and judging whether the stimulation channels cover the strongly connected nodes.

[0009] Furthermore, it also includes: a processor; the processor trains a classifier based on the input features and the classification results of the effectiveness of electrical stimulation to pre-adjust the stimulation mode of electrical stimulation.

[0010] In a second aspect, the present invention provides a neural regulation device, comprising: electrodes, a stimulation module and the electrical stimulation system; wherein the processor controls the stimulation module to call a corresponding stimulation mode to implement electrical stimulation through the electrodes.

[0011] In a third aspect, the present invention provides an epilepsy treatment device, comprising: a first device, located outside the body, comprising the electrical stimulation system, used to adjust the stimulation pattern of the electrical stimulation; a second device, located outside the body, containing a power supply module; a third device, located inside the body, comprising a stimulation module and an electrode; wherein the third device is wirelessly coupled to the second device to obtain electrical energy through the power supply module; and the third device wirelessly communicates with the first device so that the stimulation module obtains the stimulation pattern.

[0012] The beneficial effect of the present invention is that the electrical stimulation system, neural regulation device, and epilepsy treatment device of the present invention set the input end of the classifier to the input feature of the signal after electrical stimulation, and set the output end to the classification result of the effectiveness of electrical stimulation, and use the correlation coefficient between the data features and the effectiveness of electrical stimulation to obtain the proportion of stimulation channels for the change in seizure energy before and after electrical stimulation and\or the situation of the stimulation channels covering strongly connected nodes, and use this as input features to train the classifier, so that the stimulation mode is associated and bound with the stimulation effect according to the data features of the EEG signal, and the effectiveness of the stimulation mode can be effectively predicted in vitro or before the device is implanted, thereby adjusting the stimulation mode.

[0013] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is the working flow chart of the electrical stimulation system.

[0017] Figure 2 It is the parameter setting interface for training the classifier of the electrical stimulation system.

[0018] Figure 3 It is the parameter setting interface for online stimulation of the electrical stimulation system.

[0019] Figure 4 This is a workflow diagram of the neuromodulation device.

[0020] Figure 5 It is the parameter setting interface of the closed-loop neural regulation device.

[0021] Figure 6 It is the EEG signal waveform of online electrical stimulation of closed-loop neural regulation equipment.

[0022] Figure 7 It is the parameter setting interface of the open-loop neural regulation device.

[0023] Figure 8 This is a block diagram of an epilepsy treatment device.

[0024] Fig. 9 Results of the effectiveness of the cross-validated stimulation patterns are shown. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. 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.

[0026] See Figure 1-Figure 3 , this embodiment provides an electrical stimulation system, including: a processor, a classifier connected to the processor respectively, a human-computer interaction machine, etc. Specifically, the processor trains the classifier according to the input features of the historical signal and the classification results of the effectiveness of the electrical stimulation to pre-adjust the stimulation mode of the electrical stimulation to achieve online detection and electrical stimulation. The input end of the classifier is the input feature of the signal after the electrical stimulation, and the output end is the classification result of the effectiveness of the electrical stimulation; wherein the input features include the proportion of stimulation channels with energy changes before and after the electrical stimulation and\or the situation where the stimulation channels cover strongly connected nodes.

[0027] Optionally, the electrical stimulation system is provided with PC hardware and PC software, and each algorithm step is executed by a processor. The human-computer interaction machine is provided with an operation interface and a display, which can be used to set operating parameters and present waveform diagrams of electrophysiological signals before and after electrical stimulation.

[0028] Taking EEG signals as an example, the application of the electrical stimulation system in evaluating the effect of epilepsy stimulation is described, which specifically includes the following steps.

[0029] Step S1, obtaining input features of the signal after electrical stimulation.

[0030] (1) Obtain the proportion of stimulation channels with changes in seizure energy before and after electrical stimulation. Compare the seizure energy of each channel before and after stimulation to indicate the range of channels affected by stimulation, including: calculating the mean and variance of the energy characteristics of the seizure period before and after electrical stimulation to obtain the proportion of stimulation channels with increased or decreased seizure energy. Specifically, for signals that do not contain electrical stimulation: directly calculate the mean and variance of the energy characteristics of the seizure period before and after electrical stimulation; for signals that contain electrical stimulation: calculate the mean and variance of the energy characteristics of the seizure period before and after electrical stimulation after processing the stimulation artifacts.

[0031] Optionally, for signals that do not contain electrical stimulation: intercept the EEG during the seizure period, perform 1-120 Hz bandpass filtering, calculate the line length and RMS of the EEG as energy features, calculate the energy mean and variance of the entire seizure period as the energy evaluation of the channel during the seizure period.

[0032] Optionally, for signals containing electrical stimulation: intercept the EEG during the seizure period, perform electrical stimulation identification, baseline removal, 1-120Hz bandpass filtering in sequence, and calculate the mean and variance of the EEG energy characteristics. Identifying electrical stimulation includes: firstly differentiating the EEG, identifying the positions where the differential value exceeds 1000 as abnormal points, and considering the abnormal points whose adjacent intervals are less than the threshold to belong to the same electrical stimulation, removing all the data during the electrical stimulation, and dividing the data of the entire seizure period into multiple segments. Removing the baseline includes: using a monotonic fitting method to complete the fitting of the artifact baseline in each segment of the data, and subtracting the fitted baseline from the original data.

[0033] (2) Obtain the situation of strong connection nodes covered by the stimulation channels. Construct the network: take the stimulation channels as network nodes, and the connections between nodes as network edges. Calculate the phase lag index (PLI) between each stimulation channel as the connection strength of the edge during the attack period; Identify strong connection nodes: For each attack period, retain the edges whose connection strength exceeds the threshold (generally set to the 50% with the highest connection strength), mark the nodes connected by the edges, and count the number of times each node appears in all attack periods; mark the nodes whose number of appearances exceeds the threshold as strong connection nodes; Determine whether the stimulation channels cover the strong connection nodes, especially when single-point or multi-point stimulation, whether the stimulation electrodes contain strong connection nodes.

[0034] (3) Obtain the input features of the signal after electrical stimulation. Construct a data group, that is, take the seizure situation of the subject as the first data group, and take any data feature of the signal as the second data group; calculate the correlation coefficient between the first data group and the second data group; sort the correlation coefficients of each data feature to select the data features related to the seizure situation as the input features of the classifier. Specifically, the seizure situation of the subject, such as the seizure frequency reduction rate and the duration reduction rate, can be selected as the positive evaluation index of the stimulation effectiveness, and then select the data feature with the largest correlation coefficient with the seizure situation as the input feature of the classifier, that is, the proportion of stimulation channels of seizure energy change before and after electrical stimulation and\or the situation where the stimulation channel covers the strongly connected nodes, and obtain the model parameters between the stimulation mode, input features, and seizure situation (i.e., stimulation effectiveness) as a stable indicator for evaluating the stimulation mode and the stimulation effect, and store them in the database, as shown in Table 1. The table shows the correlation coefficients between the data features of different EEG signals and the descriptive indicators of the treatment effect (seizure frequency reduction rate, duration reduction rate, stimulation effectiveness). The positive or negative value of the correlation coefficient indicates positive or negative correlation. The absolute value of the correlation coefficient indicates the strength of the correlation between the data feature and the treatment effect. The larger the absolute value of the correlation coefficient, the stronger the correlation between the corresponding data feature and the treatment effect, and the more accurate the description of the treatment effect using this data feature. Among them, the absolute values ​​of the correlation coefficients formed by "stimulating PLI strongly connected nodes" and "seizure frequency reduction rate", "energy increase channel proportion / reduction channel proportion" and "duration reduction rate" are relatively large (>0.8), indicating that these two data features can characterize the treatment effect from different angles.

[0035] Table 1 Stability indicators between input features and stimulation effects.

[0036] Data characteristics Seizure frequency reduction rate Duration reduction rate Stimulus effectiveness PLI pre-stimulation connection strength 0.22 0.35 0.29 Connection strength after PLI stimulation 0.11 0.31 0.12 The rate of change in connection strength before and after PLI stimulation 0.22 0.32 0.24 PLI global efficiency before stimulation 0.32 0.27 0.18 Global efficiency after PLI stimulation 0.06 0.08 0.22 The change rate of global efficiency before and after PLI stimulation 0.15 -0.1 0.25 Interval discharge frequency reduction rate 0.71 0.44 0.56 … … … … Stimulate PLI strong connection nodes 0.91 -0.15 0.45 Multi-point stimulation of PLI strong connection nodes 0.28 -0.44 0.31 Mean connection strength after PLI stimulation 0.23 -0.31 -0.07 Energy increase channel ratio / reduction channel ratio -0.12 -0.81 0.31 Step S2: training the classifier.

[0037] like Figure 2 As shown, the processor trains a classifier based on historical data input features and classification results of electrical stimulation effectiveness to adjust or preset the stimulation mode of electrical stimulation. Optionally, the stimulation mode includes but is not limited to: at least one of stimulation current parameters, stimulation sequence, stimulation time, stimulation frequency (stimulation interval, number of stimulations, rest time, number of cycles), stimulation channel, and stimulation target.

[0038] (1) Reading training data. On the operation interface of the human-computer interaction machine, select the data path - select the result saving path - select the file: start reading the file, read the next one, click the drop-down box - file name list: drop-down box display - waveform display.

[0039] (2) Waveform display. In the waveform browsing in the coordinate graph, select the browsing box operation: drag to adjust the channel, drag to adjust the time, adjust the scale, etc.

[0040] (3) Trigger configuration. Delete or replace the original trigger; add a trigger; save the trigger as a file, read the trigger file, etc.

[0041] (4) Filter parameter configuration: such as low-pass, high-pass, notch parameters, etc.

[0042] (5) Preprocessing and feature extraction. Preprocessing: Windowed baseline removal and filtering; Feature extraction: Extract and save the features used by the model; Read saved files: Check the box that the features have been saved.

[0043] (6) Seizure detection training and adjustment of model parameters. Automatic training: Automatic training according to the existing process, and plot the results and parameters of the optimal channel; Manual parameter change: Manually add or subtract the small window threshold and large window threshold; Specify channel: Plot the results and model parameters of the set channel.

[0044] (7) Preliminary detection training and adjustment of model parameters. Automatic training: Automatic training according to the existing process, and plot the results and parameters of the optimal channel; Manual parameter change: Manually add or subtract the large window threshold; Specified channel: Plot the results and model parameters of the set channel.

[0045] Step S3, online stimulation.

[0046] See Figure 3 , the processor controls the stimulation module online to call the corresponding stimulation mode to implement electrical stimulation through the electrodes. Set the following detection parameters on the operation interface of the human-computer interaction machine: Stimulation sequence: Generate stimulation sequence in automatic mode / manual mode, display all electrodes (such as needle 1-needle 8) and channels on each electrode (such as C1-C64); generate stimulation sequence according to electrodes and channels: single channel, ascending sequence of all channels, descending sequence.

[0047] Parameter configuration, adding stimulation channels: setting and adding electrode pairs for stimulation; parameter adjustment: stimulation interval, stimulation duration, stimulation current intensity, stimulation pulse width, pulse interval, stimulation frequency, series stimulation cycle, pulse duration, stimulation intensity step; parameter delivery: delivery to single / all electrode pairs.

[0048] Stimulation sequence configuration, stimulation sequence: list all stimulation pairs; number of cycle stimulation: stimulate all stimulation pairs, set the number of cycles; generate trigger, display specific parameters; delete stimulation pair: delete selected, delete all; trigger stimulation: stimulate the next channel pair, automatically stimulate in sequence, stimulate all at the same time, stimulate a single channel pair.

[0049] Discharge parameter configuration, manual discharge: duration, discharge channel; automatic discharge: discharge all channels after each stimulation.

[0050] Device status: Stimulation Box connection status.

[0051] Stimulus mark trigger, stimulus name: stimulus channel pair; stimulus status: stimulus start / stimulation end; time: current trigger time; stimulus information: record stimulus parameters.

[0052] In some embodiments, a neuromodulation device is provided, comprising: electrodes, a stimulation module, and the electrical stimulation system; wherein the processor controls the stimulation module to call a corresponding stimulation mode to implement electrical stimulation through the electrodes. Figure 4 The neuroregulatory device is divided into open-loop (direct stimulation without online detection) and closed-loop (deciding whether to implement stimulation after online detection). It uses EEG characteristics to predict the patient's treatment effect, guide the electrode placement of the neuroregulatory device, and can guide the initial stimulation parameters to quickly verify the short-term treatment effect.

[0053] (1) Closed-loop neural regulation equipment.

[0054] See Figure 5 The workflow of the closed-loop neural regulation device includes: configuring sEEG device parameters, setting preprocessing parameters, seizure detection parameters, pre-seizure detection parameters, protection parameters, configuring ECS ​​device parameters, selecting stimulation targets, confirming and starting. The specific operation interface of the human-computer interaction machine is as follows.

[0055] Basic configuration: whether to save data, save path, file name (not save); current number of channels of sEEG device, sampling rate (134, 2000); detection parameters: detection channel number, lead mode (81, bipolar lead); whether to output stimulation command: only collect (do not send stimulation command to the outside), stimulate (call stimulation mode).

[0056] Preprocessing parameter configuration, baseline drift: subtract the starting 0.1s mean value (0.1) from each segment of data; low pass, high pass, notch: filter coefficients a and b.

[0057] Seizure detection classifier parameter configuration, minimum window threshold 1, 2: 0.1s window energy during seizure and spike wave (9000, 4750); seizure and spike wave window threshold: number of consecutive abnormal windows during seizure and spike wave (9, 8).

[0058] Pre-ictal detection classifier parameter configuration, parameter 1, parameter 2: pre-ictal detection classifier parameter vector, such as w and c of LDA; continuous window number threshold: in the pre-ictal period, the number of continuous abnormal windows (6).

[0059] Stimulation protection parameters, stimulation interval, number of stimulations: the output stimulation in 300s cannot exceed 10 times (300, 10).

[0060] Acquisition and stimulation equipment parameters, sEEG equipment: equipment IP and communication port (127.0.0.1, 8000); ECS equipment: equipment IP and communication port (127.0.0.1, 9000).

[0061] Select stimulation target, Stimulate onset, Stimulate pre-onset, Stimulate spikes: detect and stimulate onset and pre-onset.

[0062] Electrical stimulation: detect epileptic seizures online and automatically apply electrical stimulation until the seizure stops. Figure 6 Closed-loop stimulation results are shown for the patient during the first and second episodes.

[0063] (2) Open-loop neural control equipment.

[0064] See Figure 7 The workflow of the open-loop neural control device includes: configuring sEEG device parameters, setting preprocessing parameters, protection parameters, open-loop parameters, and configuring ECS ​​device parameters. Figure 8 As shown, the following parameters can be set on the operation interface of the human-computer interaction machine.

[0065] Basic configuration: whether to save data, save path, file name (not save); current number of channels and sampling rate of sEEG device (115, 2000).

[0066] Preprocessing parameter configuration, baseline drift: subtract the starting 0.1s mean value (0.1) from each segment of data; low pass, high pass, notch: filter coefficients a and b.

[0067] Seizure detection classifier parameter configuration: minimum window threshold 1, 2; seizure and spike window threshold.

[0068] Preictal detection classifier parameter configuration: parameters 1, 2; continuous window number threshold.

[0069] Stimulation protection parameters, stimulation interval, number of stimulations: the output stimulation in 300s cannot exceed 10 times (300, 10).

[0070] Open-loop parameter configuration, such as stimulation interval, number of stimulations, rest time, and number of cycles: stimulation 1s, interval 9s, stimulation 6 times, rest 300s, and cycle 100 times (9, 6, 300, 100).

[0071] Acquisition and stimulation equipment parameters, sEEG equipment: equipment IP and communication port (127.0.0.1, 8000); ECS equipment: equipment IP and communication port (127.0.0.1, 9000).

[0072] Select stimulation target: Stimulate onset, Stimulate pre-onset, Stimulate spikes: detect and stimulate onset and pre-onset.

[0073] See Figure 8 In some embodiments, an epilepsy treatment device is provided, comprising: a first device, located outside the body, comprising the electrical stimulation system, for adjusting the stimulation pattern of electrical stimulation; a second device, located outside the body, comprising a power supply module; a third device, located inside the body, comprising a stimulation module and an electrode; wherein the third device is wirelessly coupled to the second device to obtain electrical energy through the power supply module; and the third device wirelessly communicates with the first device so that the stimulation module obtains the stimulation pattern.

[0074] Specifically, when the stimulation mode needs to be adjusted, the third device can be wirelessly communicated with the first device to adjust the stimulation mode of electrical stimulation using the first device; thereafter, the third device is disconnected from the first device, and the electrical stimulation is implemented using the adjusted stimulation mode. In other words, the epilepsy treatment device in this case forwards, processes, and classifies EEG signals in real time, and controls the application of electrical stimulation based on the results. Before the third device is formally implanted, it can use historical data to verify the various algorithms in the third device in vitro, and use EEG features to bind the stimulation mode, thereby confirming the neuromodulatory treatment effect. Before and after the third device is formally implanted, the third device is wirelessly coupled to the second device to obtain electrical energy through the energy supply module, and then the preset stimulation mode can be used to achieve online detection and stimulation, without the need for a large amount of machine training after implantation.

[0075] Comparison of test results.

[0076] Taking multiple epilepsy patients (01-10) as an example, the proportion of stimulation channels with changes in seizure energy before and after electrical stimulation and\or the situation of stimulation channels covering strongly connected nodes are used as the input features of the classifier, and whether long-term neural regulation is effective is used as the output label of the classifier to train the K-nearest neighbor algorithm (KNN) classifier. Among them, feature 1 (proportion of channels with increased seizure energy / proportion of channels with reduced seizure energy) and feature 2 (the situation of stimulation channels covering strongly connected nodes, that is, whether key nodes are stimulated) are used as the second data group, and the confusion matrix is ​​constructed as the first data group to predict whether the treatment is effective (reduced seizure duration or reduced seizure frequency), as shown in Table 2.

[0077] Table 2 Elements of the confusion matrix.

[0078] patient 01 02 03 04 05 06 07 08 09 10 Feature 1 1 1 0 1 1 1 1 1 1 1 Feature 2 0.79 0.58 0.37 0.70 0.99 0.40 0.47 0.11 0.99 0.86 Effectiveness 1 0 0 1 0 1 1 1 0 1 like Fig. 9 As shown, the confusion matrix of the recognition results of the constructed simple classification model is used to describe the classification accuracy of the model to cross-validate the effectiveness results of the stimulation pattern. Fig. 9 Middle: Patients with no treatment effect are marked as 0, and patients with treatment effect are marked as 1. The model has a recognition accuracy of 100% (6 / 6) for patients with treatment effect; a classification accuracy of 50% (2 / 4) for patients with no treatment effect; and an overall accuracy of 80% (8 / 10). This shows that the classifier constructed using these two input features has a certain classification ability. After further data collection, the accuracy can still be improved. From this, we can see that: (1) Based on the short-term treatment effect during sEEG, the classifier can effectively predict whether the stimulation mode (i.e., treatment plan) is suitable for the patient.

[0079] (2) The therapeutic effect can be improved by stimulating key points.

[0080] (3) By checking whether energy reduction occurs after short-term treatment, it can be used to adjust the stimulation location / stimulation parameters.

[0081] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0082] If the function or model is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0084] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. An electrical stimulation system, comprising: A classifier; characterized in that The input end of the classifier is the input feature of the signal after electrical stimulation, and the output end is the classification result of the effectiveness of electrical stimulation; The input features include the proportion of stimulation channels with energy changes before and after electrical stimulation and\or the situation where the stimulation channels cover strongly connected nodes; The input features of the signal after electrical stimulation include: Constructing a data set, that is, taking the seizure condition of the subject as the first data set, and taking any data feature of the signal as the second data set; Calculating a correlation coefficient between the first data set and the second data set; The correlation coefficients of the data features are sorted to select the data features related to the attack conditions as the input features of the classifier.

2. The electrical stimulation system according to claim 1, characterized in that: The proportion of stimulation channels for obtaining the change in seizure energy before and after electrical stimulation includes: The mean and variance of the energy characteristics of the seizure period before and after electrical stimulation were calculated to obtain the proportion of stimulation channels with increased or decreased seizure energy, i.e. For signals that do not contain electrical stimulation: directly calculate the mean and variance of the energy characteristics of the attack period before and after electrical stimulation; For signals containing electrical stimulation: after processing the stimulation artifacts, calculate the mean and variance of the energy characteristics of the attack period before and after the electrical stimulation.

3. The electrical stimulation system according to claim 2, characterized in that: The processing of stimulation artifacts includes: Identifying electrical stimulation, including: performing differential analysis on the signal, identifying the position where the differential value exceeds a threshold as an abnormal point, classifying the abnormal points whose adjacent intervals are less than the threshold as the same electrical stimulation, and removing all data of the electrical stimulation period; The baseline is removed, including: dividing the original data of the attack period into multiple segments, fitting the baseline of each segment of data using a monotone fitting method, and subtracting the fitted baseline from the original data.

4. The electrical stimulation system according to claim 1, characterized in that: The situations where the stimulation channel covers the strongly connected nodes include: Construct the network: take the stimulation channels as network nodes, the connections between nodes as network edges, and calculate the phase lag index between each stimulation channel as the connection strength of the edge during the attack period; Identify strongly connected nodes: For each attack period, retain the edges whose connection strength exceeds the threshold, mark the nodes connected by the edges, and count the number of times each node appears in all attack periods; mark the nodes whose number of appearances exceeds the threshold as strongly connected nodes; Determine whether the stimulation channel covers a strongly connected node.

5. The electrical stimulation system according to claim 1, characterized in that: Also includes: processor; The processor trains a classifier based on the input features and the classification result of the electrical stimulation effectiveness to adjust a stimulation pattern of the electrical stimulation.

6. A neural regulation device, characterized in that: include: An electrode, a stimulation module and an electrical stimulation system as claimed in claim 5; in The processor controls the stimulation module to call a corresponding stimulation mode to implement electrical stimulation through the electrodes.

7. An epilepsy treatment device, characterized in that: include: A first device, located outside the body, comprising the electrical stimulation system according to any one of claims 1 to 5, for pre-adjusting a stimulation pattern of electrical stimulation; The second device, located outside the body, contains the energy supply module; a third device, located in the body, comprising a stimulation module and electrodes; in The third device is wirelessly coupled to the second device to obtain electrical energy through the energy supply module; The third device communicates wirelessly with the first device so that the stimulation module acquires the stimulation pattern.

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