Abnormal discharge detection system, method, electronic device and storage medium for anterior temporal region

Abstract

The present application relates to a detection system, method, electronic device and storage medium for abnormal discharge in the anterior temporal region, and belongs to the technical field of digital electroencephalogram signal processing and analysis. The detection system for abnormal discharge in the anterior temporal region obtains the deviation from the mean and the target frequency band corresponding to each lead according to the original electroencephalogram data of each lead in each sampling interval, and finally judges the abnormal discharge in the anterior temporal region according to the first ratio corresponding to the left anterior temporal lead and the second ratio corresponding to the right anterior temporal lead, realizing the automation and intelligence of the processing of the original electroencephalogram data. Compared with manual processing, it saves a lot of manpower and material resources, and there will be no omission of data, so that the efficiency of processing the original electroencephalogram data is greatly improved, and the rapid screening of abnormal discharge in the anterior temporal region can be realized. The detection system for abnormal discharge in the anterior temporal region is more objective and accurate, and the subjective error of manual screening is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of digital electroencephalogram signal processing and analysis, and in particular to a detection system, method, electronic device and storage medium for abnormal discharge in the anterior temporal region. Background Art

[0002] Epilepsy is a common paroxysmal disease in neurology, and electroencephalogram (EEG) is the preferred examination method for diagnosing epilepsy. During an epileptic seizure, abnormal discharges in the patient's cerebral cortex can be monitored. Therefore, by analyzing the patient's EEG, it is possible to diagnose whether the patient has epilepsy or the severity of the disease.

[0003] In the prior art, the patient's EEG is usually processed manually. An experienced doctor needs 10 to 30 minutes to complete a one-hour video EEG diagnosis report. Therefore, when the amount of EEG data obtained is large, the doctor needs to spend more time on diagnosis and processing. It can be seen that manual processing not only consumes a lot of time, but also may result in omissions or misinterpretation, thereby causing errors.

[0004] Application Contents

[0005] In view of the shortcomings of the prior art that manual processing not only consumes a lot of time, but may also result in omissions or misinterpretations, causing errors, the present application provides a detection system, method, electronic device and storage medium for abnormal discharges in the anterior temporal region, which can solve at least one of the above shortcomings. The technical solution is as follows:

[0006] In a first aspect, a system for detecting abnormal discharges in the anterior temporal region is provided, the system comprising:

[0007] A data acquisition module, used for acquiring raw EEG data of each lead in each sampling interval of a plurality of leads, wherein the plurality of leads at least include a left anterior temporal lead and a right anterior temporal lead;

[0008] A first data processing module is used to determine, for each of the leads, the deviation from the mean corresponding to the lead in each of the sampling intervals according to the original EEG data corresponding to the lead;

[0009] A second data processing module is used to filter the deviations from the mean corresponding to each lead within the total sampling time, and obtain the target frequency band that meets the set frequency condition among the deviations from the mean corresponding to each lead, wherein the total sampling time is the sum of all sampling intervals;

[0010] The third data processing module is used to obtain the dynamic time planning distance between the target frequency bands of any two leads by using the dynamic time planning algorithm, and obtain the dynamic time planning distance mean corresponding to each lead in each of the leads according to each of the dynamic time planning distances, and determine the minimum dynamic time planning distance mean from each of the dynamic time planning distance means;

[0011] A fourth data processing module, used to determine a first ratio of the mean dynamic time planning distance corresponding to the left anterior temporal lead to the mean minimum dynamic time planning distance, and a second ratio of the mean dynamic time planning distance corresponding to the right anterior temporal lead to the mean minimum dynamic time planning distance;

[0012] The result output module is used for indicating abnormal discharge in the anterior temporal region when any one of the first ratio or the second ratio is greater than a first preset value.

[0013] The beneficial effects of the abnormal discharge detection system of the anterior temporal region of the present application are as follows:

[0014] The detection system for abnormal discharges in the anterior temporal region uses the original EEG data of each lead in each sampling interval to obtain the deviation from the mean and the target frequency band corresponding to each lead based on the original EEG data, and finally judges the abnormal discharges in the anterior temporal region based on the first ratio corresponding to the left anterior temporal lead and the second ratio corresponding to the right anterior temporal lead, thereby realizing the automation and intelligent processing of raw EEG data. Compared with manual processing, it saves a lot of manpower and material resources, and there will be no data omissions, which greatly improves the efficiency of processing raw EEG data, and can realize rapid screening of abnormal discharges in the anterior temporal region. The detection system for abnormal discharges in the anterior temporal region is more objective and accurate, and reduces the subjective errors of manual screening.

[0015] On the basis of the above solution, the detection system of abnormal discharge in the anterior temporal region of the present application can also be improved as follows.

[0016] Furthermore, the result output unit is further configured to indicate that the anterior temporal region is normal when either the first ratio or the second ratio is less than a second preset value;

[0017] The second preset value is smaller than the first preset value.

[0018] The beneficial effect of adopting the above further scheme is that it can not only indicate abnormal discharges in the anterior temporal area, but also indicate that the anterior temporal area is normal, reducing the workload of manual operation and directly presenting the results corresponding to the original EEG data, which is convenient and fast.

[0019] Furthermore, the first preset value is 1.3, and the second preset value is 1.1.

[0020] Furthermore, the above-mentioned frequency setting condition is: the frequency band is greater than 8 Hz and not greater than 13 Hz.

[0021] The beneficial effect of adopting the above further solution is that by limiting the specific values ​​of the first preset value and the second preset value, as well as the specific setting frequency conditions, the automation of the raw EEG data processing process is further achieved.

[0022] Furthermore, the abnormal discharge in the anterior temporal region includes abnormal discharge in the left anterior temporal region and abnormal discharge in the right anterior temporal region, and the result output unit is specifically used to: when the first ratio is greater than a first preset value, it indicates abnormal discharge in the left anterior temporal region; when the second ratio is greater than the first preset value, it indicates abnormal discharge in the right anterior temporal region.

[0023] The beneficial effect of adopting the above further solution is that the area of ​​abnormal discharge is located to the left anterior temporal region or the right anterior temporal region through more specific result output, which is more referenceable.

[0024] Further, the multiple leads include: left frontal lead, right frontal lead, left frontal lead, right frontal lead, left central lead, right central lead, left parietal lead, right parietal lead, left occipital lead, right occipital lead, left anterior temporal lead, right anterior temporal lead, left middle temporal lead, right middle temporal lead, left posterior temporal lead, right posterior temporal lead, frontal midline lead, central midline lead, and parietal midline lead.

[0025] In a second aspect, a method for detecting abnormal discharges in the anterior temporal region is provided, the method comprising:

[0026] Acquire raw EEG data of each lead in a plurality of leads in each sampling interval, wherein the plurality of leads at least include a left anterior temporal lead and a right anterior temporal lead;

[0027] For each of the leads, determining the deviation from the mean corresponding to the lead in each of the sampling intervals according to the original EEG data corresponding to the lead;

[0028] Within the total sampling time, each deviation from the mean corresponding to each lead is filtered to obtain a target frequency band that meets the set frequency condition among each deviation from the mean corresponding to each lead, wherein the total sampling time is the sum of all sampling intervals;

[0029] Using a dynamic time planning algorithm, a dynamic time planning distance between target frequency bands of any two leads is obtained, and according to each of the dynamic time planning distances, a mean of the dynamic time planning distances corresponding to each of the leads is obtained, and a minimum dynamic time planning distance mean is determined from the means of the dynamic time planning distances;

[0030] Determine a first ratio of the mean dynamic time planning distance corresponding to the left anterior temporal lead to the mean minimum dynamic time planning distance, and a second ratio of the mean dynamic time planning distance corresponding to the right anterior temporal lead to the mean minimum dynamic time planning distance;

[0031] When any one of the first ratio or the second ratio is greater than a first preset value, it indicates abnormal discharge in the anterior temporal region.

[0032] The beneficial effects of the method for detecting abnormal discharges in the anterior temporal region of the present application are as follows:

[0033] The detection system for abnormal discharges in the anterior temporal region uses the original EEG data of each lead in each sampling interval to obtain the deviation from the mean and the target frequency band corresponding to each lead based on the original EEG data, and finally judges the abnormal discharges in the anterior temporal region based on the first ratio corresponding to the left anterior temporal lead and the second ratio corresponding to the right anterior temporal lead, thereby realizing the automation and intelligent processing of raw EEG data. Compared with manual processing, it saves a lot of manpower and material resources, and there will be no data omissions, which greatly improves the efficiency of processing raw EEG data, and can realize rapid screening of abnormal discharges in the anterior temporal region. The detection system for abnormal discharges in the anterior temporal region is more objective and accurate, and reduces the subjective errors of manual screening.

[0034] On the basis of the above technical solution, the method for detecting abnormal discharges in the anterior temporal region of the present application can also be improved as follows.

[0035] Furthermore, the method further includes: when any of the first ratio or the second ratio is less than a second preset value, it indicates that the anterior temporal region is normal;

[0036] The second preset value is smaller than the first preset value.

[0037] The beneficial effect of adopting the above further scheme is that it can not only indicate abnormal discharges in the anterior temporal area, but also indicate that the anterior temporal area is normal, reducing the workload of manual operation and directly presenting the results corresponding to the original EEG data, which is convenient and fast.

[0038] According to a third aspect, an electronic device is provided, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the functions of the system according to the first aspect when executed.

[0039] In a fourth aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is loaded and executed by a processor to implement the functions of the system described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present application is further described below in conjunction with the accompanying drawings and embodiments.

[0041] Figure 1 It is a structural schematic diagram of a detection system for abnormal discharges in the anterior temporal region according to an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the steps of a method for detecting abnormal discharges in the anterior temporal region according to an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0045] Figure 1 1 is a schematic diagram of a detection system 100 for abnormal discharge in the anterior temporal region provided in an embodiment of the present application. The embodiment includes:

[0046] The data acquisition module 110 is used to acquire the original EEG data of each lead in each sampling interval, and the multiple leads at least include a left anterior temporal lead and a right anterior temporal lead.

[0047] The first data processing module 120 is used to determine, for each lead, the deviation from the mean corresponding to the lead in each sampling interval according to the original EEG data corresponding to the lead.

[0048] The second data processing module 130 is used to filter the deviations from the mean corresponding to each lead within the total sampling time, and obtain the target frequency band that meets the set frequency condition in the deviations from the mean corresponding to each lead, wherein the total sampling time is the sum of all sampling intervals.

[0049] The third data processing module 140 is used to use the dynamic time planning algorithm to obtain the dynamic time planning distance between the target frequency bands of any two leads, and according to each dynamic time planning distance, obtain the dynamic time planning distance mean corresponding to each lead in each lead, and determine the minimum dynamic time planning distance mean from each dynamic time planning distance mean.

[0050] The fourth data processing module 150 is used to determine a first ratio of the mean dynamic time planning distance corresponding to the left anterior temporal lead to the mean minimum dynamic time planning distance, and a second ratio of the mean dynamic time planning distance corresponding to the right anterior temporal lead to the mean minimum dynamic time planning distance.

[0051] The result output module 160 is configured to indicate abnormal discharge in the anterior temporal region when either the first ratio or the second ratio is greater than a first preset value.

[0052] Optionally, abnormal discharge in the anterior temporal region includes abnormal discharge in the left anterior temporal region and abnormal discharge in the right anterior temporal region, and the result output unit is specifically used to: when the first ratio is greater than the first preset value, it indicates abnormal discharge in the left anterior temporal region; when the second ratio is greater than the first preset value, it indicates abnormal discharge in the right anterior temporal region.

[0053] Optionally, multiple leads include: left frontal lead, right frontal lead, left frontal lead, right frontal lead, left central lead, right central lead, left parietal lead, right parietal lead, left occipital lead, right occipital lead, left anterior temporal lead, right anterior temporal lead, left middle temporal lead, right middle temporal lead, left posterior temporal lead, right posterior temporal lead, frontal midline lead, central midline lead, and parietal midline lead.

[0054] Among them, the dynamic time warping (DTW) algorithm is a method for calculating the similarity of two time series, which uses the dynamic programming DP (dynamic programming) method to perform time warping calculations. For example, the two sequences have very similar shapes as a whole, but these shapes are not aligned on the time axis. Therefore, before comparing their similarities, one (or both) of the sequences needs to be warped on the time axis to achieve better alignment, and then the similarity is compared. The result is expressed as the dynamic time planning distance (Warping Distance), which can also be called the DTW distance. The larger the distance, the lower the similarity, and the smaller the distance, the higher the similarity.

[0055] At present, in the process of collecting raw EEG data, common EEG equipment includes 19 cortical electrodes, and the electrodes are placed using the 10-20 international calibration placement. The corresponding positions of the electrodes are: left frontal Fp1, right frontal Fp2, left frontal F3, right frontal F4, left central C3, right central C4, left parietal P3, right parietal P4, left occipital O1, right occipital O2, left anterior temporal F7, right anterior temporal F8, left middle temporal T3, right middle temporal T4, left posterior temporal T5, right posterior temporal T6, frontal midline Fz, central midline Cz, parietal midline Pz, and the sampling frequency is 256 sampling points per second.

[0056] Among them, in the process of using the dynamic time planning algorithm to obtain the dynamic time planning distance between the target frequency bands of any two leads, the corresponding result can be represented by a matrix, the elements in the matrix represent the dynamic time planning distance between the target frequency bands of any two leads, and each row and column represents a lead respectively.

[0057] Optionally, the result output unit is further used to indicate that the anterior temporal region is normal when either the first ratio or the second ratio is less than a second preset value; wherein the second preset value is less than the first preset value.

[0058] Optionally, the normal anterior temporal region includes a normal left anterior temporal region and a normal right anterior temporal region, and the result output unit is specifically used to: when the first ratio is less than the second preset value, it indicates that the left anterior temporal region is normal; when the second ratio is less than the second preset value, it indicates that the right anterior temporal region is normal, wherein the second preset value is less than the first preset value.

[0059] Optionally, the first preset value is 1.3 and the second preset value is 1.1.

[0060] Optionally, the frequency condition is set as follows: the frequency band is greater than 8 Hz and not greater than 13 Hz.

[0061] Specifically, the frequency band greater than 8Hz and less than 13Hz can be called alpha waves. Alpha waves are 8-13Hz rhythms that appear at the back of the head when a person is awake. Most people have 9-10Hz, with an amplitude of 10-100μV. This is the basic rhythm of normal people, and the amplitude is generally highest in the occipital region. It is easy to appear when a person closes his eyes and relaxes his mind. It is roughly symmetrical on both sides and is the most important indicator for analyzing EEG background activity. It is closely related to the state of brain function and development level.

[0062] In order to better illustrate and understand the principle of the system provided by the present application, the scheme of the present application is described below in conjunction with an optional specific embodiment. It should be noted that the specific functions implemented by each module in the specific embodiment should not be understood as a limitation on the scheme of the present application. On the basis of the principle of the scheme provided by the present application, other implementation methods that can be thought of by those skilled in the art should also be considered within the scope of protection of the present application.

[0063] Assume that within a certain sampling interval, the raw EEG data of the right anterior temporal F8 lead collected is P F8 , the original EEG data of the left frontal Fp1 lead is P Fp1 , the original EEG data of the right frontal Fp2 lead is P Fp2 , the original EEG data of left frontal F3 lead is P F3 , the original EEG data of the right frontal F4 lead is P F4 , the original EEG data of the left central C3 lead is P C3 , the original EEG data of the right central C4 lead is P C4 , the original EEG data of the left parietal P3 lead is P P3 , the original EEG data of the right parietal P4 lead is P P4 , the original EEG data of left occipital O1 lead is P O1 , the original EEG data of the right occipital O2 lead is PO2 , the original EEG data of left anterior temporal lead F7 is P F7 , the original EEG data of left middle temporal T3 lead is P T3 , the original EEG data of right middle temporal T4 lead is P T4 , the original EEG data of left posterior temporal T5 lead is P T5 , the original EEG data of right posterior temporal T6 lead is P T6 The original EEG data of the frontal midline Fz lead is P Fz , the original EEG data of the central midline Cz lead is P Cz The original EEG data of the top midline Pz lead is P Pz .

[0064] In this sampling interval, take the right anterior temporal F8 lead as an example:

[0065] The corresponding deviation from the mean of right anterior temporal lead F8 is P F8-ave For: P F8-ave =P F8 -(P Fp1 +P Fp2 +P F3 +P F4 +P C3 +P C4 +P P3 +P P4 +P O1 +P O2 +P F7 +P F8 +P T3 +P T4 +P T5 +P T6 +P Fz +P Cz +P Pz ) / 19.

[0066] Assume that the original EEG data of 4 leads are obtained based on 7 sampling intervals. For example, the original EEG data P of the left frontal F3 lead are obtained. F3 , the original EEG data of the right frontal F4 lead P F4 , the original EEG data of left anterior temporal F7 lead P F7 and the original EEG data of right anterior temporal lead F8 F8 , then the total sampling time is the sum of the 7 sampling intervals, and the original EEG data of each sampling interval is defined as a group of data. Then, 7 groups of data can be obtained within the total sampling time.

[0067] Assume that the first set of data obtained in the first sampling interval is: P F3 =2,P F4 =3,P F7=1,P F8 =2, corresponding to the mean AVE1 = 2; the second set of data obtained in the second sampling interval is: P F3 =4,P F4 =4,P F7 =2,P F8 =3, corresponding to the mean AVE2 = 3.25; the third set of data obtained in the third sampling interval is: P F3 =6, P F4 =7,P F7 =3,P F8 =4, corresponding to the mean AVE3 = 5; the fourth set of data obtained in the fourth sampling interval is: P F3 =8,P F4 =8,P F7 =4,P F8 =3, corresponding to the mean value AVE4 = 5.75; the fifth set of data obtained in the fifth sampling interval is: P F3 =6, P F4 =7,P F7 =3,P F8 =3, corresponding to the mean AVE5 = 4.75; the sixth set of data obtained in the sixth sampling interval is: P F3 =4,P F4 =5,P F7 =2,P F8 =1, corresponding to the mean value AVE6 = 3; the seventh group of data obtained in the seventh sampling interval is: P F3 =2,P F4 =3,P F7 =1,P F8 =1, corresponding to the mean AVE7=1.75.

[0068] In the total sampling time, for the right anterior temporal F8 lead, there is a mean difference P in the first sampling interval. F8-ave1 =0, the deviation from the mean within the second sampling interval is P F8-ave2 =-0.25, the deviation from the mean in the third sampling interval P F8-ave3 =-1, the deviation from the mean within the fourth sampling interval P F8-ave4 =-2.75, the deviation from the mean in the fifth sampling interval P F8-ave5 =-1.75, the deviation from the mean in the sixth sampling interval P F8-ave6 =-2, the deviation from the mean within the 7th sampling interval P F8-ave7 =-0.75.

[0069] Within the total sampling time, all deviations from the mean corresponding to the right anterior temporal F8 lead are represented by connecting different points into lines, and each point corresponds to a deviation from the mean. After filtering, the first target frequency band greater than 8 Hz and not greater than 13 Hz corresponding to the right anterior temporal F8 lead can be obtained.

[0070] Using similar principles as above, the second target frequency band greater than 8 Hz and not greater than 13 Hz corresponding to the left frontal F3 lead, the third target frequency band greater than 8 Hz and not greater than 13 Hz corresponding to the right frontal F4 lead, and the fourth target frequency band greater than 8 Hz and not greater than 13 Hz corresponding to the left anterior temporal F7 lead can be obtained.

[0071] The dynamic time planning algorithm is used to obtain the dynamic time planning distances between the second target frequency band and the first target frequency band, the second target frequency band and the third target frequency band, the second target frequency band and the fourth target frequency band, the first target frequency band and the third target frequency band, the first target frequency band and the fourth target frequency band, and the third target frequency band and the fourth target frequency band. Assuming that the dynamic time planning distances are 1.5, 2, 3, 1, 0.7, and 4, respectively, the mean dynamic time planning distance corresponding to the right anterior temporal F8 lead is (1.5+1+0.7) / 3≈1.07, the mean dynamic time planning distance corresponding to the left frontal F3 lead is (1.5+2+3) / 3≈2.17, the mean dynamic time planning distance corresponding to the right frontal F4 lead is (2+1+4) / 3≈2.33, and the mean dynamic time planning distance corresponding to the left anterior temporal F7 lead is (3+0.7+4) / 3≈2.57. The minimum dynamic time planning distance mean is 1.07.

[0072] Then, the second ratio of the dynamic time planning distance mean corresponding to the right anterior temporal lead to the minimum dynamic time planning distance mean is 1.07 / 1.07=1. Therefore, the second ratio is less than the second preset value 1.1, indicating that the right anterior temporal region is normal. The first ratio of the dynamic time planning distance mean corresponding to the left anterior temporal lead to the minimum dynamic time planning distance mean is 2.57 / 1.07≈2.40. Therefore, the first ratio is greater than the first preset value 1.3, indicating abnormal discharge in the left anterior temporal region.

[0073] Based on the same technical concept, the embodiment of the present application also provides a method for detecting abnormal discharge in the anterior temporal region, such as Figure 2 As shown, the method includes:

[0074] Step S1, obtaining original EEG data of each lead in each sampling interval of a plurality of leads, wherein the plurality of leads at least include a left anterior temporal lead and a right anterior temporal lead.

[0075] Step S2: for each lead, determine the deviation from the mean corresponding to the lead in each sampling interval according to the original EEG data corresponding to the lead.

[0076] Step S3: within the total sampling time, filter the deviations from the mean corresponding to each lead to obtain the target frequency band that meets the set frequency condition among the deviations from the mean corresponding to each lead, wherein the total sampling time is the sum of all sampling intervals.

[0077] Step S4: using a dynamic time planning algorithm, obtain the dynamic time planning distance between the target frequency bands of any two leads, and according to each dynamic time planning distance, obtain the dynamic time planning distance mean corresponding to each lead in each lead, and determine the minimum dynamic time planning distance mean from each dynamic time planning distance mean.

[0078] Step S5, determining a first ratio of the mean dynamic time planning distance corresponding to the left anterior temporal lead to the mean minimum dynamic time planning distance, and a second ratio of the mean dynamic time planning distance corresponding to the right anterior temporal lead to the mean minimum dynamic time planning distance.

[0079] Step S6: When either the first ratio or the second ratio is greater than a first preset value, it indicates abnormal discharge in the anterior temporal region.

[0080] Optionally, abnormal discharge in the anterior temporal region includes abnormal discharge in the left anterior temporal region and abnormal discharge in the right anterior temporal region. When either the first ratio or the second ratio is greater than a first preset value, abnormal discharge in the anterior temporal region is indicated, including: when the first ratio is greater than the first preset value, abnormal discharge in the left anterior temporal region is indicated; when the second ratio is greater than the first preset value, abnormal discharge in the right anterior temporal region is indicated.

[0081] Optionally, multiple leads include: left frontal lead, right frontal lead, left frontal lead, right frontal lead, left central lead, right central lead, left parietal lead, right parietal lead, left occipital lead, right occipital lead, left anterior temporal lead, right anterior temporal lead, left middle temporal lead, right middle temporal lead, left posterior temporal lead, right posterior temporal lead, frontal midline lead, central midline lead, and parietal midline lead.

[0082] Optionally, it also includes: when any ratio of the first ratio or the second ratio is smaller than a second preset value, it indicates that the anterior temporal region is normal; wherein the second preset value is smaller than the first preset value.

[0083] Optionally, a normal anterior temporal region includes a normal left anterior temporal region and a normal right anterior temporal region, and when either the first ratio or the second ratio is less than a second preset value, it indicates that the anterior temporal region is normal, including: when the first ratio is less than the second preset value, it indicates that the left anterior temporal region is normal; when the second ratio is less than the second preset value, it indicates that the right anterior temporal region is normal, wherein the second preset value is less than the first preset value.

[0084] Optionally, the first preset value is 1.3 and the second preset value is 1.1.

[0085] Optionally, the frequency condition is set as follows: the frequency band is greater than 8 Hz and not greater than 13 Hz.

[0086] It should be noted that: the above embodiment provides a detection system for abnormal discharge in the anterior temporal region, when prompting abnormal discharge in the anterior temporal region, only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above. In addition, the above embodiment provides a detection system for abnormal discharge in the anterior temporal region and an embodiment of a detection method for abnormal discharge in the anterior temporal region, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0087] Figure 3 The block diagram of the structure of an electronic device 300 provided by an exemplary embodiment of the present application is shown. The electronic device 300 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The electronic device 300 may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0088] Typically, the electronic device 300 includes a processor 310 and a memory 320 .

[0089] The processor 310 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 310 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 310 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 310 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 310 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0090] The memory 320 may include one or more computer-readable storage media, which may be non-transitory. The memory 320 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash memory storage devices. The memory 320 stores a computer program 321 that can be run on the processor 310. When the computer program 321 is run, the function of the detection system for abnormal discharge in the anterior temporal region in the present application can be realized.

[0091] In some embodiments, the electronic device 300 may also optionally include: a peripheral device interface and at least one peripheral device. The processor 310, the memory 320 and the peripheral device interface may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a display screen, a camera assembly, an audio circuit and a power supply.

[0092] The peripheral device interface can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 310 and the memory 320. In some embodiments, the processor 310, the memory 320 and the peripheral device interface are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 310, the memory 320 and the peripheral device interface can be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0093] The display screen is used to display the UI (User Interface). The UI may include graphics, text, icons, videos and any combination thereof. When the display screen is a touch screen, the display screen also has the ability to collect touch signals on the surface or above the surface of the display screen. The touch signal can be input to the processor as a control signal for processing. At this time, the display screen can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen can be one, arranged on the front panel of the electronic device 300; in other embodiments, the display screen can be at least two, respectively arranged on different surfaces of the electronic device 300 or in a folding design; in other embodiments, the display screen can be a flexible display screen, arranged on a curved surface or a folding surface of the electronic device 300. Even, the display screen can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode) and the like.

[0094] The camera assembly is used to capture images or videos. Optionally, the camera assembly includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize the panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0095] The audio circuit may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor for processing, or input them into the radio frequency circuit to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the electronic device 300. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor or the radio frequency circuit into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit may also include a headphone jack.

[0096] The power supply is used to power various components in the electronic device 300. The power supply can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When the power supply includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged through a wired line, and a wireless rechargeable battery is a battery that is charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0097] Those skilled in the art will understand that Figure 3 The structure shown in the figure does not constitute a limitation on the electronic device 300, and may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0098] In an exemplary embodiment, a computer-readable storage medium is also provided, in which at least one instruction is stored, and the above instruction can be loaded and executed by a processor to implement the function of the detection system for abnormal discharge in the anterior temporal region in the above embodiment. The storage medium can be non-transient. For example, the storage medium can be ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0099] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A detection system for abnormal discharge in the anterior temporal region, characterized in that: include: A data acquisition module, used for acquiring raw EEG data of each lead in each sampling interval of a plurality of leads, wherein the plurality of leads at least include a left anterior temporal lead and a right anterior temporal lead; A first data processing module is used to determine, for each of the leads, the deviation from the mean corresponding to the lead in each of the sampling intervals according to the original EEG data corresponding to the lead; A second data processing module is used to filter the deviations from the mean corresponding to each lead within the total sampling time, and obtain the target frequency band that meets the set frequency condition among the deviations from the mean corresponding to each lead, wherein the total sampling time is the sum of all sampling intervals; The third data processing module is used to obtain the dynamic time planning distance between the target frequency bands of any two leads by using the dynamic time planning algorithm, and obtain the dynamic time planning distance mean corresponding to each lead in each of the leads according to each of the dynamic time planning distances, and determine the minimum dynamic time planning distance mean from each of the dynamic time planning distance means; A fourth data processing module, used to determine a first ratio of the mean dynamic time planning distance corresponding to the left anterior temporal lead to the mean minimum dynamic time planning distance, and a second ratio of the mean dynamic time planning distance corresponding to the right anterior temporal lead to the mean minimum dynamic time planning distance; The result output module is used for indicating abnormal discharge in the anterior temporal region when any one of the first ratio or the second ratio is greater than a first preset value.

2. A detection system for abnormal discharge in the anterior temporal region according to claim 1, characterized in that: The result output module is further configured to indicate that the anterior temporal region is normal when either the first ratio or the second ratio is less than a second preset value; The second preset value is smaller than the first preset value.

3. A detection system for abnormal discharge in the anterior temporal region according to claim 2, characterized in that: The first preset value is 1.3, and the second preset value is 1.

1.

4. The detection system for abnormal discharge in the anterior temporal region according to claim 1, characterized in that: The setting frequency condition is: the frequency band is greater than 8 Hz and not greater than 13 Hz.

5. The system for detecting abnormal discharge in the anterior temporal region according to claim 1, characterized in that: The abnormal discharge in the anterior temporal region includes abnormal discharge in the left anterior temporal region and abnormal discharge in the right anterior temporal region. The result output module is specifically used to: when the first ratio is greater than a first preset value, it indicates abnormal discharge in the left anterior temporal region; when the second ratio is greater than the first preset value, it indicates abnormal discharge in the right anterior temporal region.

6. A detection system for abnormal discharge in the anterior temporal region according to any one of claims 1 to 5, characterized in that: The multiple leads include: left frontal lead, right frontal lead, left frontal lead, right frontal lead, left central lead, right central lead, left parietal lead, right parietal lead, left occipital lead, right occipital lead, left anterior temporal lead, right anterior temporal lead, left middle temporal lead, right middle temporal lead, left posterior temporal lead, right posterior temporal lead, frontal midline lead, central midline lead, and parietal midline lead.

7. A method for detecting abnormal discharges in the anterior temporal region, characterized in that: include: Acquire raw EEG data of each lead in a plurality of leads in each sampling interval, wherein the plurality of leads at least include a left anterior temporal lead and a right anterior temporal lead; For each of the leads, determining the deviation from the mean corresponding to the lead in each of the sampling intervals according to the original EEG data corresponding to the lead; Within the total sampling time, each deviation from the mean corresponding to each lead is filtered to obtain a target frequency band that meets the set frequency condition among each deviation from the mean corresponding to each lead, wherein the total sampling time is the sum of all sampling intervals; Using a dynamic time planning algorithm, a dynamic time planning distance between target frequency bands of any two leads is obtained, and according to each of the dynamic time planning distances, a mean of the dynamic time planning distances corresponding to each of the leads is obtained, and a minimum dynamic time planning distance mean is determined from the means of the dynamic time planning distances; Determine a first ratio of the mean dynamic time planning distance corresponding to the left anterior temporal lead to the mean minimum dynamic time planning distance, and a second ratio of the mean dynamic time planning distance corresponding to the right anterior temporal lead to the mean minimum dynamic time planning distance; When any one of the first ratio or the second ratio is greater than a first preset value, it indicates abnormal discharge in the anterior temporal region.

8. The method for detecting abnormal discharge in the anterior temporal region according to claim 7, characterized in that: Also includes: When either the first ratio or the second ratio is less than a second preset value, it indicates that the anterior temporal region is normal; The second preset value is smaller than the first preset value.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, the function of a detection system for abnormal discharges in the anterior temporal region as claimed in any one of claims 1 to 6 is realized.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the function of the abnormal discharge detection system of the anterior temporal region according to any one of claims 1 to 6.

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