Electroencephalogram anomaly analysis method, device and equipment and storage medium

Abstract

The invention relates to an electroencephalogram anomaly analysis method, device and equipment and a storage medium, and belongs to the technical field of electroencephalogram analys.The method comprises the steps that first electroencephalogram data and first related parameters, collected by electroencephalogram collection equipment, of a current user are obtained, the first related parameters comprise basic information of the current user and acquisition time of the first electroencephalogram data; acquiring a second related parameter of a historical user in an electroencephalogram threshold database, wherein the second related parameter comprises basic information of the historical user and a time interval corresponding to an electroencephalogram threshold range; selecting a second relevant parameter matched with the first relevant parameter; determining an electroencephalogram threshold range based on the selected second related parameters, and taking the determined electroencephalogram threshold range as a reference threshold range; and comparing the first electroencephalogram data with a reference threshold range, and when the first electroencephalogram data is not in the reference threshold range, generating early warning information. The method has the effect of reducing the possibility of misdiagnosis or missed diagnosis.

Description

Technical Field

[0001] The present application relates to the technical field of electroencephalogram (EEG) analysis, and in particular to an EEG abnormality analysis method, device, equipment and storage medium. Background Art

[0002] Electroencephalography (EEG) is a method of examining the activity of brain neurons by recording the brain's electrical activity captured by electrodes on the scalp. It is widely used in the treatment of neurological diseases and the study of brain neurobiological mechanisms.

[0003] Generally speaking, abnormal EEG is one of the important manifestations of neurological diseases or abnormal brain function. Abnormal EEG refers to the deviation of the EEG from the normal range. Common clinical diseases with abnormal EEG include encephalitis, meningitis, epilepsy, cerebral ischemia and hypoxia, etc.

[0004] At present, when analyzing EEG, a fixed normal range value is generally used. However, different people may correspond to different normal ranges. If everyone uses the same normal range value to analyze the EEG, misdiagnosis or missed diagnosis may occur, affecting the accuracy of the analysis. Summary of the invention

[0005] In order to reduce the possibility of misdiagnosis or missed diagnosis, the present application provides an electroencephalogram abnormality analysis method, device, equipment and storage medium.

[0006] In the first aspect, the present application provides a method for analyzing abnormal electroencephalograms, which adopts the following technical solution:

[0007] A method for analyzing abnormal electroencephalogram (EEG), comprising:

[0008] Acquire first electroencephalogram data and first related parameters of the current user acquired by an electroencephalogram acquisition device, wherein the first related parameters include basic information of the current user and acquisition time of the first electroencephalogram data;

[0009] Acquire a second related parameter of a historical user in the electroencephalogram threshold database, wherein the second related parameter includes basic information of the historical user and a time interval corresponding to the electroencephalogram threshold range;

[0010] Selecting a second relevant parameter that matches the first relevant parameter;

[0011] determining an electroencephalogram threshold range based on the selected second related parameter, and using the determined electroencephalogram threshold range as a reference threshold range;

[0012] The first electroencephalogram data is compared with the reference threshold range, and when the first electroencephalogram data is not within the reference threshold range, a warning message is generated.

[0013] By adopting the above technical solution, by obtaining the first relevant parameter of the current user and the acquisition time of the first EEG data, matching similar historical users through the first relevant parameter and the second relevant parameter, and determining the reference threshold range of the current user through the EEG threshold range of similar historical users, the selected reference threshold range is more in line with the actual state of the current user. Compared with the method in which all people use the same normal range value to analyze the EEG, the possibility of misdiagnosis and missed diagnosis is reduced to a certain extent.

[0014] Optionally, the basic information includes age, gender and disease type; and the selecting of the second relevant parameter matching the first relevant parameter includes:

[0015] Converting the gender and disease type into first information to be calculated and second information to be calculated based on preset rules;

[0016] Acquire the third information to be calculated corresponding to the time interval to which the acquisition time belongs;

[0017] Calculate a screening value based on the first information to be calculated, the second information to be calculated, the third information to be calculated, and the age of the current user;

[0018] Selecting a reference value matching the screening value in the electroencephalogram threshold database, and using a second related parameter corresponding to the selected reference value as a second related parameter matching the first related parameter, wherein the reference value is calculated based on the basic information of the historical user and a time interval corresponding to the electroencephalogram threshold range;

[0019] The first information to be calculated, the second information to be calculated and the third information to be calculated are all numbers.

[0020] By adopting the above technical solution, non-numerical basic information and time intervals are converted into information to be calculated in digital form, so that all information can be processed and compared in a unified form. By calculating the screening value and searching for matching reference values ​​in the EEG threshold database, accurate matching of historical user data can be achieved. After the basic information and time intervals are converted into numbers, efficient numerical calculation methods can be used to calculate the screening value and quickly search for matching reference values ​​in the database. This is more efficient than matching through text or character strings, can reduce matching time and improve response speed.

[0021] Optionally, before generating the warning information, the method further includes:

[0022] Acquire a first physiological parameter of the current user, where the first physiological parameter includes a blood pressure parameter and a heart rate parameter;

[0023] Determining whether the current state of the current user is abnormal based on the blood pressure parameter and the heart rate parameter;

[0024] If so, a new threshold range is determined based on the blood pressure parameter and the heart rate parameter, the new threshold range is used as the reference threshold range, and the step of comparing the first EEG data with the reference threshold range is performed.

[0025] By adopting the above technical solution, when the first EEG data is not within the reference threshold range, the first physiological data is analyzed to determine whether the current user is in a normal state, and the reference threshold range is re-determined and compared based on the current user's state, thereby reducing the possibility of false alarms.

[0026] Optionally, the method further includes:

[0027] Acquiring the second physiological data of the current user in real time;

[0028] When it is determined based on the second physiological data that the current state of the current user is normal, acquiring second electroencephalogram data of the current user;

[0029] using the electroencephalogram threshold range determined by the second related parameter as a reference threshold range;

[0030] The second electroencephalogram data is compared with the reference threshold range.

[0031] By adopting the above technical solution, the second physiological data is obtained in real time to confirm that the user's current state is normal, ensuring that the second EEG data under the normal state of the current user is collected, and the second EEG data under the normal state is compared with the reference threshold range, and the EEG data of the current user is further confirmed, thereby improving the accuracy of the EEG analysis of the current user.

[0032] Optionally, after taking the determined electroencephalogram threshold range as the reference threshold range, the method further includes:

[0033] Obtaining the number and time of EEG tests of the current user;

[0034] When the number of EEG detections and the detection time meet the preset requirements, the EEG data of the same time interval are divided into one analysis group;

[0035] Comparing the EEG data in each analysis group in pairs to determine whether the EEG trend has changed;

[0036] If not, the EEG threshold range of the current user is determined based on the EEG data in the analysis group, and the EEG threshold range is used as the reference threshold range.

[0037] By adopting the above technical solution, the EEG data of the same time interval are divided into an analysis group, which can more accurately reflect the user's EEG activity in the time period, reduce the fluctuation and difference of the EEG data caused by the large time span, and improve the accuracy and reliability of the analysis. By comparing the EEG data in each analysis group in pairs to determine whether the EEG trend has changed, the stability and change trend of the user's EEG activity can be further understood. If the EEG trend has not changed, the user's personalized EEG threshold range is determined based on the EEG data in the analysis group, thereby improving the pertinence and effectiveness of the analysis.

[0038] Optionally, after generating the warning information, the method further includes:

[0039] Obtaining contact information of users associated with the current user;

[0040] The warning information is sent to the mobile terminal corresponding to the associated user based on the contact information.

[0041] By adopting the above technical solution, when the EEG data of the current user is abnormal and a warning message is generated, the information is sent to the corresponding associated users, which can ensure that more people know about it at the first time and may take countermeasures.

[0042] Optionally, after sending the warning information to the mobile terminal corresponding to the associated user based on the contact information, the method further includes:

[0043] Acquire historical feedback information of each associated user on the warning information, wherein the historical feedback information includes read and unread information;

[0044] Obtaining the reading time of each associated user's historical feedback of the warning information as read;

[0045] Obtain the sending time of the warning information that has been read in the historical feedback;

[0046] Calculate the time difference of each read warning information based on the read time and the sending time;

[0047] Counting the number of screening warning information whose time difference is greater than a preset difference;

[0048] Obtaining the total amount of the warning information;

[0049] Calculating the ratio of the screening quantity to the total quantity;

[0050] When the quantity ratio is less than a preset quantity ratio, the associated users corresponding to the quantity ratio less than the preset quantity ratio are deleted.

[0051] By adopting the above technical solution, by tracking the read and unread status of the warning information by the associated users, it is possible to understand the attention paid by the associated users to the warning information, and to screen and delete the associated users who have not read or processed the warning information for a long time, thereby improving the utilization rate of resources.

[0052] In a second aspect, the present application provides an electroencephalogram abnormality analysis device, which adopts the following technical solution:

[0053] An electroencephalogram abnormality analysis device, comprising:

[0054] A first acquisition module, used to acquire first electroencephalogram data and first related parameters of the current user acquired by an electroencephalogram acquisition device, wherein the first related parameters include basic information of the current user and an acquisition time of the first electroencephalogram data;

[0055] A second acquisition module, used to acquire second related parameters of historical users in the EEG threshold database, wherein the second related parameters include basic information of the historical users and a time interval corresponding to the EEG threshold range;

[0056] A selection module, used for selecting a second relevant parameter matching the first relevant parameter;

[0057] a determination module, configured to determine an electroencephalogram threshold range based on the selected second related parameter, and use the determined electroencephalogram threshold range as a reference threshold range;

[0058] A comparison module is used to compare the first electroencephalogram data with the reference threshold range, and generate warning information when the first electroencephalogram data is not within the reference threshold range.

[0059] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0060] An electronic device comprises a processor and a memory, wherein the processor is coupled to the memory;

[0061] The processor is used to execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0062] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0063] A computer-readable storage medium comprises a computer program or an instruction, and when the computer program or the instruction is executed on a computer, the computer is caused to execute the method as described in any one of the first aspects.

[0064] In summary, the present application includes at least one of the following beneficial technical effects:

[0065] 1. By adopting the above technical solution, by obtaining the first relevant parameter of the current user and the acquisition time of the first electroencephalogram data, matching similar historical users through the first relevant parameter and the second relevant parameter, and determining the reference threshold range of the current user through the electroencephalogram threshold range of similar historical users, the selected reference threshold range is more consistent with the actual state of the current user. Compared with the method in which all people use the same normal range value to analyze the electroencephalogram, the possibility of misdiagnosis and missed diagnosis is reduced to a certain extent;

[0066] 2. Convert non-numeric basic information and time intervals into information to be calculated in digital form, so that all information can be processed and compared in a unified form. By calculating the screening value and searching for matching reference values ​​in the EEG threshold database, accurate matching of historical user data can be achieved. After converting the basic information and time intervals into numbers, efficient numerical calculation methods can be used to calculate the screening value and quickly search for matching reference values ​​in the database. This is more efficient than matching through text or character strings, reducing matching time and improving response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of a method for analyzing abnormal electroencephalogram in an embodiment of the present application.

[0068] Figure 2 It is a structural block diagram of the electroencephalogram acquisition system in the embodiment of the present application.

[0069] Figure 3 It is a structural block diagram of the electroencephalogram acquisition device in the embodiment of the present application.

[0070] Figure 4 It is a structural block diagram of an electroencephalogram abnormality analysis device in an embodiment of the present application.

[0071] Figure 5 It is a structural block diagram of an electronic device in an embodiment of the present application.

[0072] Explanation of the reference numerals: 200, EEG cap; 201, EEG acquisition device; 2011, charging management circuit; 2012, lithium battery; 2013, voltage conversion circuit; 2014, main control module; 2015, BLE transmission module; 2016, posture detection circuit; 2017, biosignal acquisition circuit; 2018, analog filtering circuit; 2019, ESD protection circuit; 202, electrode; 300, terminal device; 400, server. DETAILED DESCRIPTION

[0073] The present application is further described in detail below in conjunction with the accompanying drawings.

[0074] The present application is further described in detail below in conjunction with the accompanying drawings.

[0075] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0076] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0077] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0078] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0079] like Figure 1 As shown, a method for analyzing abnormal electroencephalogram (EEG) is described, and the main process of the method is described as follows (steps S101 to S105):

[0080] Step S101, acquiring first electroencephalogram data and first related parameters of the current user acquired by an electroencephalogram acquisition device, where the first related parameters include basic information of the current user and acquisition time of the first electroencephalogram data;

[0081] In this embodiment, the EEG acquisition device for acquiring the user's EEG is applied to the EEG acquisition system. First, the EEG acquisition system is described. Figure 2 As shown, the EEG acquisition system includes an EEG cap 200, a terminal device 300 and a server 400. The EEG cap 200 includes an EEG acquisition device 201 and an electrode 202. The electrode 202 and the EEG acquisition device 201 are connected via a signal wire. The EEG acquisition device 201 also includes a lithium battery 2012. The lithium battery 2012 is electrically connected to the EEG acquisition device 201. The EEG acquisition device 201 is connected to the terminal device 300 via Bluetooth or communication. The terminal device 300 is connected to the server 400 via communication.

[0082] Among them, the lithium battery 2012 adopts a 3.7V lithium battery with a built-in short-circuit and overcurrent protection circuit; the electrode 202 adopts a dry electrode, and considering the comfort of use, a flexible dry electrode is selected; the EEG cap 200 adopts elastic EEG caps of small, medium and large sizes, and the EEG cap 200 contains 10-20 lead distributions equal to the number of channels of the electrode 202, as well as reference leads and bias leads. At the same time, the EEG cap 200 is provided with a space for fixing the EEG acquisition device 201; the lead wire has good conductivity, strong acquisition performance and a firm plug connection; the distal end of the connector is a flexible dry electrode, the proximal end is a D-type faucet plug, and the cable material is medical-grade TPU2.0 wire.

[0083] By using the above-mentioned EEG acquisition system, patients or users can monitor the EEG status not only in medical environments such as hospitals and sanatoriums, but also in static or moving scenes of daily work and life.

[0084] like Figure 3 As shown, the EEG acquisition device 201 also includes a charging management circuit 2011, a voltage conversion circuit 2013, a main control module 2014, a BLE transmission module 2015, a posture detection circuit 2016, a biological signal acquisition circuit 2017, an analog filtering circuit 2018 and an ESD protection circuit 2019, wherein the charging management circuit 2011, the lithium battery 2012, the voltage conversion circuit 2013 and the main control module 2014 are connected in sequence, the BLE transmission module 2015 is connected to the main control module 2014, the posture detection circuit 2016 is connected to the main control module 2014, and the ESD protection circuit 2019, the analog filtering circuit 2018, the biological signal acquisition circuit 2017 and the main control module 2014 are connected in sequence.

[0085] Among them, the charging management circuit 2011 adopts a charging management chip, and a Type-C charging interface is set on the device, and the standard 5V1A power is used to charge the lithium battery 2012; the voltage conversion circuit 2013 stabilizes the voltage output by the lithium battery 2012 to a 5V power supply through the LDO power supply chip, and then converts the 5V power supply to 3.3V and 2.5V through the step-down voltage regulator chip, and converts the 2.5V power supply to -2.5V through the DC-DC power supply chip as the power supply voltage for different circuits; the front-end filtering preprocessing circuit of the analog filtering circuit 2018 includes a high-pass, a low-pass and a notch circuit. The cut-off frequency of the first-order RC high-pass circuit is 0.5Hz, which filters out the direct current contained in the signal, and the cut-off frequency of the second-order RC low-pass filter circuit is 100Hz , eliminate high-frequency interference and prevent frequency aliasing. The double-T trap circuit eliminates 50 / 60V power frequency noise interference. The order of the signal passing through the filtering network is high-pass first, low-pass second, and then filtering; posture detection circuit 2016: The EEG acquisition device 201 has a built-in 6-axis or 9-axis motion sensor to record the movement state of the head in real time; ESD protection circuit 2019: Connect a transient voltage suppression diode that meets the air discharge immunity level ≥±8KV and contact discharge ≥±6KV on the path where the electrostatic current mainly flows; biological signal acquisition circuit 2017: Select the ADS1299 chip with a low-noise 8-channel, 24-bit analog-to-digital converter suitable for EEG, and configure the chip's right leg drive and electrode detachment detection functions; main control module 2014: select ZYNQ7020 The SOC chip is used as a processor, and the embedded software is deployed on a heterogeneous architecture composed of a dual-core ARM and a programmable logic FPGA. The embedded software realizes the configuration of ADS1299 registers, digital filtering, EEG data acquisition, head movement monitoring, EEG signal artifact removal algorithm, EEG signal feature extraction algorithm, EEG signal classification algorithm, EEG data encoding, EEG data encryption and BLE wireless transmission module 2015 transmission function.

[0086] The EEG abnormality analysis method can be performed by an electronic device, which can be a terminal device or a server in an EEG acquisition system, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0087] In this embodiment, the electronic device obtains the electroencephalogram data of the current user collected by the electroencephalogram acquisition device, and the electronic device uses the obtained electroencephalogram data as the first electroencephalogram data. The electronic device obtains the first related parameters of the current user while obtaining the electroencephalogram data of the current user, wherein the first related parameters include the basic information of the current user and the acquisition time of the first electroencephalogram data, and the basic information of the current user is entered into the electronic device in advance before the current user uses the electroencephalogram acquisition device.

[0088] Step S102, obtaining a second related parameter of a historical user in the EEG threshold database, the second related parameter including basic information of the historical user and a time interval corresponding to the EEG threshold range;

[0089] In this embodiment, the EEG threshold database stores a large number of second related parameters of historical users, where the second related parameters are basic information of historical users and time intervals corresponding to the EEG threshold ranges.

[0090] Step S103, selecting a second relevant parameter that matches the first relevant parameter;

[0091] Specifically, based on preset rules, gender and disease type are converted into first information to be calculated and second information to be calculated; the third information to be calculated corresponding to the time interval to which the acquisition time belongs is obtained; the screening value is calculated based on the first information to be calculated, the second information to be calculated, the third information to be calculated and the age of the current user; a reference value matching the screening value is selected from the electroencephalogram threshold database, and the second related parameter corresponding to the selected reference value is used as the second related parameter matching the first related parameter, and the reference value is calculated from the basic information of the historical user and the time interval corresponding to the electroencephalogram threshold range; wherein the first information to be calculated, the second information to be calculated and the third information to be calculated are all numbers.

[0092] In this embodiment, when the electronic device obtains the first electroencephalogram data of the current user, the non-digital basic information is converted into digits.

[0093] Among them, a mapping relationship table of digital information corresponding to gender, disease type and time interval is stored in the electronic device. For example, the number corresponding to male is 1, and the number corresponding to female is 2; each disease type will correspond to a corresponding number. In this embodiment, the digital information corresponding to the gender is used as the first information to be calculated, and the digital information corresponding to the disease type is used as the second information to be calculated.

[0094] It should be noted that the EEG threshold range corresponding to each time period may be different, such as morning, morning and evening, so multiple time periods are preset.

[0095] In this embodiment, when the electronic device obtains the first electroencephalogram data, it searches the mapping relationship table for the time interval corresponding to the acquisition time, obtains the number corresponding to the time interval, and uses the number as the third information to be calculated, and searches the mapping relationship table for the numbers corresponding to the gender and the type of disease, and uses the corresponding numbers as the first information to be calculated and the second information to be calculated, respectively.

[0096] When obtaining the first information to be calculated, the second information to be calculated, and the third information to be calculated, the electronic device uses a hash algorithm to calculate the hash values ​​of the first information to be calculated, the second information to be calculated, the third information to be calculated, and the age, and uses the calculated hash values ​​as screening values, wherein the method for calculating the hash values ​​can use the SHA-2 algorithm.

[0097] It should be noted in particular that multiple age ranges are set in the electronic device, and each age range corresponds to a corresponding calculation value. The age in the above-mentioned first information to be calculated, second information to be calculated, third information to be calculated and age calculation filter value based on the current user is the calculation value corresponding to the age range.

[0098] Among them, in the EEG threshold database, each historical user will have a corresponding EEG threshold range and a corresponding reference value. The reference value is obtained through a hash algorithm based on the basic information of the historical user and the time interval corresponding to the EEG threshold range. The calculation method of the reference value is the same as the method of screening the value, which will not be repeated here.

[0099] In this embodiment, the electronic device selects the corresponding second related parameter based on the screening value and the reference value, wherein the method for selecting the corresponding second related parameter based on the screening value and the reference value is to sort the reference values ​​from small to large, use the binary method to select the reference value consistent with the screening value, and use the second related parameter corresponding to the selected reference value as the second related parameter matching the first related parameter.

[0100] Step S104, determining an EEG threshold range based on the selected second related parameter, and using the determined EEG threshold range as a reference threshold range;

[0101] In this embodiment, when there is one reference value that is consistent with the screening value, there is only one matching second related parameter, and when there are multiple reference values ​​that are consistent with the screening value, there are multiple matching second related parameters.

[0102] When there is only one matching second-related parameter, the EEG threshold range corresponding to the second-related parameter is selected as the reference threshold range; when there are multiple matching second-related parameters, the EEG threshold ranges corresponding to all the matching second-related parameters are obtained, the average value of the obtained EEG threshold ranges is calculated, and the average value is used as the reference threshold range of the current user, wherein the method for calculating the average value can be: respectively calculate the average value of each endpoint value in the EEG threshold range, and the average values ​​of the two endpoints form a certain reference threshold range.

[0103] In this embodiment, if the EEG data collected for the current user is sufficient, the reference threshold is updated according to the EEG data of the current user.

[0104] Specifically, the number of EEG detection times and detection time of the current user are obtained; when the number of EEG detection times and detection time meet the preset requirements, the EEG data of the same time interval are divided into an analysis group; the EEG data in each analysis group are compared pairwise to determine whether the EEG trend has changed; if not, the EEG threshold range of the current user is determined based on the EEG data in the analysis group, and the EEG threshold range is used as the reference threshold range.

[0105] In this embodiment, the preset requirements are preset in advance. For example, if there are three preset time intervals in the electronic device and there are at least 5 EEG data in each time interval, it is determined that the preset conditions are met, and the EEG data are divided into corresponding time intervals according to the acquisition time of each EEG data, and the EEG data in the same time interval are divided into an analysis group; based on the neural network model, the EEG data in each analysis group are compared two by two to determine the change trend of the EEG in each analysis group. When the EEG change trend is relatively stable, the maximum and minimum values ​​of the EEG are used as the corresponding reference threshold range.

[0106] Step S105: compare the first electroencephalogram data with a reference threshold range, and generate warning information when the first electroencephalogram data is not within the reference threshold range.

[0107] In this embodiment, the electronic device compares the first EEG data with the reference threshold range. When the first EEG data is not within the reference threshold range, it proves that the EEG data of the current user is abnormal. At this time, an early warning message is generated to remind the current user.

[0108] In this embodiment, in order to reduce the impact of environmental factors on EEG data comparison, it is also necessary to determine the current user's status before generating warning information.

[0109] Specifically, the first physiological parameter of the current user is obtained, and the first physiological parameter includes a blood pressure parameter and a heart rate parameter; based on the blood pressure parameter and the heart rate parameter, it is determined whether the current state of the current user is abnormal; if so, a new threshold range is determined based on the blood pressure parameter and the heart rate parameter, the new threshold range is used as a reference threshold range, and the step of comparing the first electroencephalogram data with the reference threshold range is executed.

[0110] In this embodiment, the current state of the current user mainly refers to whether the user is in a state of exercise or a state after exercise.

[0111] The electronic device obtains a first physiological parameter detected by a physiological parameter device, wherein the first physiological parameter includes a blood pressure parameter and a heart rate parameter, and the physiological parameter device may be a smart watch. When the electronic device obtains the first physiological parameter, the first physiological parameter is compared with the physiological parameters of the current user in a normal state, wherein the physiological parameters in the normal state are physiological data of the current user in a stable state, that is, not in a motion state or a state after motion. When the first physiological parameter and the physiological parameters in the normal state do not meet a preset difference range, it is determined that the current state of the current user is abnormal, and the abnormal state is a state in motion or a state after motion. At this time, the corresponding electroencephalogram threshold range under the abnormal state is obtained, and the electroencephalogram threshold range is used as a new threshold range, wherein the corresponding electroencephalogram threshold range under the abnormal state is the electroencephalogram threshold range collected by the current user in a motion state or a state after motion.

[0112] In order to improve the accuracy of abnormal analysis of the EEG of the current user, when the current state of the current user is normal, abnormal analysis is performed again on the EEG data in the normal state.

[0113] Specifically, the second physiological data of the current user is obtained in real time; when the current state of the current user is determined to be normal based on the second physiological data, the second electroencephalogram data of the current user is obtained; the electroencephalogram threshold range determined by the second related parameter is used as a reference threshold range; and the second electroencephalogram data is compared with the reference threshold range.

[0114] In this embodiment, the electronic device compares the second physiological parameter with the normal physiological parameter range in real time. When the second physiological parameter is within the normal physiological parameter range, the second electroencephalogram data is acquired, and the second electroencephalogram data is compared with the reference threshold range. When the reference threshold range is not met, an alarm message is generated, wherein the normal physiological parameter range is the physiological parameters collected by the current user under normal conditions.

[0115] After the warning information is generated, the following contents are also included:

[0116] Specifically, the contact information of the user associated with the current user is obtained; and the warning information is sent to the mobile terminal corresponding to the associated user based on the contact information.

[0117] In this embodiment, before the current user performs EEG data detection, the user's basic information and corresponding contact information need to be input into the electronic device through the input device of the electronic device. After the electronic device generates warning information, the contact information of the associated person corresponding to the current user is searched, and the warning information is sent to the mobile terminal of the corresponding associated person through the contact information, where the associated persons include but are not limited to relatives, friends and doctors.

[0118] After sending the warning information to the mobile terminal corresponding to the associated user based on the contact information, the following contents are also included:

[0119] Specifically, the historical feedback information of each associated user on the warning information is obtained, and the historical feedback information includes read and unread information; the reading time of each associated user's historical feedback on the warning information as read is obtained; the sending time of the warning information with historical feedback as read is obtained; the time difference of each read warning information is calculated based on the reading time and the sending time; the number of screened warning information whose time difference is greater than a preset difference is counted; the total amount of warning information is obtained; the ratio of the screened number to the total amount is calculated; when the number ratio is less than the preset number ratio, the associated users corresponding to the number ratio less than the preset number ratio are deleted.

[0120] In this embodiment, one of the associated persons is used for illustration. The electronic device obtains the total amount of warning information received by the associated person, and obtains feedback information for each warning information, wherein the feedback information is read and unread, extracts the warning information with feedback information as read, obtains the reading time and sending time of the extracted warning information, and calculates the time difference between the sending time and the reading time. After the time difference is obtained, the time difference is compared with the preset difference, and the number of warning information greater than the preset difference is counted, and the counted number is used as the screening number. When obtaining the screening number, the screening number is used as the numerator and the total amount as the denominator to calculate the quantity ratio, and the quantity ratio is compared with the preset quantity ratio. When the quantity ratio is less than the preset quantity ratio, the associated users corresponding to the quantity ratio less than the preset quantity ratio are deleted.

[0121] It is particularly noted that the prerequisite for executing the operation of deleting associated users is that the total amount is greater than a preset value, wherein the preset value can be set as needed and is not specifically limited thereto.

[0122] In this embodiment, the detection time of the current user can be monitored. Specifically, it is queried whether the current user has historical disease information; if so, the type of disease is determined based on the historical disease information; the detection time period is determined based on the type of disease; the next detection time is determined based on the detection time period and the current time; when the next detection time arrives, a detection prompt information is generated, and the detection prompt information is sent to the mobile terminal corresponding to the current user.

[0123] In this embodiment, a standard time period can be set according to common diseases. At the same time, the appropriate testing frequency and time period for the current user can be determined based on the doctor's assessment of the stability of the current user's condition and the treatment plan. The electronic device detects and calculates the time value between the current time and the previous testing time in real time. When the time value meets the standard time period, a testing prompt message is sent to the current user to remind the user to conduct a timely test.

[0124] Figure 4 1 is a structural block diagram of an electroencephalogram abnormality analysis device 500 provided in this application. Figure 4 As shown, the EEG abnormality analysis device 500 mainly includes:

[0125] A first acquisition module 501 is used to acquire first electroencephalogram data and first related parameters of the current user acquired by an electroencephalogram acquisition device, where the first related parameters include basic information of the current user and acquisition time of the first electroencephalogram data;

[0126] A second acquisition module 502 is used to acquire second related parameters of historical users in the EEG threshold database, where the second related parameters include basic information of the historical users and a time interval corresponding to the EEG threshold range;

[0127] A selection module 503 is used to select a second related parameter that matches the first related parameter;

[0128] A determination module 504, configured to determine an EEG threshold range based on the selected second related parameter, and use the determined EEG threshold range as a reference threshold range;

[0129] The comparison module 505 is used to compare the first electroencephalogram data with a reference threshold range, and generate warning information when the first electroencephalogram data is not within the reference threshold range.

[0130] As an optional implementation of this embodiment, the calculation module 503 includes:

[0131] A conversion submodule, used for converting gender and disease type into first information to be calculated and second information to be calculated based on preset rules;

[0132] An information acquisition submodule, used for acquiring the third information to be calculated corresponding to the time interval to which the acquisition time belongs;

[0133] A first calculation submodule, used to calculate a screening value based on the first information to be calculated, the second information to be calculated, the third information to be calculated and the age of the current user;

[0134] The reference value acquisition submodule is used to select a reference value matching the screening value in the EEG threshold database, and use the second related parameter corresponding to the selected reference value as the second related parameter matching the first related parameter, and the reference value is calculated based on the basic information of the historical user and the time interval corresponding to the EEG threshold range; wherein the first information to be calculated, the second information to be calculated and the third information to be calculated are all numbers.

[0135] As an optional implementation of this embodiment, the EEG abnormality analysis device 500 further includes:

[0136] A parameter acquisition module, used for acquiring a first physiological parameter of the current user before generating the warning information, the first physiological parameter including a blood pressure parameter and a heart rate parameter;

[0137] The state judgment module is used to judge whether the current state of the current user is abnormal based on the blood pressure parameters and the heart rate parameters; if so, a new threshold range is determined based on the blood pressure parameters and the heart rate parameters, the new threshold range is used as a reference threshold range, and the step of comparing the first electroencephalogram data with the reference threshold range is executed.

[0138] As an optional implementation of this embodiment, the EEG abnormality analysis device 500 further includes:

[0139] A first data acquisition module, used for acquiring second physiological data of the current user in real time;

[0140] A second data acquisition module, configured to acquire second electroencephalogram data of the current user when it is determined based on the second physiological data that the current state of the current user is normal;

[0141] a range determination module, configured to use the electroencephalogram threshold range determined by the second related parameter as a reference threshold range;

[0142] The comparison module is used to compare the second electroencephalogram data with a reference threshold range.

[0143] As an optional implementation of this embodiment, the EEG abnormality analysis device 500 further includes:

[0144] The third acquisition module, after the user selects the EEG warning threshold range as the reference threshold range, obtains the number of EEG detections and detection time of the current user;

[0145] A division module is used to divide the EEG data of the same time interval into an analysis group when the number of EEG detections and the detection time meet the preset requirements;

[0146] The comparison module is used to compare the EEG data in each analysis group in pairs to determine whether the EEG trend has changed; if not, the EEG threshold range of the current user is determined based on the EEG data in the analysis group, and the EEG threshold range is used as the reference threshold range.

[0147] As an optional implementation of this embodiment, the EEG abnormality analysis device 500 further includes:

[0148] The contact information acquisition module is used to obtain the contact information of the current user's associated users after the warning information is generated;

[0149] The sending module is used to send the warning information to the mobile terminal corresponding to the associated user based on the contact information.

[0150] As an optional implementation of this embodiment, the EEG abnormality analysis device 500 further includes:

[0151] A feedback information acquisition module, used to acquire historical feedback information of each associated user on the warning information after sending the warning information to the mobile terminal corresponding to the associated user based on the contact information, the historical feedback information including read and unread information;

[0152] The first time acquisition module is used to obtain the reading time of each associated user's historical feedback on the warning information as read;

[0153] The second time acquisition module is used to obtain the sending time of the warning information that has been read in the historical feedback;

[0154] A time calculation module, used to calculate the time difference of each read warning information based on the read time and the send time;

[0155] The statistics module is used to count the number of screening warning information whose time difference is greater than the preset difference;

[0156] The total amount acquisition module is used to obtain the total amount of warning information;

[0157] A ratio calculation module, used to calculate the ratio of the screening quantity to the total quantity;

[0158] The deletion module is used to delete the associated users corresponding to the number ratio being less than the preset number ratio when the number ratio is less than the preset number ratio.

[0159] The functional modules in the embodiments of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. If the function is implemented in the form of a software functional 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 application is essentially 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, which is stored in a storage medium and includes a number of instructions for an electronic device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of an electroencephalogram abnormality analysis method in each embodiment of the present application.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] Figure 5 FIG. 6 is a structural block diagram of an electronic device 600 provided in an embodiment of the present application. Figure 5 As shown, the electronic device 600 includes a memory 601, a processor 602 and a communication bus 603; the memory 601 and the processor 602 are connected via the communication bus 603. The memory 601 stores an EEG abnormality analysis method provided in the above embodiment which can be loaded and executed by the processor 602.

[0162] The memory 601 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 601 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing an abnormal electroencephalogram analysis method provided in the above embodiment, etc.; the data storage area may store data involved in an abnormal electroencephalogram analysis method provided in the above embodiment, etc.

[0163] The processor 602 may include one or more processing cores. The processor 602 executes various functions and processes data of the present application by running or executing instructions, programs, code sets or instruction sets stored in the memory 601, calling the data stored in the memory 601. The processor 602 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor 602 function may also be other, and the embodiments of the present application are not specifically limited.

[0164] The communication bus 603 may include a path to transmit information between the above components. The communication bus 603 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 603 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one double arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0165] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute an electroencephalogram abnormality analysis method as provided in the above embodiment.

[0166] In this embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0167] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0168] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for analyzing abnormal electroencephalogram, characterized in that: include: Acquire first electroencephalogram data and first related parameters of the current user acquired by an electroencephalogram acquisition device, wherein the first related parameters include basic information of the current user and acquisition time of the first electroencephalogram data; Acquire a second related parameter of a historical user in the electroencephalogram threshold database, wherein the second related parameter includes basic information of the historical user and a time interval corresponding to the electroencephalogram threshold range; Selecting a second relevant parameter that matches the first relevant parameter; determining an electroencephalogram threshold range based on the selected second related parameter, and using the determined electroencephalogram threshold range as a reference threshold range; The first electroencephalogram data is compared with the reference threshold range, and when the first electroencephalogram data is not within the reference threshold range, a warning message is generated.

2. The method according to claim 1, characterized in that The basic information includes age, gender and disease type; the selecting the second relevant parameter matching the first relevant parameter includes: Converting the gender and disease type into first information to be calculated and second information to be calculated based on preset rules; Acquire the third information to be calculated corresponding to the time interval to which the acquisition time belongs; Calculate a screening value based on the first information to be calculated, the second information to be calculated, the third information to be calculated, and the age of the current user; Selecting a reference value matching the screening value in the electroencephalogram threshold database, and using a second related parameter corresponding to the selected reference value as a second related parameter matching the first related parameter, wherein the reference value is calculated based on the basic information of the historical user and a time interval corresponding to the electroencephalogram threshold range; The first information to be calculated, the second information to be calculated and the third information to be calculated are all numbers.

3. The method according to claim 1, characterized in that Before generating the warning information, the method further includes: Acquire a first physiological parameter of the current user, where the first physiological parameter includes a blood pressure parameter and a heart rate parameter; Determining whether the current state of the current user is abnormal based on the blood pressure parameter and the heart rate parameter; If so, a new threshold range is determined based on the blood pressure parameter and the heart rate parameter, the new threshold range is used as the reference threshold range, and the step of comparing the first EEG data with the reference threshold range is performed.

4. The method according to claim 3, characterized in that The method further comprises: Acquiring the second physiological data of the current user in real time; When it is determined based on the second physiological data that the current state of the current user is normal, acquiring second electroencephalogram data of the current user; using the electroencephalogram threshold range determined by the second related parameter as a reference threshold range; The second electroencephalogram data is compared with the reference threshold range.

5. The method according to claim 1, characterized in that After the determined electroencephalogram threshold range is used as a reference threshold range, the method further includes: Obtaining the number and time of EEG tests of the current user; When the number of EEG detections and the detection time meet the preset requirements, the EEG data in the same time interval are divided into one analysis group; Comparing the EEG data in each analysis group in pairs to determine whether the EEG trend has changed; If not, the EEG threshold range of the current user is determined based on the EEG data in the analysis group, and the EEG threshold range is used as the reference threshold range.

6. The method according to claim 1, characterized in that After generating the warning information, the method further includes: Obtain contact information of users associated with the current user; The warning information is sent to the mobile terminal corresponding to the associated user based on the contact information.

7. The method according to claim 6, characterized in that After sending the warning information to the mobile terminal corresponding to the associated user based on the contact information, the method further includes: Acquire historical feedback information of each associated user on the warning information, wherein the historical feedback information includes read and unread information; Obtaining the reading time of each associated user's historical feedback of the warning information as read; Obtain the sending time of the warning information that has been read in the historical feedback; Calculate the time difference of each read warning information based on the read time and the sending time; Counting the number of screening warning information whose time difference is greater than a preset difference; Obtaining the total amount of the warning information; Calculating the ratio of the screening quantity to the total quantity; When the quantity ratio is less than a preset quantity ratio, the associated users corresponding to the quantity ratio less than the preset quantity ratio are deleted.

8. An electroencephalogram abnormality analysis device, characterized in that: include: A first acquisition module, used to acquire first electroencephalogram data and first related parameters of the current user acquired by an electroencephalogram acquisition device, wherein the first related parameters include basic information of the current user and an acquisition time of the first electroencephalogram data; A second acquisition module, used to acquire second related parameters of historical users in the EEG threshold database, wherein the second related parameters include basic information of the historical users and a time interval corresponding to the EEG threshold range; A selection module, used for selecting a second relevant parameter matching the first relevant parameter; a determination module, configured to determine an electroencephalogram threshold range based on the selected second related parameter, and use the determined electroencephalogram threshold range as a reference threshold range; A comparison module is used to compare the first electroencephalogram data with the reference threshold range, and generate warning information when the first electroencephalogram data is not within the reference threshold range.

9. An electronic device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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