WO2022236749A1

WO2022236749A1 - Method and apparatus for detecting abnormal discharge of electroencephalogram, and medium and device

Info

Publication number: WO2022236749A1
Application number: PCT/CN2021/093431
Authority: WO
Inventors: 王星; 王京鹤; 邹元庆; 朱朗宁
Original assignee: 北京太阳电子科技有限公司
Priority date: 2021-05-12
Filing date: 2021-05-12
Publication date: 2022-11-17
Also published as: CN116390685A

Abstract

A method and apparatus for detecting abnormal discharge of an electroencephalogram, and a medium and a device. The method comprises: acquiring an original electroencephalogram signal (101); performing hyperpolarization detection on the original electroencephalogram signal, so as to obtain a predicted discharge position (102); acquiring a montage signal from the original electroencephalogram signal, and then performing discharge detection on the montage signal according to the predicted discharge position (103); and generating a discharge probability graph that includes discharge position information and discharge probability information (104). By means of the method, apparatus, medium and device, automatic detection of abnormal discharge of an electroencephalogram is realized, and the detection accuracy is improved.

Description

Method, device, medium and equipment for detecting abnormal brain wave discharge

technical field

Embodiments of the present disclosure relate to the technical field of EEG detection, and embodiments of the present disclosure relate to, but are not limited to, a method, device, medium, and equipment for detecting abnormal discharge of EEG.

Background technique

Electroencephalogram (electroencephalogram, EEG) is a sensitive indicator for evaluating brain function and central nervous system status, and is widely used in the research and diagnosis of central nervous system diseases and mental diseases. For example, applied to the qualitative and localization of epilepsy and other paroxysmal brain function abnormalities, EEG is a diagnostic technique that cannot be replaced by other methods.

There are various abnormal waveforms in EEG, which are mainly divided into background activity abnormalities and paroxysmal abnormalities in clinical practice. Among them, background activity abnormalities include normal rhythm changes, slow wave abnormalities, etc.; and paroxysmal abnormalities include epileptiform discharges, rhythmic bursts, and periodic waves. Epileptiform discharges (defined with reference to IFSECN), as a biomarker of epilepsy, are caused by rapid hypersynchronous depolarization of a group of neurons, reflecting abnormally increased excitability of neurons. Epileptiform discharges play a very important role in the daily diagnosis and treatment of epilepsy, but due to their low frequency, doctors spend a lot of time in daily EEG interpretation to find epileptiform discharges. In addition, the lack of human resources for interpreting EEG in hospitals leads to low ability to interpret EEG.

Contents of the invention

The following is an overview of the topics described in detail in this article. This summary is not intended to limit the scope of the claims.

Embodiments of the present disclosure provide a method, device, medium and equipment for detecting abnormal discharge of brain waves.

According to the first aspect of an embodiment of the present disclosure, a method for detecting abnormal brain wave discharge is provided, the method comprising:

Obtain the original EEG signal;

Performing hyperpolarization detection on the original EEG signal to obtain a predicted discharge position;

Obtaining a lead combination signal from the original EEG signal, and performing discharge detection on the lead combination signal according to the predicted discharge position;

A discharge probability map including discharge location information and discharge probability information is generated.

In some embodiments, said obtaining the original EEG signal also includes:

The original EEG signal is the data collected by the EEG instrument through the reference electrode.

In some embodiments, the hyperpolarization detection of the original EEG signal to obtain the predicted discharge position further includes:

performing filtering processing on each channel data of the original EEG signal to obtain the lead data;

superimposing the lead data to obtain the superimposed waveform;

Performing hyperpolarization detection on the superimposed waveform to obtain a hyperpolarization probability map;

Based on the hyperpolarization probability map, the predicted discharge location is determined.

In some embodiments, performing hyperpolarization detection on the superimposed waveform to obtain the hyperpolarization detection in the hyperpolarization probability map includes:

Selecting the waveform position whose recall rate of the feature parameter of the superimposed waveform exceeds 0.99;

combining the waveform positions;

Obtain the hyperpolarization probability map;

In some embodiments, the characteristic parameters include the height of the waveform, the rising duration of the peak, the falling duration of the peak, and the angle of the peak.

In some embodiments, the obtaining the lead combination signal from the original EEG signal, and performing discharge detection on the lead combination signal according to the predicted discharge position further includes:

According to the original EEG signal, a bipolar reference EEG signal, an ear pole reference EEG signal and an average reference EEG signal are obtained, wherein the bipolar reference EEG signal, the ear pole reference EEG signal and the The average reference EEG signal jointly forms the lead combination signal;

Determining the number of detectors according to the number of EEG signals included in the lead combination signal, where the number of detectors corresponds to the number of EEG signals included in the lead combination signal;

Using a plurality of the detectors to respectively perform discharge detection on positions corresponding to the predicted discharge positions in the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal.

According to the second aspect of the embodiments of the present disclosure, there is also provided a device for detecting abnormal discharge of brain waves, the device includes an acquisition module, and the acquisition module is configured to:

Get the original EEG signal.

In some embodiments, the apparatus further includes a processing module configured to:

Filtering each channel data of the original EEG signal to obtain the lead data; superimposing the lead data to obtain the superimposed waveform; performing hyperpolarization detection on the superimposed waveform to obtain A hyperpolarization probability map; determining the predicted discharge location according to the hyperpolarization probability map.

In some embodiments, the apparatus further includes a detection module configured to:

According to the original EEG signal, a bipolar reference EEG signal, an ear pole reference EEG signal and an average reference EEG signal are obtained, wherein the bipolar reference EEG signal, the ear pole reference EEG signal and the The average reference EEG signals jointly form the lead combination signal; according to the number of EEG signals contained in the lead combination signal, the number of detectors is determined, and the number of detectors is related to the lead combination signal The number of included EEG signals corresponds to each other; using a plurality of detectors to respectively compare the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal with the The discharge detection is performed at the position corresponding to the predicted discharge position.

In some embodiments, the apparatus further includes a generating module configured to:

A third aspect of the embodiments of the present disclosure provides a computer-readable storage medium, where a computer program is stored on the storage medium, and when the program is executed by a processor, the method described in the above-mentioned first aspect is implemented.

A fourth aspect of the embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the program when executing the program. The method of the first aspect above.

A method, device, medium, and equipment for detecting abnormal brain wave discharge in the embodiments of the present disclosure can achieve the following beneficial effects:

The automatic detection of epileptiform discharge is realized, and the candidate location of abnormal discharge is determined by using the hyperpolarization probability map, which can greatly reduce the detection complexity and the required calculation time. Since the candidate position of the abnormal discharge in the early stage is located by the automatic detection method, the labor cost is saved.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with the description, serve to explain principles of the embodiments of the disclosure. In the drawings, like reference numerals are used to denote like elements. The drawings in the following description are some, but not all, embodiments of the present disclosure. Those skilled in the art can obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram of a scene for detecting abnormal discharge of brain waves according to an embodiment of the present disclosure.

Fig. 2 is a flow chart of a method for detecting abnormal brain wave discharge according to an embodiment of the disclosure.

Fig. 3 is a block diagram of a device for detecting abnormal brain wave discharge according to an embodiment of the disclosure.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments It is a part of the embodiments of the present disclosure, but not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present disclosure. It should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined arbitrarily with each other.

At present, in the process of diagnosis and treatment, it takes a lot of manpower to interpret the EEG of epileptiform discharges. However, in the process of interpretation, the relevant personnel are often misjudged or missed due to the influence of the interpretation experience of the personnel.

The embodiment of the present disclosure proposes a method for detecting abnormal discharge of brain waves. Among them, using the form of the hyperpolarization probability map to determine the selection of the candidate positions of the abnormal discharge can greatly reduce the overall computational complexity and greatly reduce the computational time.

An embodiment of the present disclosure provides a method for detecting abnormal discharge of brain waves, as shown in FIG. 2 , the method includes:

Step 101, obtaining the original EEG signal;

Step 102, performing hyperpolarization detection on the original EEG signal to obtain the predicted discharge position;

Step 103, obtaining the lead combination signal from the original EEG signal, and performing discharge detection on the lead combination signal according to the predicted discharge position;

Step 104, generating a discharge probability map including discharge location information and discharge probability information.

In the embodiment of the present disclosure, when step 101 is performed, the original EEG signal of the human body can be obtained by using a digital multifunctional EEG instrument. The digital multifunctional EEG instrument has system default settings stored in it, and the user collects signals by placing a patch on the head of the person to be collected. Therefore, the user only needs to perform simple operations to realize EEG collection. The reference electrodes used in the process of collecting EEG signals can directly use the preset system reference electrodes on the digital multifunctional EEG instrument, which is convenient for operation. The collected EEG signals will be stored in the digital multifunctional EEG instrument, and the collected original EEG signals are discrete data. For different EEG signal acquisition scenarios, different sampling rates can be used for sampling. Among them, the commonly used sampling rate for collecting EEG signals through the scalp patch method can be 512HZ, 256HZ, etc.

Among them, the original EEG signal can be according to the electrode placement method of the international 10-20 system, connect multiple electrodes to various positions of the head, and collect the EEG signals at the positions where the multiple electrodes are located. As shown in Figure 1, the international 10-20 system involves 19 recording electrodes: Fp1, Fp2, F7, F3, FZ, F4, F8, T3, C3, CZ, C4, T4, T5, P3, PZ, P4, T6 , 01, 02. In step 101, the EEG data obtained through the system reference electrode can be used as the EEG raw data, and the data of other different reference electrodes can be calculated through the EEG raw data. For example, in an exemplary scenario, the system reference electrode can be taken as F3/F4 or C3/C4. In the embodiment of the present disclosure, the collection position of the original EEG signal is not limited to the 19 recording electrode positions stipulated by the international 10-20 system, and may include any one or more positions of the 19 recording electrode positions shown in Figure 1, or Any other location of the human brain may be included, and the present disclosure is not limited here.

Above-mentioned step 102 also comprises:

Step 1021, filtering each channel data of the original EEG signal to obtain lead data;

Step 1022, superimposing the lead data to obtain superimposed waveforms;

Step 1023, performing hyperpolarization detection on the superimposed waveform to obtain a hyperpolarization probability map;

Step 1024, determine the predicted discharge location according to the hyperpolarization probability map.

Wherein, in step 1021, performing filtering processing on each channel data of the original EEG signal includes:

Perform power frequency filtering on each channel data of the original EEG signal, the power frequency signal is generally 50HZ, and then pass the power frequency filtered EEG signal through a 0.5HZ-75HZ band-pass filter, after passing through the band-pass filter , to get the lead data.

Wherein, the hyperpolarization detection in step 1023 includes:

The waveform position whose recall rate of the characteristic parameters of the superimposed waveform exceeds 0.99 is selected, wherein the recall rate refers to the probability that the abnormal discharge position of the EEG in the superimposed waveform can be correctly identified in the hyperpolarization detection. Combining the positions of the waveforms; obtaining a hyperpolarization probability map; wherein, the characteristic parameters include but are not limited to the height of the waveform, the duration of the rise of the peak, the duration of the fall of the peak, and the angle of the peak. In addition, the recall rate can be set according to different needs, and the setting value of the recall rate can also be obtained by using a machine learning method, which is not limited in the present disclosure.

Above-mentioned step 103 also comprises:

Step 1031, according to the original EEG signal, obtain the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal, wherein, the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal The signals together form the lead combination signal.

In step 1031, the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal are obtained by the bipolar reference lead (bipolar montage), the ear pole reference lead (earlobe referential montage) and the average reference lead lead respectively. (averaged referential montage) obtained.

Among them, the bipolar reference lead is recorded by a pair of probe electrodes. In all bipolar leads, no common electrode is connected to each lead. The configuration of the bipolar lead group is mostly a chain connection, that is, along the adjacent electrodes arranged in the same way, one electrode is shared, connected to the input terminal of one amplifier and connected to the input terminal of another amplifier, as shown in Figure 1 , such as frontal-central area (such as the area enclosed by FZ-CZ), central area-apical area (such as the area enclosed by CZ-PZ), etc., can avoid the graphic distortion caused by the activation of the reference electrode. And in the case of focal discharge, a special EEG pattern-phase inversion can be formed.

The ear pole reference lead uses the two earlobes as reference electrodes, also known as a unipolar lead, and the reference electrode is a lead composed of the common electrodes of most leads. All recording electrodes are connected to the negative terminal of the amplifier, and the reference electrode is connected to the positive terminal; the average reference lead connects each recording electrode on the scalp (only each electrode placed) in series with a resistor of 1-2 MΩ, and then connects them in parallel , After this treatment, the potential of each point of the scalp is significantly weakened and averaged, and the potential is theoretically close to zero.

The embodiments provided in the present disclosure can improve the interpretation ability of EEG and improve the accuracy of interpretation. By using the lead combination waveform diagram, comprehensive analysis is carried out according to the different manifestations of the lead combination waveform diagram in different lead situations, and the discharge detection is performed according to the discharge activation logic rules, which significantly reduces the false alarm rate and improves the accuracy of epilepsy detection. Spend.

Step 1032: Determine the number of detectors according to the number of EEG signals included in the lead combination signal, and the number of detectors corresponds to the number of EEG signals included in the lead combination signal.

Step 1033 , using a plurality of detectors to respectively perform discharge detection on the positions corresponding to the predicted discharge positions in the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal.

Wherein, the detector is a single-channel multi-type waveform classifier corresponding to the lead data. The waveform classifier classifies segmental EEG into epileptiform discharges, slow waves, artifacts and other four categories. When calculating the discharge probability map, first calculate the probability of the occurrence of the artifact image, and partially filter the artifact, and then compare the occurrence probability position of the artifact image with the discharge position in the hyperpolarization probability map, and further analyze Artifacts are filtered to improve detection accuracy.

The embodiment provided by the present disclosure further filters the artifacts through the difference between the occurrence logic of the artifacts and the discharge occurrence logic when calculating the discharge probability map, and solves the problem of low accuracy caused by errors in the detection equipment.

The embodiment of the present disclosure also provides a device for detecting abnormal discharge of brain waves. As shown in FIG. 3 , the device includes an acquisition module 201, and the acquisition module 201 is configured to:

Get the original EEG signal.

Wherein, the device further includes a processing module 202, and the processing module 202 is configured to:

Filter the data of each channel of the original EEG signal to obtain the lead data; superimpose the lead data to obtain the superimposed waveform; perform hyperpolarization detection on the superimposed waveform to obtain a hyperpolarization probability map; according to the hyperpolarization Probability map to identify predicted discharge locations.

Wherein, the device also includes a detection module 203, and the detection module 203 is configured to:

According to the original EEG signal, the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal are obtained, wherein the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal are jointly formed Lead combination signal; determine the number of detectors according to the number of EEG signals contained in the lead combination signal, and the number of detectors corresponds to the number of EEG signals contained in the lead combination signal; using multiple detection The device performs discharge detection on the position corresponding to the predicted discharge position in the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal respectively.

Wherein, the device further includes a generating module 204, and the generating module 204 is configured to:

An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

An embodiment of the present disclosure also provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the program, the steps of the above method are realized.

The embodiments of the present disclosure can save precious time for doctors to search for epileptiform discharges, and realize automatic detection of epileptiform discharges. By using the form of the hyperpolarization probability map to determine the selection of the candidate positions of the abnormal discharge, the overall computational complexity is greatly reduced, the computational time is greatly reduced, and the labor cost is saved.

Those skilled in the art should understand that the embodiments of the present disclosure may be provided as a method, an apparatus (device), or a computer program product. Accordingly, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data , including but not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can be used in Any other medium, etc. that stores desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the present disclosure. It should be understood that each procedure and/or block in the flowchart and/or block diagrams, and a combination of procedures and/or blocks in the flowchart and/or block diagrams can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more processes of the flowchart and/or one or more blocks of the block diagram

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

As used herein, the terms "comprises", "comprises" or any other variation thereof are intended to cover a non-exclusive inclusion such that an article or device comprising a set of elements includes not only those elements but also other elements not expressly listed. elements, or also elements inherent in such articles or equipment. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the article or device comprising said element.

While preferred embodiments of the present disclosure have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once the basic inventive concept is appreciated. Therefore, it is intended that the appended claims be construed to cover the preferred embodiment and all changes and modifications which fall within the scope of the present disclosure.

Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. In this way, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and equivalent technologies thereof, the intent of the present disclosure is to also include these modifications and variations.

Industrial Applicability

The embodiments of the present disclosure provide a method, device, medium, and equipment for detecting abnormal brain wave discharges, which can solve the problem of consuming a lot of doctors' precious time to find epileptiform discharges in the prior art, and the ability of basic-level interpretation is low and the accuracy is not good. high question. In addition, the detection of abnormal discharge of brain waves by means of maximizing the probability map can not only accurately obtain the location of abnormal discharge of brain waves, but also reduce the burden of manpower, save time, and better improve the quality of medical services.

Claims

A method for detecting abnormal electroencephalogram discharge, characterized in that,

Obtain the original EEG signal;

Performing hyperpolarization detection on the original EEG signal to obtain a predicted discharge position;

Obtaining a lead combination signal from the original EEG signal, and performing discharge detection on the lead combination signal according to the predicted discharge position;

A discharge probability map including discharge location information and discharge probability information is generated.
The method for detecting abnormal EEG discharge according to claim 1, wherein the hyperpolarization detection of the original EEG signal to obtain a predicted discharge position includes:

Superimposing the lead data in the original EEG signal to obtain a superimposed waveform;

Performing hyperpolarization detection on the superimposed waveform to obtain a hyperpolarization probability map;

Based on the hyperpolarization probability map, the predicted discharge location is determined.
The method for detecting abnormal EEG discharge according to claim 2, wherein the superimposing the lead data in the original EEG signal to obtain a superimposed waveform includes:

performing filtering processing on each channel data of the original EEG signal to obtain the lead data;

Superimpose the lead data to obtain the superimposed waveform.
The method for detecting abnormal EEG discharge according to claim 2 or 3, wherein the hyperpolarization detection is performed on the superimposed waveform to obtain a hyperpolarization probability map, including:

Selecting the waveform position whose recall rate of the feature parameter of the superimposed waveform exceeds 0.99;

combining the waveform positions;

Obtain the hyperpolarization probability map;

Wherein, the characteristic parameters include the height of the waveform, the rising time of the peak, the falling time of the peak and the angle of the peak.
The method for detecting abnormal EEG discharge according to claim 1, wherein the lead combination signal is obtained from the original EEG signal, and the discharge detection is performed on the lead combination signal according to the predicted discharge position ,include:

According to the original EEG signal, a bipolar reference EEG signal, an ear pole reference EEG signal and an average reference EEG signal are obtained, wherein the bipolar reference EEG signal, the ear pole reference EEG signal and the The average reference EEG signal jointly forms the lead combination signal;

performing discharge detection on a position corresponding to the predicted discharge position in the lead combination signal.
The method for detecting abnormal EEG discharge according to any one of claims 1-5, wherein the discharge detection of the position corresponding to the predicted discharge position in the lead combination signal comprises:

Determining the number of detectors according to the number of EEG signals included in the lead combination signal, where the number of detectors corresponds to the number of EEG signals included in the lead combination signal;

Using multiple detectors to respectively perform discharge detection on positions corresponding to the predicted discharge positions in the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal.
The method for detecting abnormal EEG discharge according to claim 1, wherein the original EEG signal is discrete sampling data with a preset frequency; and/or,

The original EEG signal is the data collected by the EEG instrument through the reference electrode.
A device for detecting abnormal electroencephalogram discharge, characterized in that,

An acquisition module, configured to acquire the original EEG signal;

A processing module, configured to perform hyperpolarization detection on the original EEG signal to obtain a predicted discharge position;

A detection module, configured to obtain a lead combination signal from the original EEG signal, and perform discharge detection on the lead combination signal according to the predicted discharge position;

A generating module, configured to generate a discharge probability map including discharge location information and discharge probability information.
The device for detecting abnormal brain wave discharge according to claim 8, wherein the processing module is specifically used for:

Superimposing the lead data in the original EEG signal to obtain a superimposed waveform;

Performing hyperpolarization detection on the superimposed waveform to obtain a hyperpolarization probability map;

Based on the hyperpolarization probability map, the predicted discharge location is determined.
The device for detecting abnormal discharge of brain waves according to claim 9, wherein the processing module is specifically used for:

performing filtering processing on each channel data of the original EEG signal to obtain the lead data;

Superimpose the lead data to obtain the superimposed waveform.
The device for detecting abnormal discharge of brain waves according to claim 9 or 10, wherein the processing module is specifically used for:

Selecting the waveform position whose recall rate of the feature parameter of the superimposed waveform exceeds 0.99;

combining the waveform positions;

Obtain the hyperpolarization probability map;

Wherein, the characteristic parameters include the height of the waveform, the rising time of the peak, the falling time of the peak and the angle of the peak.
The device for detecting abnormal brain wave discharge according to claim 8, wherein the detection module is specifically used for:

According to the original EEG signal, a bipolar reference EEG signal, an ear pole reference EEG signal and an average reference EEG signal are obtained, wherein the bipolar reference EEG signal, the ear pole reference EEG signal and the The average reference EEG signal jointly forms the lead combination signal;

performing discharge detection on a position corresponding to the predicted discharge position in the lead combination signal.
The device for detecting abnormal brain wave discharge according to any one of claims 8-12, wherein the detection module is specifically used for:

Determining the number of detectors according to the number of EEG signals included in the lead combination signal, where the number of detectors corresponds to the number of EEG signals included in the lead combination signal;

Using multiple detectors to respectively perform discharge detection on positions corresponding to the predicted discharge positions in the bipolar reference EEG signal, the ear pole reference EEG signal and the average reference EEG signal.
The device for detecting abnormal EEG discharge according to claim 1, wherein the original EEG signal is discrete sampling data with a preset frequency; and/or,

The original EEG signal is the data collected by the EEG instrument through the reference electrode.
A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method according to any one of claims 1 to 7 is realized.
A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, the computer program described in any one of claims 1 to 7 is realized. described method.