Electroencephalogram abnormality analysis method, device, apparatus, and storage medium

Abstract

The application relates to an electroencephalogram anomaly analysis method, device, equipment and storage medium, and belongs to the technical field of electroencephalogram analysis. The method comprises the following steps: acquiring first electroencephalogram data and first related parameters of a current user collected by an electroencephalogram collection device, wherein the first related parameters comprise basic information of the current user and an acquisition time of the first electroencephalogram data; acquiring second related parameters of historical users in an electroencephalogram threshold value database, wherein the second related parameters comprise basic information of the historical users and a time interval corresponding to an electroencephalogram threshold value range; selecting second related parameters matched with the first related parameters; determining an electroencephalogram threshold value range based on the selected second related parameters, and taking the determined electroencephalogram threshold value range as a reference threshold value range; and comparing the first electroencephalogram data with the reference threshold value range, and generating an early warning information when the first electroencephalogram data is not in the reference threshold value range. The application 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 analysis, and in particular to an electroencephalogram abnormality analysis method, device, equipment and storage medium. BACKGROUND

[0002] Electroencephalogram is a method of reflecting the activity state of brain neurons by recording the electrical activity of the brain captured by electrodes on the scalp. It has a wide application in the treatment of neurological diseases and the study of brain neural biology mechanisms.

[0003] Generally, electroencephalogram abnormality is one of the important manifestations of nervous system diseases or brain function abnormalities. Electroencephalogram abnormality refers to the deviation of electroencephalogram from the normal range. Clinically common diseases with abnormal electroencephalogram include encephalitis, meningitis, epilepsy, cerebral ischemia and hypoxia, etc.

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

[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 a first aspect, the present application provides an electroencephalogram abnormality analysis method, which adopts the following technical solution:

[0007] An electroencephalogram abnormality analysis method, comprising:

[0008] obtaining first electroencephalogram data and first related parameters of a current user collected by an electroencephalogram collection device, the first related parameters comprising basic information of the current user and acquisition time of the first electroencephalogram data;

[0009] obtaining second related parameters of a historical user in an electroencephalogram threshold database, the second related parameters comprising basic information of the historical user and a time interval corresponding to an electroencephalogram threshold range;

[0010] selecting second related parameters matching the first related parameters;

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

[0012] comparing the first electroencephalogram data with the reference threshold range, and generating a warning information when the first electroencephalogram data is not in the reference threshold range.

[0013] By adopting the above technical solution, by obtaining the first relevant parameter and the acquisition time of the first EEG data of the current user, matching similar historical users through the first and second relevant parameters, 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 of using the same normal range value for all people to analyze EEG, it reduces the possibility of misdiagnosis and missed diagnosis to a certain extent.

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

[0015] Based on preset rules, the gender and disease type are converted into first and second information to be calculated.

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

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

[0018] A reference value matching the screening value is selected from the EEG 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. The reference value is calculated from the basic information of the historical user and the time interval corresponding to the EEG threshold range.

[0019] Among them, 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 transformed into digital information to be calculated, enabling all information to be processed and compared in a unified form. By calculating the filter value and searching for a matching reference value 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 filter value and quickly search for the matching reference value in the database. This is more efficient than matching through text or strings, reducing matching time and improving response speed.

[0021] Optionally, before generating the warning information, the following steps are also included:

[0022] Obtain the first physiological parameters of the current user, the first physiological parameters including blood pressure parameters and heart rate parameters;

[0023] Based on the blood pressure parameters and the heart rate parameters, determine whether the current user's current state is abnormal;

[0024] If so, then based on the new threshold range determined by 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 current user's normal state is determined by analyzing the first physiological data, 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] Real-time acquisition of the current user's second physiological data;

[0028] When the current state of the current user is determined to be normal based on the second physiological data, the second electroencephalogram (EEG) data of the current user is acquired.

[0029] The EEG threshold range determined by the second relevant parameter is used as the reference threshold range;

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

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

[0032] Optionally, after using the determined EEG threshold range as a reference threshold range, the method further includes:

[0033] Obtain the number of EEG tests and the test time for the current user;

[0034] 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 an analysis group;

[0035] The EEG data in each analysis group were compared pairwise to determine whether the EEG trend had 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, EEG data within the same time interval are divided into an analysis group, which can more accurately reflect the user's brain activity during that time period, reduce the fluctuations and differences in EEG data caused by large time spans, and improve the accuracy and reliability of the analysis. By comparing the EEG data in each analysis group pairwise, it can be determined whether the EEG trend has changed, which can further understand the stability and trend of the user's brain activity. If the EEG trend has not changed, the user's personalized EEG threshold range can be determined based on the EEG data in that analysis group, thus improving the targeting and effectiveness of the analysis.

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

[0039] Obtain the contact information of the current user's associated users;

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

[0041] By adopting the above technical solution, when an abnormality is found in the current user's EEG data, an early warning message is generated and sent to the corresponding associated users, ensuring that more people are aware of the situation and can take appropriate measures as soon as possible.

[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] Obtain historical feedback information of each associated user regarding the warning information, the historical feedback information including read and unread status;

[0044] Obtain the read time of each associated user's historical feedback on 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 for each read warning message based on the read time and the sending time;

[0047] Count the number of early warning messages whose time difference is greater than a preset difference.

[0048] Obtain the total amount of the aforementioned early warning information;

[0049] Calculate the ratio of the number of samples selected to the total number of samples.

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

[0051] By adopting the above technical solution and tracking the read and unread status of related users' alert information, we can understand the attention that related users pay to the alert information. For related users who do not read or process the alert information for a long time, we can filter and delete them, thereby improving the utilization rate of resources.

[0052] Secondly, this application provides an electroencephalogram (EEG) abnormality analysis device, which adopts the following technical solution:

[0053] An electroencephalogram (EEG) abnormality analysis device, comprising:

[0054] The first acquisition module is used to acquire the first electroencephalogram (EEG) data and first related parameters of the current user collected by the EEG acquisition device. The first related parameters include the basic information of the current user and the acquisition time of the first EEG data.

[0055] The second acquisition module is used to acquire the second relevant parameters of historical users in the EEG threshold database. The second relevant parameters include the basic information of the historical users and the time interval corresponding to the EEG threshold range.

[0056] The selection module is used to select a second relevant parameter that matches the first relevant parameter.

[0057] The determination module is used to determine the EEG threshold range based on the selected second relevant parameter, and to use the determined EEG threshold range as a reference threshold range.

[0058] The comparison module is used to compare the first EEG data with the reference threshold range, and generate a warning message when the first EEG data is not within the reference threshold range.

[0059] Thirdly, this application provides an electronic device that adopts the following technical solution:

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

[0061] The processor is configured to execute a computer program stored in the memory, causing the electronic device to perform the method as described in any of the first aspects.

[0062] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0063] A computer-readable storage medium includes a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in any of the first aspects.

[0064] In summary, this 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 and the acquisition time of the first EEG data of the current user, matching similar historical users through the first and second relevant parameters, 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 of using the same normal range value for all people to analyze EEG, it reduces the possibility of misdiagnosis and missed diagnosis to a certain extent.

[0066] 2. By converting non-numerical basic information and time intervals into numerical information to be calculated, all information can be processed and compared in a unified manner. By calculating the filter value and searching for a matching reference value 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 filter value and quickly find the matching reference value in the database. This is more efficient than matching through text or strings, reducing matching time and improving response speed. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating a method for analyzing abnormal electroencephalograms in an embodiment of this application.

[0068] Figure 2 This is a structural block diagram illustrating the electroencephalogram (EEG) acquisition system in the embodiments of this application.

[0069] Figure 3 This is a structural block diagram illustrating the electroencephalogram (EEG) acquisition device in the embodiments of this application.

[0070] Figure 4 This is a structural block diagram illustrating an electroencephalogram (EEG) abnormality analysis device in the embodiments of this application.

[0071] Figure 5 This is a structural block diagram illustrating an electronic device in the embodiments of this application.

[0072] Explanation of reference numerals in the attached figures: 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 Implementation

[0073] The present application will be further described in detail below with reference to the accompanying drawings.

[0074] The present application will be further described in detail below with reference to the accompanying drawings.

[0075] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0076] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0078] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0079] like Figure 1 As shown, a method for analyzing abnormalities in electroencephalograms (EEGs) has the following main flowchart (steps S101-S105):

[0080] Step S101: Obtain the first EEG data and first related parameters of the current user collected by the EEG acquisition device. The first related parameters include the basic information of the current user and the acquisition time of the first EEG data.

[0081] In this embodiment, the electroencephalogram (EEG) acquisition device for acquiring user EEG data is used in the EEG acquisition system. First, the EEG acquisition system will be described, such as... 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 electrodes 202. The electrodes 202 and the EEG acquisition device 201 are connected via signal wires. The EEG acquisition device 201 also includes a lithium battery 2012, which 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] The lithium battery 2012 is a 3.7V lithium battery with built-in short-circuit and overcurrent protection circuits; the electrode 202 is a dry electrode, and a flexible dry electrode is selected for user comfort; the EEG cap 200 is an EEG cap available in small, medium, and large sizes with elasticity, and includes a 10-20 lead distribution equal to the number of channels on the electrode 202, as well as reference leads and bias leads. The EEG cap 200 also has space for fixing the EEG acquisition device 201; the lead wires are made of materials with good conductivity, strong acquisition performance, and secure plug connections; the connector has a flexible dry electrode at the distal end and a D-type faucet plug at the proximal end, and the cable material is medical-grade TPU2.0 wire.

[0083] Using the above-mentioned EEG acquisition system, patients or users can monitor their EEG status not only in medical environments such as hospitals and sanatoriums, but also in static or dynamic scenarios during 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, an attitude detection circuit 2016, a biosignal acquisition circuit 2017, an analog filtering circuit 2018, and an ESD protection circuit 2019. The charging management circuit 2011, lithium battery 2012, voltage conversion circuit 2013, and main control module 2014 are connected in sequence. The BLE transmission module 2015 is connected to the main control module 2014. The attitude detection circuit 2016 is connected to the main control module 2014. The ESD protection circuit 2019, analog filtering circuit 2018, biosignal acquisition circuit 2017, and main control module 2014 are connected in sequence.

[0085] The charging management circuit 2011 uses a charging management chip and is equipped with a Type-C charging interface, charging the lithium battery 2012 with a standard 5V 1A power output. The voltage conversion circuit 2013 uses an LDO power chip to stabilize the voltage output from the lithium battery 2012 to a 5V power supply, then uses a step-down regulator chip to convert the 5V power supply to 3.3V and 2.5V, and a DC-DC power chip to convert the 2.5V power supply to -2.5V, serving as the power supply voltage for different circuits. The analog filter circuit 2018's front-end filtering preprocessing circuit includes high-pass, low-pass, and notch filter circuits. The first-order RC high-pass circuit has a cutoff frequency of 0.5Hz, filtering out DC current contained in the signal, while the second-order RC low-pass filter circuit has a cutoff frequency of 100Hz. To eliminate high-frequency interference and prevent frequency aliasing, a dual-T notch filter circuit eliminates 50 / 60V power frequency noise interference. The signal passes through the filtering network in the following order: high-pass, low-pass, and then filtering. The posture detection circuit (2016) uses a built-in 6-axis or 9-axis motion sensor in the EEG acquisition device (201) to record head movement in real time. The ESD protection circuit (2019) connects transient voltage suppression diodes that meet air discharge immunity levels ≥ ±8KV and contact discharge levels ≥ ±6KV along the main path of electrostatic current flow. The biosignal acquisition circuit (2017) uses the ADS1299 chip, a low-noise 8-channel, 24-bit analog-to-digital converter suitable for EEG, and configures it with right leg drive and electrode detachment detection functions. The main control module (2014) uses the ZYNQ7020. The SOC chip acts as a processor, deploying embedded software on a heterogeneous architecture consisting of a dual-core ARM and a programmable logic FPGA. The embedded software implements the configuration of ADS1299 registers, digital filtering, EEG data acquisition, head motion 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] This EEG abnormality analysis method can be executed by an electronic device, which can be a terminal device or a server in an EEG acquisition system. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.

[0087] In this embodiment, the electronic device acquires the EEG data of the current user collected by the EEG acquisition device. The electronic device uses the acquired EEG data as the first EEG data. While acquiring the current user's EEG data, the electronic device also acquires the current user's first related parameters. The first related parameters include the current user's basic information and the acquisition time of the first EEG data. The current user's basic information is pre-entered into the electronic device before the current user uses the EEG acquisition device.

[0088] Step S102: Obtain the second relevant parameters of historical users in the EEG threshold database. The second relevant parameters include the basic information of the historical users and the time interval corresponding to the EEG threshold range.

[0089] In this embodiment, the EEG threshold database stores a large number of second relevant parameters of historical users. The second relevant parameters are the basic information of historical users and the time interval corresponding to the EEG threshold range.

[0090] Step S103: Select a second correlation parameter that matches the first correlation parameter;

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

[0092] In this embodiment, when the electronic device acquires the current user's first electroencephalogram (EEG) data, it converts non-digital basic information into digital data.

[0093] The electronic device stores a mapping table of numerical information corresponding to gender, disease type, and time interval. For example, the number corresponding to male is 1, and the number corresponding to female is 2. Each disease type corresponds to a corresponding number. In this embodiment, the numerical information corresponding to gender is used as the first information to be calculated, and the numerical information corresponding to disease type is used as the second information to be calculated.

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

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

[0096] When the first, second, and third information to be calculated are obtained, the electronic device uses a hash algorithm to calculate the hash value of the first, second, and third information to be calculated and the age, and uses the calculated hash value as the filtering value. The hash value can be calculated using the SHA-2 algorithm.

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

[0098] In the EEG threshold database, each historical user has a corresponding EEG threshold range and a corresponding reference value. The reference value is obtained by hashing based on the historical user's basic information and the time interval corresponding to the EEG threshold range. The calculation method for the reference value and the method for filtering the value are the same, so they will not be described in detail here.

[0099] In this embodiment, the electronic device selects a corresponding second related parameter based on the filter value and the reference value. The method for selecting the corresponding second related parameter based on the filter value and the reference value can be to sort the reference values ​​in ascending order, select the reference value that matches the filter value using a binary search method, and use the second related parameter corresponding to the selected reference value as the second related parameter that matches the first related parameter.

[0100] Step S104: Determine the EEG threshold range based on the selected second relevant parameter, and use the determined EEG threshold range as the reference threshold range;

[0101] In this embodiment, when there is a reference value that matches the filter value, there is only one matching second related parameter; when there are multiple reference values ​​that match the filter value, there will be 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 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 for the current user. The average value can be calculated by: calculating the average value of each endpoint value in the EEG threshold range, and combining the average values ​​of the two endpoints to form a reference threshold range.

[0103] In this embodiment, if there is enough EEG data collected from the current user, the reference threshold is updated using the current user's EEG data.

[0104] Specifically, the system obtains the number of EEG tests and the test time for the current user; when the number of EEG tests and the test time meet the preset requirements, the EEG data in 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 system determines the current user's EEG threshold range based on the EEG data in the analysis group, and uses the EEG threshold range as a reference threshold range.

[0105] In this embodiment, the preset requirements are pre-set. For example, if there are three preset time intervals in the electronic device, and each time interval contains at least 5 EEG data, it is determined that the preset conditions are met. Based on the acquisition time of each EEG data, the EEG data is divided into the corresponding time interval, 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 pairwise to determine the change trend of the EEG in each analysis group. When the change trend of the EEG 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 EEG data with the reference threshold range. If the first EEG data is not within the reference threshold range, generate a warning message.

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

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

[0109] Specifically, the system acquires the current user's first physiological parameters, including blood pressure and heart rate. Based on the blood pressure and heart rate parameters, it determines whether the current user's current state is abnormal. If so, it uses a new threshold range determined based on the blood pressure and heart rate parameters as a reference threshold range and performs a step of comparing the first EEG data with the reference threshold range.

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

[0111] The electronic device acquires the first physiological parameters detected by the device, including blood pressure and heart rate parameters. The physiological parameter device can be a smartwatch. When the electronic device acquires the first physiological parameters, it compares them with the physiological parameters of the current user in a normal state. The physiological parameters in a normal state are the physiological data of the current user in a stable state, i.e., not in a state of exercise or in a state after exercise. When the first physiological parameter and the physiological parameters in a normal state do not meet the preset difference range, the current user's current state is determined to be abnormal. The abnormal state is the state of exercise or in a state after exercise. At this time, the corresponding EEG threshold range in the abnormal state is acquired and used as the new threshold range. The corresponding EEG threshold range in the abnormal state is the EEG threshold range collected when the current user is in a state of exercise or in a state after exercise.

[0112] To improve the accuracy of abnormal analysis of the current user's EEG, abnormal analysis is performed again on the EEG data under the normal state when the current user's current state is normal.

[0113] Specifically, the system acquires the user's second physiological data in real time; when the user's current state is determined to be normal based on the second physiological data, it acquires the user's second electroencephalogram (EEG) data; the EEG threshold range determined by the second relevant parameters is used as a reference threshold range; and the second EEG 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 EEG data is acquired and compared with the reference threshold range. When the reference threshold range is not met, an alarm message is generated. The normal physiological parameter range refers to the physiological parameters collected by the current user in a normal state.

[0115] After generating the warning information, the following content is also included:

[0116] Specifically, obtain the contact information of the current user's associated users; and send the warning information to the mobile terminal corresponding to the associated user based on the contact information.

[0117] In this embodiment, before the current user undergoes 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 an early warning information, it searches for the contact information of the current user's associated persons and sends the early warning information to the mobile terminal of the corresponding associated persons through the contact information. The associated persons include, but are not limited to, relatives, friends and doctors.

[0118] After sending the alert information to the mobile device corresponding to the associated user based on the contact information, the following content is also included:

[0119] Specifically, the system retrieves historical feedback information for each associated user regarding the warning information, including read and unread data; retrieves the read time for each associated user's historical read feedback regarding the warning information; retrieves the sending time of the warning information for which historical read feedback was obtained; calculates the time difference for each read warning information based on the read time and the sending time; counts the number of warning information filtered if the time difference is greater than a preset difference; retrieves the total number of warning information; calculates the ratio of the number of filtered warning information to the total number of warning information; and deletes the associated users whose ratio is less than a preset ratio when the ratio is less than the preset ratio.

[0120] In this embodiment, taking one associated person as an example, the electronic device obtains the total number of warning messages received by the associated person and obtains feedback information for each warning message, wherein the feedback information is read or unread. The warning messages with read feedback information are extracted, and the read time and sending time of the extracted warning messages are obtained. The time difference between the sending time and the read time is calculated. After obtaining the time difference, the time difference is compared with a preset difference. The number of warning messages with a value greater than the preset difference is counted. The counted number is used as the filtering number. When the filtering number is obtained, the filtering number is used as the numerator and the total number is used as the denominator to calculate the number ratio. The number ratio is compared with a preset number ratio. When the number ratio is less than the preset number ratio, the associated user corresponding to the number ratio less than the preset number ratio is deleted.

[0121] It should be noted that the prerequisite for performing the deletion of associated users is that the total number is greater than the preset value, which can be set as needed and is not specifically limited.

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

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

[0124] Figure 4 This application provides a structural block diagram of an electroencephalogram (EEG) abnormality analysis device 500. Figure 4 As shown, the EEG abnormality analysis device 500 mainly includes:

[0125] The first acquisition module 501 is used to acquire the first EEG data and first related parameters of the current user acquired by the EEG acquisition device. The first related parameters include the basic information of the current user and the acquisition time of the first EEG data.

[0126] The second acquisition module 502 is used to acquire the second relevant parameters of historical users in the EEG threshold database. The second relevant parameters include the basic information of the historical users and the time interval corresponding to the EEG threshold range.

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

[0128] The determination module 504 is used to determine the EEG threshold range based on the selected second relevant parameter, and to use the determined EEG threshold range as a reference threshold range.

[0129] The comparison module 505 is used to compare the first EEG data with a reference threshold range. When the first EEG data is not within the reference threshold range, an early warning message is generated.

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

[0131] The conversion submodule is used to convert gender and disease type into first and second information to be calculated based on preset rules.

[0132] The information acquisition submodule is used to acquire the third piece of information to be calculated corresponding to the time interval to which the acquisition time belongs.

[0133] The first calculation submodule is used to calculate the filter value based on the current user's first, second, and third information to be calculated and age.

[0134] The reference value acquisition submodule is used to select a reference value that matches the filter value from the EEG threshold database. The second related parameter corresponding to the selected reference value is used as the second related parameter that matches the first related parameter. The reference value is calculated from the basic information of the historical user and the time interval corresponding to the EEG threshold range. Among them, 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 electroencephalogram (EEG) abnormality analysis device 500 further includes:

[0136] The parameter acquisition module is used to acquire the current user's first physiological parameters before generating the warning information. The first physiological parameters include blood pressure parameters and heart rate parameters.

[0137] The status judgment module is used to determine whether the current user's current status is abnormal based on blood pressure and heart rate parameters. If so, it uses a new threshold range determined based on blood pressure and heart rate parameters as a reference threshold range and performs a step of comparing the first EEG data with the reference threshold range.

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

[0139] The first data acquisition module is used to acquire the current user's second physiological data in real time.

[0140] The second data acquisition module is used to acquire the second electroencephalogram (EEG) data of the current user when the current user's current state is determined to be normal based on the second physiological data.

[0141] The range determination module is used to use the EEG threshold range determined by the second relevant parameter as the reference threshold range;

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

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

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

[0145] The segmentation module is used to divide EEG data within the same time interval into an analysis group when the number of EEG tests and the test time meet the preset requirements.

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

[0147] As an optional implementation of this embodiment, the electroencephalogram (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 warning information to the mobile terminals of associated users based on their contact information.

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

[0151] The feedback information acquisition module is used to acquire the historical feedback information of each associated user on the warning information after the warning information is sent to the mobile terminal corresponding to the associated user based on the contact information. The historical feedback information includes read and unread information.

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

[0153] The second time acquisition module is used to acquire the sending time of warning information that has been read in the past.

[0154] The time calculation module is used to calculate the time difference of each read warning message based on the read time and the sending time;

[0155] The statistics module is used to count the number of early warning messages whose time difference exceeds a preset value.

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

[0157] The ratio calculation module is used to calculate the ratio of the number of samples to the total number of samples.

[0158] The deletion module is used to delete associated users whose quantity ratio is less than a preset quantity ratio when the quantity ratio is less than the preset quantity ratio.

[0159] The functional modules in the embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as 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 this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of an electroencephalogram (EEG) abnormality analysis method according to various embodiments of this application.

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

[0161] Figure 5 This is a structural block diagram of an electronic device 600 provided in an embodiment of this application. (See diagram below.) 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 electroencephalogram (EEG) abnormality analysis method as provided in the above embodiments, which can be loaded and executed by the processor 602.

[0162] The memory 601 can be used to store instructions, programs, code, 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 the electroencephalogram (EEG) abnormality analysis method provided in the above embodiments; the data storage area may store data involved in the EEG abnormality analysis method provided in the above embodiments.

[0163] Processor 602 may include one or more processing cores. Processor 602 executes instructions, programs, code sets, or instruction sets stored in memory 601, and calls data stored in memory 601 to perform various functions and process data as described in this application. Processor 602 may be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 602 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0164] The communication bus 603 may include a path for transmitting information between the aforementioned components. The communication bus 603 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 603 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0165] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, a method for analyzing electroencephalogram (EEG) abnormalities.

[0166] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can 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 can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

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

[0168] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for analyzing abnormalities in electroencephalograms (EEGs), characterized in that, include: Acquire the first EEG data and first related parameters of the current user collected by the EEG acquisition device, wherein the first related parameters include the basic information of the current user and the acquisition time of the first EEG data; Obtain a second relevant parameter from the historical user database of the electroencephalogram (EEG) threshold database. The second relevant parameter includes the basic information of the historical user and the time interval corresponding to the EEG threshold range. Select a second relevant parameter that matches the first relevant parameter; The EEG threshold range is determined based on the selected second relevant parameter, and the determined EEG threshold range is used as the reference threshold range. The first EEG data is compared with the reference threshold range. When the first EEG data is not within the reference threshold range, a warning message is generated. The basic information includes age, gender, and disease type; the selection of a second relevant parameter that matches the first relevant parameter includes: Based on preset rules, the gender and disease type are converted into first and second information to be calculated. Obtain the third information to be calculated corresponding to the time interval to which the acquisition time belongs; Calculate the filter value based on the first, second, and third information to be calculated and the age of the current user; A reference value matching the screening value is selected from the EEG 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. The reference value is calculated from 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; Following the step of using the determined EEG threshold range as a reference threshold range, the method further includes: Obtain the number of EEG tests and the test time for 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 an analysis group; The EEG data in each analysis group were compared pairwise to determine whether the EEG trend had changed. If not, the current user's EEG threshold range is determined based on the EEG data in the analysis group, and the determined current user's EEG threshold range is used as the reference threshold range.

2. The method for analyzing abnormal electroencephalograms according to claim 1, characterized in that, Before generating the warning information, the following is also included: Obtain the first physiological parameters of the current user, the first physiological parameters including blood pressure parameters and heart rate parameters; Based on the blood pressure parameters and the heart rate parameters, determine whether the current user's current state 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 the reference threshold range, and the step of comparing the first EEG data with the reference threshold range is performed.

3. The method for analyzing abnormal electroencephalograms according to claim 2, characterized in that, The electroencephalogram (EEG) abnormality analysis method also includes: Real-time acquisition of the current user's second physiological data; When the current state of the current user is determined to be normal based on the second physiological data, the second electroencephalogram (EEG) data of the current user is acquired. The EEG threshold range determined by the second relevant parameter is used as the reference threshold range; The second EEG data is compared with the reference threshold range.

4. The method for analyzing abnormal electroencephalograms according to claim 1, characterized in that, After generating the warning information, the following is also included: Obtain the contact information of the current user's associated users; The warning information will be sent to the mobile terminal corresponding to the associated user based on the contact information provided.

5. The method for analyzing abnormal electroencephalograms according to claim 4, 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: Obtain historical feedback information of each associated user regarding the warning information, the historical feedback information including read and unread status; Obtain the historical feedback information of each associated user regarding the warning information, including the read time; Obtain the sending time of the historical feedback information that indicates the warning information has been read; Calculate the time difference for each read warning message based on the read time and the sending time; Count the number of early warning messages whose time difference is greater than a preset difference. Obtain the total amount of the aforementioned early warning information; Calculate the ratio of the number of samples selected to the total number of samples. When the quantity ratio is less than a preset quantity ratio, the associated users corresponding to the quantity ratio being less than the preset quantity ratio are deleted.

6. An electroencephalogram (EEG) abnormality analysis apparatus for implementing the EEG abnormality analysis method according to any one of claims 1 to 5, characterized in that, include: The first acquisition module is used to acquire the first electroencephalogram (EEG) data and first related parameters of the current user collected by the EEG acquisition device. The first related parameters include the basic information of the current user and the acquisition time of the first EEG data. The second acquisition module is used to acquire the second relevant parameters of historical users in the EEG threshold database. The second relevant parameters include the basic information of the historical users and the time interval corresponding to the EEG threshold range. The selection module is used to select a second relevant parameter that matches the first relevant parameter. The determination module is used to determine the EEG threshold range based on the selected second relevant parameter, and to use the determined EEG threshold range as a reference threshold range. The comparison module is used to compare the first EEG data with the reference threshold range, and generate a warning message when the first EEG data is not within the reference threshold range.

7. An electronic device, characterized in that, It includes 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 performs the electroencephalogram abnormality analysis method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Includes a computer program that, when run on a computer, causes the computer to perform the electroencephalogram abnormality analysis method as described in any one of claims 1 to 5.

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