Big Data-Based Nervous System Assisted Diagnosis Method and System

Abstract

The present invention provides a nervous system auxiliary diagnosis method and system based on big data, which receives the diagnosis requirements of the diagnosis end, analyzes the diagnosis requirements, obtains multiple diagnosis time nodes, and obtains the neural waveform graphs of the target person corresponding to the multiple detection time nodes based on the diagnosis time nodes; extracts the pathological waveform parameters corresponding to the corresponding neural waveform graph, and generates the completed waveform graphs of the corresponding diagnosis time nodes according to the waveform completion strategy and the corresponding pathological waveform parameters; retrieves the corresponding completed waveform graph and / or the neural waveform graph as the target waveform graph according to the diagnosis time node, customizes and stores the target waveform graph according to the time series axis in the spatial storage coordinate system, and obtains a waveform storage interface; obtains the target pathological band of the target electrode corresponding to the corresponding target waveform graph in the waveform storage interface, and generates a waveform comparison interface according to the waveform alignment strategy and the target pathological band and sends it to the diagnosis end.

Description

Technical Field

[0001] The present invention relates to data processing technologies, and in particular, to a method and system for assisting in the diagnosis of the nervous system based on big data. Background Art

[0002] The nervous system is the leading system in the body, playing a leading role in maintaining the homeostasis of the internal environment of the body, keeping the integrity and unity of the body, and coordinating the balance of its external environment. Therefore, when there is a nerve abnormality, a nerve diagnosis is required to ensure the normal operation of the body.

[0003] In the prior art, an electroencephalogram signal is usually used to generate an electroencephalogram image for the diagnosis of nervous system abnormalities. Therefore, when medical staff need to compare and analyze electroencephalogram images obtained from multiple examinations of a person, due to the long time, it is difficult to store and easy to lose the data of multiple diagnoses. At the same time, medical staff need to hold multiple electroencephalogram images for repeated viewing and analysis, seriously affecting the viewing efficiency of medical staff.

[0004] Therefore, how to perform customized storage according to the actual electroencephalogram image, facilitate the viewing and comparison of personnel, and improve the viewing efficiency of personnel has become an urgent problem to be solved. Summary of the Invention

[0005] The present invention provides a method and system for assisting in the diagnosis of the nervous system based on big data. The present invention can perform customized storage according to the actual electroencephalogram image, facilitate the viewing and comparison of personnel, and improve the viewing efficiency of personnel.

[0006] In a first aspect of an embodiment of the present invention, a method for assisting in the diagnosis of the nervous system based on big data is provided, including:

[0007] Receiving a diagnosis requirement from a diagnosis terminal, parsing the diagnosis requirement to obtain a plurality of diagnosis time nodes, and obtaining electroencephalogram waveforms of a target person corresponding to a plurality of detection time nodes based on the diagnosis time nodes;

[0008] Extracting pathological waveform parameters corresponding to the electroencephalogram waveforms, and generating a filled waveform diagram corresponding to the diagnosis time node according to a waveform filling strategy and the corresponding pathological waveform parameters;

[0009] Retrieving the corresponding filled waveform diagram and / or the electroencephalogram waveform as a target waveform diagram according to the diagnosis time node, and performing customized storage on the target waveform diagram according to the time series axis in a spatial storage coordinate system to obtain a waveform storage interface;

[0010] Obtaining a target pathological wave band of a target electrode corresponding to the target waveform diagram in the waveform storage interface, and generating a waveform comparison interface according to a waveform alignment strategy and the target pathological wave band, and sending the waveform comparison interface to the diagnosis terminal.

[0011] Optionally, in a possible implementation of the first aspect, receiving the diagnostic requirements of the diagnostic end, parsing the diagnostic requirements to obtain multiple diagnostic time nodes, and obtaining the neurogram of the target person corresponding to multiple detection time nodes based on the diagnostic time nodes includes:

[0012] Receiving the diagnostic requirements of the diagnostic end, parsing the diagnostic requirements to obtain multiple diagnostic time nodes;

[0013] Retrieving the detection repository corresponding to the target person, where the detection repository includes the corresponding relationship between the detection time nodes and the neurograms Figure 1 one-to-one;

[0014] When it is determined that the diagnostic time node is consistent with the corresponding detection time node, taking the corresponding diagnostic time node as the matching time node, and retrieving the neurogram of the corresponding detection time node as the matching waveform diagram based on the matching time node;

[0015] When it is determined that the diagnostic time node is not consistent with the detection time node, taking the corresponding diagnostic time node as the filling time node, and retrieving the neurograms of the 2 detection time nodes adjacent to the filling time node as the reference waveform diagrams.

[0016] Optionally, in a possible implementation of the first aspect, extracting the pathological waveform parameters corresponding to the corresponding neurogram includes:

[0017] Obtaining the detection time nodes of two adjacent reference waveform diagrams, determining the reference waveform diagram of the previous detection time node as the first reference diagram, and taking the reference waveform diagram of the latter detection time node as the second reference diagram;

[0018] Obtaining the standard amplitude intervals corresponding to each electrode, determining the electrodes in the first reference diagram and the second reference diagram as pathological electrodes based on the standard amplitude intervals, and taking the remaining electrodes as normal electrodes;

[0019] Intercepting the first reference band of the corresponding pathological electrode in the first reference diagram according to the standard amplitude interval, and intercepting the second reference band of the corresponding pathological electrode in the second reference diagram based on the standard amplitude interval;

[0020] Determining the waveforms that are not in the corresponding standard amplitude interval in the first reference band and the second reference band as abnormal waveforms;

[0021] Determining the first pathological waveform parameter of the first reference diagram according to the abnormal waveform in the first reference band, and determining the second pathological waveform parameter of the second reference diagram based on the abnormal waveform in the second reference band;

[0022] Based on the first pathological waveform parameter and the second pathological waveform parameter, obtain the pathological waveform parameter corresponding to the reference waveform diagram.

[0023] Optionally, in a possible implementation manner of the first aspect, the determining the first pathological waveform parameter of the first reference diagram according to the abnormal waveform in the first reference band and determining the second pathological waveform parameter of the second reference diagram based on the abnormal waveform in the second reference band includes:

[0024] Obtain the first band duration corresponding to the first reference band and the first quantity of abnormal waveforms, and obtain the first abnormal frequency according to the ratio of the first quantity to the first band duration;

[0025] Statistically analyze the time limit of the abnormal waveforms in the first reference band to obtain the first total time limit, and obtain the first average time limit based on the ratio of the first total time limit to the first quantity;

[0026] Obtain the extreme value of the abnormal waveform in the first reference band as the first abnormal extreme value, determine the absolute value of the first abnormal extreme value to obtain the first abnormal value, statistically analyze the first abnormal value of the abnormal waveforms to obtain the first total value, and obtain the first average value according to the ratio of the first total value to the first quantity;

[0027] According to the first band duration, the first abnormal frequency, the first average time limit, and the first average value, obtain the first pathological waveform parameter of the first reference diagram;

[0028] Obtain the second band duration corresponding to the second reference band and the second quantity of abnormal waveforms, and obtain the second abnormal frequency according to the ratio of the second quantity to the second band duration;

[0029] Statistically analyze the time limit of the abnormal waveforms in the second reference band to obtain the second total time limit, and obtain the second average time limit based on the ratio of the second total time limit to the second quantity;

[0030] Obtain the extreme value of the abnormal waveform in the second reference band as the second abnormal extreme value, determine the absolute value of the second abnormal extreme value to obtain the second abnormal value, statistically analyze the second abnormal value to obtain the second total value, and obtain the second average value according to the ratio of the second total value to the second quantity;

[0031] According to the second band duration, the second abnormal frequency, the second average time limit, and the second average value, obtain the second pathological waveform parameter of the second reference diagram.

[0032] Optionally, in a possible implementation manner of the first aspect, generating a compensated waveform graph corresponding to the diagnosis time node according to the waveform compensation strategy and the corresponding pathological waveform parameters includes:

[0033] Retrieving the pathological waveform parameters corresponding to the corresponding reference waveform graph based on the corresponding compensation time node, where the pathological waveform parameters include first pathological waveform parameters and second pathological waveform parameters;

[0034] Retrieving the first abnormal frequency, the first average time limit, and the first average value in the first pathological waveform parameters, and obtaining the second abnormal frequency, the second average time limit, and the second average value in the second pathological waveform parameters;

[0035] Obtaining the detection time node corresponding to the first pathological waveform parameters as the first time node, and taking the detection time node corresponding to the second pathological waveform parameters as the second time node;

[0036] Obtaining a total time node according to the sum value of the first time node and the second time node, and obtaining a node ratio based on the ratio of the corresponding diagnosis time node to the total time node;

[0037] Obtaining a total abnormal frequency based on the sum value of the first abnormal frequency and the second abnormal frequency, and obtaining a compensated abnormal frequency according to the product of the node ratio and the total abnormal frequency;

[0038] Obtaining a total average time limit according to the sum value of the first average time limit and the second average time limit, and obtaining a compensated average time limit according to the product of the node ratio and the total average time limit;

[0039] Based on the sum value of the first average value and the second average value, obtaining a total average value, and obtaining a compensated average value according to the product of the node ratio and the total average value;

[0040] Obtaining a total band duration according to the sum value of the first band duration and the second band duration, and obtaining a compensated average duration according to the product of the node ratio and the total band duration;

[0041] Generating a compensated pathological band corresponding to the pathological electrode corresponding to the compensation time node according to the compensated abnormal frequency, the compensated average time limit, the compensated average value, and the compensated average duration;

[0042] Retrieving the standard bands corresponding to each electrode, supplementing the compensated pathological band of the pathological electrode corresponding to the compensation time node based on the corresponding standard bands, and taking the corresponding standard bands as the bands of the normal electrodes to obtain the compensated waveform graph corresponding to the corresponding compensation time node.

[0043] Optionally, in a possible implementation of the first aspect, retrieving the corresponding complement waveform diagram and / or the nerve waveform diagram as the target waveform diagram according to the diagnosis time node, and customarily storing the target waveform diagram according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface, including:

[0044] Retrieving the corresponding complement waveform diagram and / or the nerve waveform diagram as the target waveform diagram according to the diagnosis time node, and sorting the diagnosis time nodes in ascending order to obtain a diagnosis time series;

[0045] Retrieving a spatial storage coordinate system, where the spatial storage coordinate system includes a longitudinal time series axis, a transverse first plane axis, and a vertical second plane axis;

[0046] Based on the diagnosis time nodes in the diagnosis time series, successively constructing storage coordinate points on the time series axis of the spatial storage coordinate system, where the storage coordinate points have corresponding diagnosis time nodes;

[0047] Determining a display plane according to the first plane axis and the second plane axis, and successively constructing waveform display slots parallel to the display plane based on the storage coordinate points;

[0048] Displaying the target waveform diagram in the waveform display slots to obtain a waveform storage interface.

[0049] Optionally, in a possible implementation of the first aspect, obtaining the target pathological waveband of the target electrode corresponding to the target waveform diagram in the waveform storage interface, and generating a waveform comparison interface according to a waveform alignment strategy and the target pathological waveband and sending it to the diagnosis end, including:

[0050] Obtaining the origin of the spatial storage coordinate system in the waveform storage interface, using the origin as a comparison coordinate point, and constructing a transparent comparison layer with the same size as the target waveform diagram, and setting the transparent comparison layer in the display plane according to the comparison coordinate point;

[0051] Based on the standard amplitude interval, obtaining the pathological electrodes in the target waveform diagram as target electrodes, and determining the abnormal waveform segments corresponding to the same target electrodes in each target waveform diagram as the target pathological waveband according to the corresponding standard amplitude interval and a preset interval duration;

[0052] Connecting the left endpoint and the right endpoint of the target pathological waveband to generate a connection line segment corresponding to the target pathological waveband;

[0053] Obtain the center point of the connection line segment as the alignment point, slide the wave band corresponding to the target electrode in the target waveform diagram, align the alignment points of the target pathological wave bands corresponding to the same target electrode, and configure corresponding pixel values for the target pathological wave bands corresponding to each target waveform diagram to be displayed in the transparent comparison layer, and generate a waveform comparison interface to be sent to the diagnosis end.

[0054] Optionally, in a possible implementation manner of the first aspect, the obtaining the target pathological wave bands corresponding to the target electrodes of the corresponding target waveform diagrams in the waveform storage interface, and generating a waveform comparison interface to be sent to the diagnosis end according to the waveform alignment strategy and the target pathological wave bands includes:

[0055] Obtain the origin of the spatial storage coordinate system in the waveform storage interface, use the origin as the comparison coordinate point, and construct a transparent comparison layer with the same size as the target waveform diagram, and set the transparent comparison layer in the display plane according to the comparison coordinate point;

[0056] Based on the standard amplitude interval, obtain the pathological electrodes in the target waveform diagram as target electrodes, and determine the abnormal waveform segments corresponding to the same target electrodes in each target waveform diagram as target pathological wave bands according to the corresponding standard amplitude interval and the preset interval duration;

[0057] Obtain the diagnosis time nodes corresponding to each target pathological wave band as analysis time nodes, sort the analysis time nodes in ascending order to obtain an analysis time series;

[0058] Based on the order of the analysis time nodes in the analysis time series, sequentially sort the corresponding target pathological wave bands at the corresponding target electrodes in the transparent comparison layer, and generate a waveform comparison interface to be sent to the diagnosis end.

[0059] Optionally, in a possible implementation manner of the first aspect, it further includes:

[0060] Receive the abnormal waveform segment selected by the diagnosis end in the waveform storage interface as the selected waveform segment, determine the electrode corresponding to the selected waveform segment as the electrode to be analyzed, and the target waveform diagram where the selected waveform segment is located as the selected waveform diagram, and use the remaining target waveform diagrams as comparison waveform diagrams;

[0061] Obtain the waveform extreme values of the electrodes to be analyzed corresponding to each target waveform diagram in the waveform storage interface, and construct a rectangular transparent layer based on the waveform extreme values and the preset length;

[0062] Determine the vertical axis coordinate of the electrode to be analyzed in the second plane axis, and set the rectangular transparent layer at the vertical axis coordinate;

[0063] Retrieve the abnormal waveform segment corresponding to the electrode to be analyzed in the comparison waveform diagram as the comparison waveform segment, and obtain the center point corresponding to the comparison waveform segment as the comparison point. Take the center point corresponding to the selected waveform segment as the selected point;

[0064] Align the comparison point with the selected point, and set corresponding pixel values for each comparison waveform segment to be displayed on the rectangular transparent layer, generating an independent electrode analysis interface and sending it to the diagnosis terminal.

[0065] In a second aspect of the present invention, there is provided a big data-based nervous system assisted diagnosis system, including:

[0066] An acquisition module, configured to receive the diagnosis requirements of the diagnosis terminal, parse the diagnosis requirements to obtain multiple diagnosis time nodes, and acquire the nerve waveform diagrams of the target person corresponding to multiple detection time nodes based on the diagnosis time nodes;

[0067] A generation module, configured to extract the pathological waveform parameters corresponding to the nerve waveform diagrams, and generate a filled waveform diagram corresponding to the diagnosis time nodes according to the waveform filling strategy and the corresponding pathological waveform parameters;

[0068] A storage module, configured to retrieve the corresponding filled waveform diagram and / or the nerve waveform diagram as the target waveform diagram according to the diagnosis time nodes, and perform customized storage on the target waveform diagram according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface;

[0069] A sending module, configured to acquire the target pathological wave band of the target electrode corresponding to the target waveform diagram in the waveform storage interface, and generate a waveform comparison interface according to the waveform alignment strategy and the target pathological wave band and send it to the diagnosis terminal.

[0070] In a third aspect of the present invention, there is provided an electronic device, including: a memory, a processor, and a computer program, where the computer program is stored in the memory, and the processor runs the computer program to execute the method according to the first aspect of the present invention and various possible aspects involved in the first aspect.

[0071] In a fourth aspect of the present invention, there is provided a storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it is used to implement the method according to the first aspect of the present invention and various possible aspects involved in the first aspect.

[0072] The beneficial effects of the present invention are as follows:

[0073] 1. The present invention can perform customized storage according to the actual electroencephalogram images, facilitating the viewing and comparison by personnel and improving the viewing efficiency of personnel. First, the present invention can retrieve the corresponding nerve waveform diagrams according to the detection time nodes, facilitating subsequent waveform analysis, and thus perform customized prediction and filling for the nerve waveform diagrams of the missing detection time nodes for the analysis and viewing by personnel, thereby improving the analysis efficiency of the diagnostic personnel. Moreover, the present invention can perform customized storage of the target waveform diagrams on the time series axis in the spatial storage coordinate system according to the diagnostic time nodes, thereby obtaining a waveform storage interface, so as to facilitate the subsequent storage of a large number of nerve waveform diagrams in chronological order. At the same time, the corresponding nerve waveform diagrams can be quickly viewed through the diagnostic time nodes on the time order axis subsequently, thereby improving the diagnostic analysis efficiency of personnel. Finally, the present invention can align the target pathological wave bands of the target electrodes in the target waveform diagrams of corresponding multiple dates, and thus display them in the same interface, obtaining a waveform comparison interface and sending it to the diagnostic end, facilitating the intuitive viewing by personnel, thereby improving the diagnostic analysis efficiency of personnel.

[0074] 2. The present invention can perform customized storage of the target waveform diagrams in the spatial storage coordinate system, facilitating the quick search for the corresponding nerve waveform diagrams and the intuitive viewing by personnel. Among them, the present invention can construct storage coordinate points on the time series axis in sequence according to the diagnostic time series, and at the same time determine the corresponding display plane and waveform display slots, so as to display the corresponding target waveform diagrams in the corresponding slots, and further facilitate the intuitive viewing by personnel.

[0075] 3. The present invention can align the target pathological wave bands of the target electrodes in the target waveform diagrams of corresponding multiple dates, and thus display them in the same interface, obtaining a waveform comparison interface and sending it to the diagnostic end, facilitating the intuitive viewing by personnel, thereby improving the diagnostic analysis efficiency of personnel. Among them, the present invention can determine the alignment points corresponding to the target pathological wave bands, so as to facilitate the alignment and comparison of multiple target pathological wave bands corresponding to the same target electrode, so as to generate a waveform comparison interface, thus facilitating the intuitive viewing by the diagnostic personnel, reducing the viewing time of repeated comparison, and thus improving the diagnostic analysis efficiency of personnel. Moreover, the present invention can also arrange and display the corresponding target pathological wave bands at the corresponding target electrodes in the transparent comparison layer in sequence according to the analysis time nodes in the analysis time series, obtaining a waveform comparison interface of the corresponding target pathological wave bands, improving the clarity of observation by the diagnostic personnel, and facilitating the diagnostic analysis by personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flowchart of a method for auxiliary diagnosis of the nervous system based on big data provided by the present invention;

[0077] Figure 2 is a schematic diagram of a waveform provided by the present invention;

[0078] Figure 3 Schematic diagram of a waveform storage interface provided by the present invention;

[0079] Figure 4 Schematic diagram of the structure of a big data-based nervous system assisted diagnosis system provided by the present invention;

[0080] Figure 5 Schematic diagram of the hardware structure of an electronic device provided by the present invention. Detailed implementation manners

[0081] The technical solution of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0082] As Figure 1 shown, the present invention provides a flowchart of a big data-based nervous system assisted diagnosis method. The big data-based nervous system assisted diagnosis method includes:

[0083] S1. Receive the diagnosis requirement of the diagnosis terminal, parse the diagnosis requirement to obtain multiple diagnosis time nodes, and obtain the nerve waveform diagrams of the target person corresponding to multiple detection time nodes based on the diagnosis time nodes.

[0084] It should be noted that when medical staff needs to compare and analyze the electroencephalogram images of the diagnosed person to judge the development trend of the person's condition, the waveform images corresponding to the corresponding dates need to be retrieved for analysis. Therefore, the server can parse the received diagnosis requirement information to obtain the corresponding diagnosis time nodes, so as to retrieve the previously examined nerve waveform diagrams for the convenience of medical staff's viewing and analysis.

[0085] It can be understood that the diagnosis terminal is the information terminal of the person performing the analysis and diagnosis. For example, it can be the mobile phone, computer, etc. of the medical staff. The diagnosis requirement is the requirement information for sending the diagnosis. The diagnosis time node is the diagnosis time that needs to be analyzed. For example, when the diagnosis requirement information is to analyze the nerve waveform diagrams of the first day, the second day, and the third day, the corresponding parsed diagnosis time nodes are the first day, the second day, and the third day. The target person is the person undergoing the nerve diagnosis examination, such as Person A, Person B, etc. The detection time node is the time when the target person undergoes the nerve examination. For example, the nerve examination can be performed on the first day and the third day. The nerve waveform diagram is the waveform diagram of multiple nerve electrodes showing the nerves of the person, such as an electroencephalogram.

[0086] It is not difficult to understand that when medical staff conduct a needs analysis, there may be cases where the target person did not undergo an examination during the dates of the nerve waveform diagrams that need to be compared and viewed. For example, when the parsed diagnosis time nodes are the first day, the second day, and the third day, and the corresponding nerve examinations of the target person were only conducted on the first day and the third day, then the nerve waveform diagrams for the corresponding first day and the second day can be retrieved. Thus, based on the nerve waveform diagram information for the existing dates, the nerve waveform diagrams for the missing dates can be predicted and supplemented, facilitating personnel to compare and view, and facilitating diagnostic analysis.

[0087] Through the above-described implementation manner, the present invention can obtain the nerve waveform diagrams of the target person corresponding to the detection time nodes, facilitating subsequent prediction of the missing dates and display for comparison, thereby facilitating personnel to view intuitively and obtaining accurate diagnostic analysis results.

[0088] In some embodiments, the specific implementation manner of step S1 (receiving the diagnostic requirements of the diagnostic end, parsing the diagnostic requirements to obtain multiple diagnostic time nodes, and based on the diagnostic time nodes, obtaining the nerve waveform diagrams of the target person corresponding to multiple detection time nodes) includes:

[0089] S11, receiving the diagnostic requirements of the diagnostic end, and parsing the diagnostic requirements to obtain multiple diagnostic time nodes.

[0090] It can be understood that when the server receives the diagnostic requirement information of the diagnostic end, it can automatically parse the corresponding diagnostic time nodes, facilitating subsequent retrieval of the detected nerve waveform diagrams of the corresponding target person according to the diagnostic time nodes, facilitating personnel to view intuitively, and thereby conducting diagnostic analysis.

[0091] S12, retrieving the detection repository corresponding to the target person, where the detection repository includes a one-to-one correspondence between the detection time nodes and the nerve waveforms Figure 1 relationship.

[0092] It can be understood that the detection repository is a database for storing the data of the nerve detection results, which can be pre-set, including the detection time nodes and the nerve waveform diagrams, and the detection time nodes and the nerve waveform diagrams are in a one-to-one correspondence.

[0093] Through the above-described implementation manner, the present invention can retrieve the detection repository corresponding to the target person, facilitating subsequent acquisition of the nerve waveform diagrams corresponding to the corresponding time nodes.

[0094] S13, when it is determined that the diagnostic time node is consistent with the corresponding detection time node, taking the corresponding diagnostic time node as the matching time node, and based on the matching time node, retrieving the nerve waveform diagram of the corresponding detection time node as the matching waveform diagram.

[0095] It should be noted that there may be a large number of neurographs corresponding to time nodes in the detection repository. Therefore, in order to quickly retrieve the neurograph corresponding to the time required by the diagnosis end, the diagnosis time node can be compared with the detection time node. When they are consistent, the corresponding neurograph can be retrieved so that the personnel can view it in time. At the same time, when comparing the diagnosis time node with the detection time node, it is also possible to determine the neurographs in the detection repository that do not have the diagnosis time node, which is convenient for subsequent targeted prediction.

[0096] It can be understood that the matching time node is the diagnosis time node corresponding to when the diagnosis time node is consistent with the corresponding detection time node. For example, when the diagnosis time nodes are the first day, the second day, and the third day, and the detection time nodes included in the corresponding detection repository are the first day and the third day, the corresponding matching time nodes are the first day and the third day, and the matching neurographs are the neurographs corresponding to the matching time nodes.

[0097] S14. When it is determined that the diagnosis time node and the detection time node are both inconsistent, use the corresponding diagnosis time node as the filling time node, and retrieve the neurographs of the 2 detection time nodes adjacent to the filling time node as the reference neurographs.

[0098] It can be understood that the filling time node is the diagnosis time node corresponding to when the diagnosis time node and the detection time node are both inconsistent. For example, when the diagnosis time nodes are the first day, the second day, and the third day, and the detection time nodes included in the corresponding detection repository are the first day and the third day, then the detection time node of the second day is inconsistent with the detection time node. Therefore, the diagnosis time node of the second day can be used as the filling time node. And in order to predict the neurograph information corresponding to the filling time node, the neurographs of the 2 detection time nodes adjacent to the filling time node can be retrieved as the reference neurographs. For example, when the filling time node is the second day, the neurographs corresponding to the first day and the third day can be retrieved as the reference neurographs.

[0099] Through the above implementation manner, the present invention can determine the filling time node and obtain the reference neurograph, so as to facilitate the subsequent generation of the corresponding filled neurograph, thereby facilitating the comparison and viewing by the personnel.

[0100] S2. Extract the pathological waveform parameters corresponding to the corresponding neurograph, and generate the filled neurograph of the corresponding diagnosis time node according to the waveform filling strategy and the corresponding pathological waveform parameters.

[0101] It should be noted that in order to generate a compensated waveform graph corresponding to the compensated time node, so that the pathological waveform parameters in the reference waveform graph can be extracted, so as to generate a compensated waveform graph of the compensated time node according to the waveform compensation strategy and the corresponding pathological waveform parameters, so as to facilitate subsequent display and facilitate the analysis and viewing by personnel.

[0102] It can be understood that the pathological waveform parameters are related parameters in the nerve waveform graph, such as pathological electrodes, waveform amplitude, fluctuation frequency, etc. The compensated waveform graph is the nerve waveform graph corresponding to the compensated time node.

[0103] Through the above embodiments, the present invention can obtain a compensated waveform graph, so as to facilitate subsequent comparison and analysis by diagnosticians and realize the nerve diagnosis of the target person.

[0104] In some embodiments, the specific implementation manner of step S2 (extracting the pathological waveform parameters corresponding to the nerve waveform graph) includes:

[0105] S201, obtain the detection time nodes of two adjacent reference waveform graphs, determine the reference waveform graph at the previous detection time node as the first reference graph, and the reference waveform graph at the subsequent detection time node as the second reference graph.

[0106] It can be understood that in order to predict the nerve fluctuations corresponding to the compensated time node and obtain the corresponding compensated waveform graph, therefore, the detection time nodes corresponding to the reference waveform graph can be obtained, and the reference waveform graph corresponding to the detection time node with an earlier date is used as the first reference graph, and the reference waveform graph at the subsequent detection time node is used as the second reference graph, so as to facilitate predicting the change trend of the corresponding waveform according to the nerve waveform graphs corresponding to different detection time nodes, so as to obtain an accurate compensated waveform graph.

[0107] For example: when the detection time nodes of the two obtained reference waveform graphs are the first day and the third day respectively, the reference waveform graph corresponding to the first day can be used as the first reference graph, and the reference waveform graph with the detection time node of the third day can be used as the second reference graph.

[0108] S202, obtain the standard amplitude range corresponding to each electrode, determine the electrodes in the first reference graph and the second reference graph as pathological electrodes based on the standard amplitude range, and the remaining electrodes as normal electrodes.

[0109] It should be noted that there can be multiple neuron electrodes in the neural waveform diagram, and there are corresponding normal floating ranges in the waveform diagrams detected by different electrodes. For example, when the standard amplitude range of electrode a is (-1, 1), it can be stated that in the neural waveform diagram, the change amplitude of the waveform of electrode a being between (-1, 1) indicates that the electrode is normal. When the corresponding change amplitude is not within the amplitude range (-1, 1), it can be stated that the corresponding neural electrode may be abnormal. Therefore, display analysis is required.

[0110] It can be understood that the standard amplitude range is the waveform amplitude range when the neural electrode is in a normal condition, and the pathological electrode is the electrode that is not within the standard amplitude range in the first reference diagram and the second reference diagram. For example, among the electrodes in the first reference diagram and the second reference diagram are electrode a, electrode b, electrode c, and electrode d. Among them, there are waveform segments in the waveform of electrode a in the first reference diagram and the second reference diagram that are not within the corresponding standard amplitude range, and there are waveform segments in the waveform of electrode b in the second reference diagram that are not within the corresponding standard amplitude range. Therefore, it can be determined that electrode a and electrode b are pathological electrodes.

[0111] Among them, the normal electrode is the electrode whose amplitude of the electrode waveform is within the standard amplitude range. For example, when among the electrodes in the first reference diagram and the second reference diagram are electrode a, electrode b, electrode c, and electrode d, and electrode a and electrode b are pathological electrodes, then the corresponding normal electrodes are electrode c and electrode d.

[0112] Through the above embodiments, the present invention can determine the pathological electrodes and the normal electrodes, so as to facilitate subsequent data processing of the pathological electrodes, thereby reducing the analysis and processing of the data of the normal electrodes, so as to reduce the data processing volume.

[0113] S203, intercept the first reference band corresponding to the pathological electrode in the first reference diagram based on the standard amplitude range, and intercept the second reference band corresponding to the pathological electrode in the second reference diagram based on the standard amplitude range.

[0114] It can be understood that the first reference band is the waveform segment whose amplitude of the waveform corresponding to the pathological electrode in the first reference diagram is not within the standard amplitude range, and the second reference band is the waveform segment whose amplitude of the waveform corresponding to the pathological electrode in the second reference diagram is not within the standard amplitude range.

[0115] It is not difficult to understand that the interception can start from the moment when the amplitude is not within the standard amplitude range. When the duration of each corresponding waveform is within the preset normal duration, then the band corresponding to the preset duration can be intercepted. When the waveform duration is abnormal, then the waveform with the abnormal waveform duration can be intercepted until the duration corresponding to the waveform is normal, and the interception of the band stops.

[0116] It is worth mentioning that the corresponding first reference band and second reference band can be artificially and actively intercepted to facilitate subsequent analysis.

[0117] S204, determine the waveforms that are not in the corresponding standard amplitude range in the first reference band and the second reference band as abnormal waveforms.

[0118] It should be noted that since the waveforms of the detected neural signals are irregular, there may be multiple waveforms with different amplitudes in the same band. For example, as Figure 2 shown, therefore, all waveforms that are not in the standard amplitude range can be regarded as abnormal waveforms.

[0119] It can be understood that an abnormal waveform is a waveform whose amplitude is not in the standard amplitude range.

[0120] Through the above embodiments, the present invention can determine abnormal waveforms to facilitate subsequent extraction of corresponding pathological waveform parameters and generation of corresponding compensated waveform diagrams.

[0121] S205, determine the first pathological waveform parameter of the first reference diagram based on the abnormal waveforms in the first reference band, and determine the second pathological waveform parameter of the second reference diagram based on the abnormal waveforms in the second reference band.

[0122] It can be understood that through the abnormal waveforms in the first reference band, corresponding pathological waveform parameters can be extracted, such as amplitude, frequency, etc. According to the abnormal waveforms in the second reference band, the second pathological waveform parameters of the second reference diagram can be extracted, such as amplitude, frequency, etc.

[0123] Among them, the first pathological waveform parameter is the waveform parameter of the abnormal waveform in the first reference diagram, and the second pathological waveform parameter is the waveform parameter of the abnormal waveform in the second reference diagram.

[0124] For example, through OpenCV recognition technology, the parameter data in the first reference diagram and the second reference diagram can be extracted.

[0125] Through the above embodiments, the present invention can obtain the corresponding first pathological waveform parameter and second pathological waveform parameter to facilitate subsequent obtaining of the corresponding pathological waveform parameter, thereby predicting the compensated waveform diagram at the corresponding compensated time node.

[0126] In some embodiments, the specific implementation manner in step S205 (determine the first pathological waveform parameter of the first reference diagram based on the abnormal waveforms in the first reference band, and determine the second pathological waveform parameter of the second reference diagram based on the abnormal waveforms in the second reference band) includes:

[0127] S2051. Obtain the first band duration corresponding to the first reference band and the first quantity of abnormal waveforms, and obtain the first abnormal frequency according to the ratio of the first quantity to the first band duration.

[0128] It can be understood that the first band duration is the band time corresponding to the first reference band. For example, it is 1 minute. The first quantity is the quantity of abnormal waveforms in the first reference band. For example, it can be 60, 90, etc. The first abnormal frequency is the frequency corresponding to the abnormal waveforms in the first reference band, that is, the ratio of the first quantity to the first band duration.

[0129] Through the above implementation manner, the present invention can obtain the first abnormal frequency, so as to facilitate subsequent prediction of the parameters corresponding to the waveform diagram of the filling time node, and thus generate the corresponding filled waveform diagram.

[0130] S2052. Statistically analyze the time limit of the abnormal waveforms in the first reference band to obtain the first total time limit, and obtain the first average time limit based on the ratio of the first total time limit to the first quantity.

[0131] It can be understood that the first total time limit is the sum of the time limits of all abnormal waveforms in the first reference band, and the first average time limit is the average time limit of each abnormal waveform in the first reference band, that is, the ratio of the first total time limit to the first quantity.

[0132] S2053. Obtain the extreme value of the abnormal waveforms in the first reference band as the first abnormal extreme value, determine the absolute value of the first abnormal extreme value to obtain the first abnormal value, statistically analyze the first abnormal values of the abnormal waveforms to obtain the first total value, and obtain the first average value according to the ratio of the first total value to the first quantity.

[0133] It can be understood that the first abnormal extreme value is the extreme value of the abnormal waveforms in the first reference band. For example, the amplitude corresponding to the abnormal waveform can be -4, 4, etc. The first abnormal value is the absolute value of the first abnormal extreme value. For example, when the first abnormal extreme value is -4, the corresponding first abnormal value can be obtained as 4. The first total value is the sum of the first abnormal values corresponding to all abnormal waveforms, and the first average value is the average amplitude corresponding to the abnormal waveforms, that is, the ratio of the first total value to the first quantity.

[0134] Through the above implementation manner, the present invention can obtain the relevant parameters of the corresponding abnormal waveforms, which is convenient for subsequent prediction.

[0135] S2054. Obtain the first pathological waveform parameter of the first reference diagram according to the first band duration, the first abnormal frequency, the first average time limit and the first average value.

[0136] It is understandable that the first pathological waveform parameters include the first waveband duration, the first abnormal frequency, the first average time limit, and the first average value.

[0137] Through the above implementation manner, the present invention can obtain the first pathological waveform parameters corresponding to the first reference diagram, so as to facilitate subsequent parameter prediction of the waveforms in the complemented waveform diagram of the complemented time nodes, thereby generating the corresponding complemented waveform diagram for subsequent comparison and viewing by personnel.

[0138] S2055, obtain the second waveband duration corresponding to the second reference waveband and the second quantity of abnormal waveforms, and obtain the second abnormal frequency according to the ratio of the second quantity to the second waveband duration.

[0139] It is understandable that the second waveband duration is the duration corresponding to the second reference waveband, the second quantity is the quantity of abnormal waveforms in the second reference waveband, and the second abnormal frequency is the frequency of the abnormal waveforms in the second reference waveband, that is, the ratio of the second quantity to the second waveband duration.

[0140] S2056, count the time limits of the abnormal waveforms in the second reference waveband to obtain the second total time limit, and obtain the second average time limit based on the ratio of the second total time limit to the second quantity.

[0141] It is understandable that the second total time limit is the sum of the time limits of all abnormal waveforms in the second reference waveband, and the second average time limit is the average time limit of the abnormal waveforms in the second reference waveband, that is, the ratio of the second total time limit to the second quantity.

[0142] S2057, obtain the extreme value of the abnormal waveforms in the second reference waveband as the second abnormal extreme value, determine the absolute value of the second abnormal extreme value to obtain the second abnormal value, count the second abnormal values to obtain the second total value, and obtain the second average value according to the ratio of the second total value to the second quantity.

[0143] It is understandable that the second abnormal extreme value is the extreme value of the amplitude of the abnormal waveforms in the second reference waveband, the second abnormal value is the absolute value of the second abnormal extreme value, the second total value is the total value of the second abnormal values corresponding to all abnormal waveforms in the second reference waveband, and the second average value is the average amplitude corresponding to the abnormal waveforms, that is, the ratio of the second total value to the second quantity.

[0144] S2058, obtain the second pathological waveform parameters of the second reference diagram according to the second waveband duration, the second abnormal frequency, the second average time limit, and the second average value.

[0145] It is understandable that the second pathological waveform parameters include the second waveband duration, the second abnormal frequency, the second average time limit, and the second average value.

[0146] Through the above embodiments, the present invention can obtain the second pathological waveform parameters corresponding to the second reference diagram, so as to facilitate subsequent parameter prediction of the waveforms in the supplemented waveform diagram of the supplemented time node, thereby generating the corresponding supplemented waveform diagram for subsequent comparison and viewing by personnel.

[0147] S206. Obtain the pathological waveform parameters corresponding to the corresponding reference waveform diagram according to the first pathological waveform parameters and the second pathological waveform parameters.

[0148] It can be understood that the pathological waveform parameters are the parameters of the waveforms corresponding to the pathological electrodes in the reference waveform diagram, including the first pathological waveform parameters and the second pathological waveform parameters.

[0149] Through the above embodiments, the present invention can obtain the pathological waveform parameters, so as to facilitate subsequent calculation and prediction of the pathological waveform parameters corresponding to the supplemented time node, thereby generating the corresponding supplemented waveform diagram for viewing and analysis by diagnosticians.

[0150] In some embodiments, the specific implementation manner of step S2 (generating the supplemented waveform diagram of the corresponding diagnosis time node according to the waveform supplementation strategy and the corresponding pathological waveform parameters) includes:

[0151] S207. Retrieve the pathological waveform parameters corresponding to the corresponding reference waveform diagram based on the corresponding supplemented time node, where the pathological waveform parameters include the first pathological waveform parameters and the second pathological waveform parameters.

[0152] It can be understood that according to the supplemented time node, the pathological waveform parameters of the two adjacent reference waveform diagrams are retrieved. For example, when the supplemented time node is the second day, the pathological waveform parameters corresponding to the reference waveform diagram on the first day and the pathological waveform parameters corresponding to the reference waveform diagram on the third day can be retrieved.

[0153] Through the above embodiments, the present invention can retrieve the corresponding pathological waveform parameters, so as to facilitate subsequent generation of the corresponding supplemented waveform diagram.

[0154] S208. Retrieve the first abnormal frequency, the first average time limit, and the first average value in the first pathological waveform parameters, and obtain the second abnormal frequency, the second average time limit, and the second average value in the second pathological waveform parameters.

[0155] It can be understood that, in order to predict the pathological waveform parameters at the filling time node and generate the corresponding filled waveform diagram, therefore, the first abnormal frequency, the first average time limit, and the first average value in the first pathological waveform parameter and the second abnormal frequency, the second average time limit, and the second average value in the second pathological waveform parameter can be retrieved, so as to facilitate the subsequent calculation of the pathological waveform parameters corresponding to the corresponding filling time node.

[0156] S209, obtain the detection time node corresponding to the first pathological waveform parameter as the first time node, and use the detection time node corresponding to the second pathological waveform parameter as the second time node.

[0157] It can be understood that the first time node is the detection time node corresponding to the first pathological waveform parameter. For example, it can be the time node corresponding to the first day, and the second time node is the detection time node corresponding to the second pathological waveform parameter. For example, it can be the time node of the third day.

[0158] Through the above implementation manner, the present invention can obtain the first time node and the second time node, so as to facilitate the subsequent obtaining of the corresponding node ratio, and thus calculate and predict the pathological waveform parameters corresponding to the filling time node.

[0159] S210, obtain the total time node according to the sum value of the first time node and the second time node, and obtain the node ratio based on the ratio of the corresponding diagnosis time node to the total time node.

[0160] It can be understood that the total time node is the sum value of the first time node and the second time node, and the node ratio is the ratio of the corresponding filling time node to the total time node. For example, when the total time node is 4 and the corresponding filling time node is 2, the corresponding node ratio is 1 / 2.

[0161] Through the above implementation manner, the present invention can obtain the node ratio, so as to facilitate the subsequent calculation of the corresponding parameter value, thereby generating the corresponding filled waveform diagram, so as to facilitate the diagnostic personnel to compare and diagnose the nerve waveform diagram of the target person.

[0162] S211, obtain the total abnormal frequency based on the sum value of the first abnormal frequency and the second abnormal frequency, and obtain the filled abnormal frequency according to the product of the node ratio and the total abnormal frequency.

[0163] It can be understood that the total abnormal frequency is the sum value of the first abnormal frequency and the second abnormal frequency, and the filled abnormal frequency is the frequency of the abnormal waveform corresponding to the filling time node, that is, the product of the node ratio and the total abnormal frequency.

[0164] Through the above embodiments, the present invention can obtain the compensated abnormal frequency to facilitate the subsequent generation of the corresponding compensated waveform diagram, thereby facilitating the viewing by personnel for neurodiagnostic analysis.

[0165] S212. Obtain the total average time limit according to the sum value of the first average time limit and the second average time limit, and obtain the compensated average time limit according to the product of the node proportion and the total average time limit.

[0166] It can be understood that the total average time limit is the sum value of the first average time limit and the second average time limit, and the compensated average time limit is the average time limit of the abnormal waveform corresponding to the compensated time node, that is, the product of the node proportion and the total average time limit.

[0167] Through the above embodiments, the present invention can obtain the compensated average time limit to facilitate the subsequent generation of the corresponding compensated waveform diagram, thereby facilitating the viewing by personnel for neurodiagnostic analysis.

[0168] S213. Obtain the total average value according to the sum value of the first average value and the second average value, and obtain the compensated average value according to the product of the node proportion and the total average value.

[0169] It can be understood that the total average value is the sum of the average quantities of the abnormal waveforms, that is, the sum value of the first average value and the second average value, and the compensated average value is the quantity of the abnormal waveforms corresponding to the compensated time node, that is, the product of the node proportion and the total average value.

[0170] Through the above embodiments, the present invention can obtain the compensated average value to facilitate the subsequent generation of the corresponding compensated waveform diagram, thereby facilitating the viewing by personnel for neurodiagnostic analysis.

[0171] S214. Obtain the total band duration according to the sum value of the first band duration and the second band duration, and obtain the compensated average duration according to the product of the node proportion and the total band duration.

[0172] It can be understood that the total band duration is the sum value of the first band duration and the second band duration, and the compensated average duration is the product of the node proportion and the total band duration.

[0173] Through the above embodiments, the present invention can obtain the compensated average duration to facilitate the subsequent generation of the corresponding compensated waveform diagram, thereby facilitating the viewing by personnel for neurodiagnostic analysis.

[0174] S215. Generate the compensated pathological band of the pathological electrode corresponding to the compensated time node according to the compensated abnormal frequency, the compensated average time limit, the compensated average value, and the compensated average duration.

[0175] It can be understood that after determining the replenishment abnormal frequency, replenishment average time limit, replenishment average value, and replenishment average duration, the replenishment pathological waveband corresponding to the pathological electrode at the replenishment time node can be generated. Thus, it is convenient to perform an update based on the replenishment pathological waveband subsequently, so as to obtain the display wave corresponding to the pathological electrode with the complete duration, and further facilitate the viewing and analysis by personnel.

[0176] Among them, the replenishment pathological waveband is the waveband of the pathological electrode corresponding to the replenishment time node.

[0177] S216, retrieve the standard wavebands corresponding to each of the electrodes, supplement the replenishment pathological waveband of the pathological electrode corresponding to the replenishment time node based on the corresponding standard wavebands, and use the corresponding standard wavebands as the wavebands of the normal electrodes to obtain the replenishment waveform diagrams corresponding to the replenishment time nodes.

[0178] It should be noted that the wave shown by each electrode has a certain duration. In the prior art, only the waveform corresponding to the length of the printed paper can be displayed, and the waveform detected for a long time cannot be completely displayed. However, the present invention can completely display the waveforms for all detection durations. Thus, the obtained replenishment pathological waveband can be supplemented, and the waveforms of the time outside the time limit corresponding to the non-replenishment pathological waveband of the corresponding pathological electrode are supplemented by the standard wavebands.

[0179] It can be understood that the standard waveband is the waveband of the electrode during the standard amplitude period. For example, it can be a waveband with a period duration during the standard amplitude period arbitrarily intercepted from the waveform of the corresponding electrode, so as to supplement the replenishment pathological waveband in terms of duration.

[0180] It is not difficult to understand that for the corresponding normal electrodes, their corresponding wavebands are all standard wavebands. In order to enable all electrode information to be normally displayed in the replenishment waveform diagram, the corresponding standard electrodes can be updated at the positions of the corresponding normal electrodes, so as to obtain the replenishment waveform diagram.

[0181] Through the above embodiments, the present invention can obtain the replenishment waveform diagram, which is convenient for subsequent customized storage, for personnel to view and compare and analyze, so as to improve the analysis efficiency of the diagnosticians.

[0182] S3, retrieve the corresponding replenishment waveform diagram and / or the nerve waveform diagram as the target waveform diagram according to the diagnosis time node, and perform customized storage on the target waveform diagram according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface.

[0183] It can be understood that the target waveform diagram is customarily stored on the time series axis in the spatial storage coordinate system according to the diagnostic time nodes in sequence, so as to obtain a waveform storage interface, which is convenient for storing a large number of neural waveform diagrams according to the time sequence subsequently. At the same time, the corresponding neural waveform diagrams can be quickly viewed through the diagnostic time nodes on the time sequence axis subsequently, thereby improving the diagnostic analysis efficiency of personnel.

[0184] Among them, the target waveform diagram is the waveform diagram corresponding to the target person, including the complemented waveform diagram and / or the neural waveform diagram. The spatial storage coordinate system is a three-dimensional coordinate system in the storage space, which can be set by the server according to the storage space. The time series axis is a sequence axis containing time, and the waveform storage interface is an interface for storing waveform diagrams.

[0185] Through the above implementation manner, the present invention can obtain a waveform storage interface, so as to quickly find the corresponding neural waveform diagram through the waveform storage interface subsequently, saving the search time.

[0186] In some embodiments, the specific implementation manner in step S3 (wherein the corresponding complemented waveform diagram and / or the neural waveform diagram are retrieved according to the diagnostic time node as the target waveform diagram, and the target waveform diagram is customarily stored according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface) includes:

[0187] S31, retrieve the corresponding complemented waveform diagram and / or the neural waveform diagram according to the diagnostic time node as the target waveform diagram, and sort the diagnostic time nodes in ascending order to obtain a diagnostic time series.

[0188] It can be understood that the corresponding target waveform diagram is retrieved, and the corresponding diagnostic time nodes are sorted in ascending order, so as to obtain a diagnostic time series, which is convenient for updating the corresponding time axis subsequently, thereby realizing the custom storage of the target waveform diagram.

[0189] Among them, the diagnostic time series is the time series after the diagnostic time nodes are sorted in ascending order. For example, the first day, the second day, the third day.

[0190] S32, retrieve the spatial storage coordinate system, and the spatial storage coordinate system includes a longitudinal time series axis, a horizontal first plane axis, and a vertical second plane axis.

[0191] It can be understood that the spatial storage coordinate system is a three-dimensional spatial coordinate system, so it includes a longitudinal time series axis, a horizontal first plane axis, and a vertical second plane axis. Among them, the time series axis is a sequence axis representing time, the first plane axis is a horizontal coordinate axis of the horizontal plane, and the second plane axis is a coordinate axis of the vertical plane.

[0192] Through the above embodiments, it is convenient to customize the storage of multiple target waveform diagrams according to the spatial storage coordinate system subsequently.

[0193] S33. Based on the diagnostic time nodes in the diagnostic time series, storage coordinate points are sequentially constructed on the time series axis of the spatial storage coordinate system, and each storage coordinate point has a corresponding diagnostic time node.

[0194] It can be understood that the storage coordinate points are the coordinate points on the time series axis, which is convenient for subsequent storage of the target waveform diagram, and each storage coordinate point has a corresponding diagnostic time node.

[0195] For example: as Figure 3 shown, when the diagnostic time nodes are the first day, the second day, and the third day respectively, three storage coordinate points can be sequentially constructed from left to right on the time series axis at a preset interval distance, and they respectively correspond to the first day, the second day, and the third day.

[0196] Through the above embodiments, the present invention can construct storage coordinate points, which is convenient for subsequent customized storage of the target waveform diagram.

[0197] S34. Determine the display plane according to the first plane axis and the second plane axis, and sequentially construct waveform display slots parallel to the display plane based on the storage coordinate points.

[0198] It should be noted that in order to intuitively display the target waveform diagram in the spatial storage coordinate system subsequently, the display plane can be determined according to the first plane axis and the second plane axis, and then the corresponding waveform display slots can be constructed at the storage coordinate points according to the display plane, so as to display the corresponding target waveform diagram in the corresponding slots, which is convenient for intuitive viewing by personnel.

[0199] It can be understood that the display plane is the plane for displaying the waveform diagram, and the waveform display slot is the slot for storing and displaying the waveform diagram. For example, as Figure 3 shown, waveform display slots are sequentially constructed at the storage coordinate points.

[0200] It is worth mentioning that the determined display plane has the same length and width as the target waveform diagram. For example, the width of the display plane determined on the first plane axis is determined by the width of the target waveform diagram, and the length of the display plane determined on the second plane axis is determined by the length of the target waveform diagram.

[0201] S35. Display the target waveform diagram in the waveform display slot to obtain a waveform storage interface.

[0202] It can be understood that the target waveform diagram is filled in the corresponding waveform display slots in sequence according to the detection time nodes, so as to obtain a waveform storage interface, which is convenient for intuitive viewing by personnel, reduces the time for personnel to hold multiple images for repeated viewing, and thus improves the efficiency of diagnostic analysis.

[0203] S4. Obtain the target pathological wave band of the target electrode corresponding to the target waveform diagram in the waveform storage interface, and generate a waveform comparison interface according to the waveform alignment strategy and the target pathological wave band, and send it to the diagnosis end.

[0204] It should be noted that when medical staff conduct diagnostic analysis on the nerve waveform diagrams of a target person, they usually need to place multiple waveform pictures of different dates together for viewing and analysis. Due to the large number of electrodes and the possible different positions of the corresponding abnormal wave bands, it is not easy to observe, which affects the viewing efficiency. However, the present invention can align the target pathological wave bands of the target electrodes in the target waveform diagrams of multiple corresponding dates, so as to display them in the same interface, obtain a waveform comparison interface and send it to the diagnosis end, which is convenient for intuitive viewing by personnel, and thus improves the diagnostic analysis efficiency of personnel.

[0205] It can be understood that the target electrode is the pathological electrode corresponding to the target waveform diagram, the target pathological wave band is the wave band with abnormalities in the corresponding target electrode, and the waveform comparison interface is a comparison interface for displaying multiple pathological wave bands.

[0206] Through the above implementation manner, the present invention can obtain a waveform comparison interface, so as to send it to the diagnosis end, thereby reducing the viewing time for personnel to make comparisons, and further improving the diagnostic analysis efficiency.

[0207] In some embodiments, the specific implementation manner in step S4 (obtaining the target pathological wave band of the target electrode corresponding to the target waveform diagram in the waveform storage interface, generating a waveform comparison interface according to the waveform alignment strategy and the target pathological wave band, and sending it to the diagnosis end) includes:

[0208] S41. Obtain the origin of the spatial storage coordinate system in the waveform storage interface, use the origin as the comparison coordinate point, and construct a transparent comparison layer with the same size as the target waveform diagram, and set the transparent comparison layer in the display plane according to the comparison coordinate point.

[0209] It should be noted that for the convenience of intuitive viewing by personnel, so that personnel can clearly view the corresponding waveform interface for comparison, the origin of the spatial storage coordinate system in the waveform storage interface can be obtained, so as to determine the corresponding display plane according to the origin, and then set the transparent comparison layer in the display plane.

[0210] It can be understood that the comparison coordinate point is the origin of the spatial storage coordinate system in the waveform storage interface, the transparent comparison layer is a transparent layer for displaying the pathological wavebands to be compared, and correspondingly, the size of the transparent comparison layer is the same as that of the target waveform diagram.

[0211] S42. Based on the standard amplitude range, obtain the pathological electrodes in the target waveform diagram as target electrodes, and determine the abnormal waveform segments corresponding to the same target electrodes in each target waveform diagram as target pathological wavebands according to the corresponding standard amplitude range and the preset interval duration.

[0212] It can be understood that the target electrodes are the pathological electrodes in the target waveform diagram, the preset interval duration is the preset interval duration, the abnormal waveform segment is the waveband whose amplitude value of the waveform does not fall within the standard amplitude range, and the target pathological waveband is the abnormal waveform segment corresponding to the same target electrode in the target waveform diagram within the preset interval duration that does not fall within the standard amplitude range.

[0213] For example: when the preset interval duration is 1 s, and there are pathological electrodes in the target waveform diagram, and the target electrodes are electrode a and electrode b, then the abnormal waveform segments corresponding to electrode a and electrode b in the target waveform diagram that do not fall within the standard amplitude range and have a duration corresponding to 10 s are used as the target pathological wavebands.

[0214] S43. Connect the left endpoint and the right endpoint of the target pathological waveband to generate a connection line segment corresponding to the target pathological waveband.

[0215] It should be noted that in order to align and display the target pathological segments of multiple dates of the corresponding electrodes, the left endpoint and the right endpoint of the target pathological waveband are connected to obtain a connection line segment corresponding to the target pathological waveband, so as to facilitate the subsequent determination of the corresponding alignment points for alignment processing.

[0216] Among them, the connection line segment is the line segment connecting the left endpoint and the right endpoint of the target pathological waveband.

[0217] S44. Obtain the center point of the connection line segment as the alignment point, slide the waveband corresponding to the target electrode in the target waveform diagram, align the alignment points of the target pathological wavebands corresponding to the same target electrode, and configure corresponding pixel values for the target pathological wavebands corresponding to each target waveform diagram for display in the transparent comparison layer, and generate a waveform comparison interface to be sent to the diagnosis end.

[0218] It should be noted that compared with the traditional waveform diagram, the present invention can store all waveforms within the corresponding detection time. When a person views, the target waveform diagram currently displayed can be slid left and right to view a large amount of waveform information and perform real-time comparison, so as to more accurately judge the condition.

[0219] It can be understood that the server can automatically slide the band corresponding to the target electrode in the target waveform diagram, so that the alignment points of the same target electrode corresponding to the target pathological band in the target waveform diagrams of multiple different dates are aligned. And in order to distinguish the target pathological bands in different target waveform diagrams, corresponding pixel values are configured for the target pathological bands corresponding to each target waveform diagram. For example, when there are three target waveform diagrams, the first target waveform diagram can be configured with red, the second target waveform diagram can be configured with yellow, and the third target waveform diagram can be configured with green, so as to be displayed in the transparent layer and generate a waveform comparison interface for easy observation by personnel.

[0220] Among them, the alignment point is the center point of the connecting line segment.

[0221] It is not difficult to understand that when the alignment points of multiple different target electrodes in the same target waveform diagram are on the same vertical line, when sliding the target pathological band, the sliding can be synchronized. When the alignment points of multiple different target electrodes in the same target waveform diagram are not on the same vertical line, the target pathological bands can be slid asynchronously and separately so that all the alignment points are at the same central position.

[0222] In some other embodiments, the specific implementation manner of step (obtaining the target pathological band corresponding to the target electrode of the corresponding target waveform diagram in the waveform storage interface, generating a waveform comparison interface according to the waveform alignment strategy and the target pathological band, and sending it to the diagnosis end) includes:

[0223] A1. Obtain the origin of the spatial storage coordinate system in the waveform storage interface, use the origin as the comparison coordinate point, and construct a transparent comparison layer with the same size as the target waveform diagram. Set the transparent comparison layer in the display plane according to the comparison coordinate point.

[0224] It should be noted that for the convenience of direct viewing by personnel, so that personnel can clearly view the waveform interface for corresponding comparison, the origin of the spatial storage coordinate system in the waveform storage interface can be obtained, so as to determine the corresponding display plane according to the origin, and then set the transparent comparison layer in the display plane.

[0225] It can be understood that the comparison coordinate point is the origin of the spatial storage coordinate system in the waveform storage interface, the transparent comparison layer is a transparent layer for displaying the compared pathological band, and moreover, the size of the corresponding transparent comparison layer is the same as that of the target waveform diagram.

[0226] A2. Based on the standard amplitude range, obtain the pathological electrodes in the target waveform diagram as target electrodes, and determine the abnormal waveform segments corresponding to the same target electrodes in each of the target waveform diagrams as target pathological wavebands according to the corresponding standard amplitude range and the preset interval duration.

[0227] It can be understood that the target electrodes are the pathological electrodes in the target waveform diagram, the preset interval duration is the pre-set interval duration, the abnormal waveform segment is the waveband whose amplitude does not fall within the standard amplitude range, and the target pathological waveband is the abnormal waveform segment corresponding to the same target electrode in the target waveform diagram within the preset interval duration that does not fall within the standard amplitude range.

[0228] For example: when the preset interval duration is 1 s, and there are pathological electrodes in the target waveform diagram, and the target electrodes are electrode a and electrode b, then the abnormal waveform segments corresponding to electrode a and electrode b in the target waveform diagram that do not fall within the standard amplitude range and have a duration corresponding to 10 s are used as the target pathological wavebands.

[0229] A3. Obtain the diagnostic time nodes corresponding to each of the target pathological wavebands as analysis time nodes, and sort the analysis time nodes in ascending order to obtain an analysis time series.

[0230] It should be noted that the present invention can also, according to the needs of the diagnostician, only compare and display the corresponding target pathological wavebands, so as to sort according to the analysis time nodes to obtain an analysis time series, which is convenient for subsequent display based on the analysis time series and convenient for personnel to view.

[0231] It can be understood that the analysis time nodes are the diagnostic time nodes corresponding to each target pathological waveband, and the analysis time series is the time series after sorting the analysis time nodes in ascending order. For example, when the analysis time nodes are the first day, the second day, and the third day, then the analysis time series can be obtained as (the first day, the second day, the third day).

[0232] A4. Based on the order of the analysis time nodes in the analysis time series, sequentially sort the corresponding target pathological wavebands at the corresponding target electrodes in the transparent comparison layer, and generate a waveform comparison interface to be sent to the diagnostic end.

[0233] It can be understood that the server can sequentially arrange and display the corresponding target pathological wavebands at the corresponding target electrodes in the transparent comparison layer according to the analysis time nodes in the analysis time series, obtain a waveform comparison interface for the corresponding target pathological wavebands, improve the clarity for the diagnostician to observe, and facilitate the diagnostic analysis by the personnel.

[0234] It should be noted that, according to the manual selection requirements of the diagnosis end, the present invention can also separately display the corresponding abnormal waveform segments, so that the diagnostician can more clearly view the corresponding abnormal information, thereby improving the accuracy of diagnostic analysis. Therefore, it further includes:

[0235] B1, receiving the abnormal waveform segment selected in the waveform storage interface by the diagnosis end as the selected waveform segment, determining the electrode corresponding to the selected waveform segment as the electrode to be analyzed, and the target waveform diagram where the selected waveform segment is located as the selected waveform diagram, and using the remaining target waveform diagrams as comparison waveform diagrams.

[0236] It can be understood that the selected waveform segment is the abnormal waveform segment selected in the waveform storage interface by the diagnosis end, the electrode to be analyzed is the electrode corresponding to the selected waveform segment, for example, it can be electrode a, the selected waveform diagram is the target waveform diagram where the selected waveform segment is located, for example, it can be the target waveform diagram corresponding to the first day, and the comparison waveform diagram is the non-selected waveform diagram. For example, when the target waveform diagram includes the waveform diagrams corresponding to the first day, the second day, and the third day respectively, and the target waveform diagram corresponding to the first day is the selected waveform diagram, then the corresponding comparison waveform diagrams are the waveform diagrams corresponding to the second day and the third day.

[0237] Through the above implementation manner, the present invention can determine the electrode to be analyzed, the selected waveform diagram, and the comparison waveform diagram, so as to facilitate the subsequent generation of an independent electrode analysis interface, thereby facilitating the intuitive viewing by personnel.

[0238] B2, obtaining the waveform extreme values of the electrode to be analyzed corresponding to each target waveform diagram in the waveform storage interface, and constructing a rectangular transparent layer based on the waveform extreme values and a preset length.

[0239] It can be understood that in order to completely display the waveform information corresponding to the electrode to be analyzed, a rectangular transparent layer can be constructed according to the waveform extreme values of the electrode to be analyzed and the preset length, so as to facilitate the subsequent intuitive display of the corresponding waveform segment.

[0240] Among them, the waveform extreme value is the extreme value of the amplitude of the waveform corresponding to the electrode to be analyzed, including the maximum value and the minimum value, the preset length is the length value preset by the person, and the rectangular transparent layer is a transparent layer in the shape of a rectangle.

[0241] B3, determining the vertical axis coordinate of the electrode to be analyzed in the second plane axis, and setting the rectangular transparent layer at the vertical axis coordinate.

[0242] It can be understood that the vertical axis coordinate is the coordinate value of the electrode to be analyzed on the vertical axis, for example, it can be 11, so that the corresponding rectangular transparent layer can be set at the corresponding vertical axis coordinate, so as to facilitate the subsequent display of the corresponding waveform segment.

[0243] B4. Retrieve the abnormal waveform segment corresponding to the electrode to be analyzed in the comparison waveform diagram as the comparison waveform segment, obtain the center point corresponding to the comparison waveform segment as the comparison point, and use the center point corresponding to the selected waveform segment as the selected point.

[0244] It can be understood that the comparison waveform segment is the abnormal waveform segment corresponding to the electrode to be analyzed in the comparison waveform diagram, the comparison point is the center point corresponding to the comparison waveform segment, and the selected point is the center point corresponding to the selected waveform segment.

[0245] Through the above embodiments, the present invention can determine the corresponding comparison waveform segment, comparison point and selected point, so as to facilitate the subsequent comparison and display of the corresponding waveform segments, thereby generating an independent electrode analysis interface for intuitive viewing by personnel.

[0246] B5. Align the comparison point with the selected point, and set corresponding pixel values for each comparison waveform segment for display in the rectangular transparent layer, generate an independent electrode analysis interface and send it to the diagnosis end.

[0247] It can be understood that aligning the comparison point with the selected point, setting different pixel values for different comparison waveform segments and displaying them in the rectangular transparent layer for personnel to distinguish by time, generating an independent electrode analysis interface and sending it to the diagnosis end, so that personnel can intuitively view the changes in the corresponding waveforms and improve the diagnostic analysis efficiency.

[0248] Among them, the independent electrode analysis interface is an interface for separately displaying and analyzing the waveform segments of the selected electrode.

[0249] See Figure 4 , which is a schematic structural diagram of a big data-based nervous system assisted diagnosis system provided by an embodiment of the present invention. The big data-based nervous system assisted diagnosis system includes:

[0250] An acquisition module, configured to receive a diagnosis requirement from a diagnosis end, parse the diagnosis requirement to obtain multiple diagnosis time nodes, and acquire neuro-waveform diagrams of a target person corresponding to multiple detection time nodes based on the diagnosis time nodes.

[0251] A generation module, configured to extract pathological waveform parameters corresponding to the neuro-waveform diagrams, and generate a filled waveform diagram corresponding to the diagnosis time nodes according to a waveform filling strategy and the corresponding pathological waveform parameters.

[0252] A storage module, configured to retrieve the corresponding filled waveform diagram and / or the neuro-waveform diagram as a target waveform diagram according to the diagnosis time node, and perform customized storage on the target waveform diagram according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface.

[0253] A sending module, configured to obtain a target pathological wave band of a target electrode corresponding to the target waveform diagram in the waveform storage interface, and generate a waveform comparison interface according to a waveform alignment strategy and the target pathological wave band, and send the waveform comparison interface to a diagnosis end.

[0254] See Figure 5 , which is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. The electronic device 50 includes: a processor 51, a memory 52, and a computer program; wherein

[0255] The memory 52 is used to store the computer program, and the memory may also be a flash memory. The computer program is, for example, an application program or a functional module that implements the above method.

[0256] The processor 51 is configured to execute the computer program stored in the memory to implement each step performed by the device in the above method. Specifically, reference may be made to the relevant descriptions in the foregoing method embodiments.

[0257] Optionally, the memory 52 may be either independent or integrated with the processor 51.

[0258] When the memory 52 is a device independent of the processor 51, the device may further include:

[0259] A bus 53, configured to connect the memory 52 and the processor 51.

[0260] The present invention further provides a readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it is used to implement the methods provided by the above various embodiments.

[0261] Among them, the readable storage medium may be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium may also be a component of the processor. The processor and the readable storage medium may be located in an application specific integrated circuit (ASIC). In addition, the ASIC may be located in a user device. Of course, the processor and the readable storage medium may also exist as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0262] The present invention also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of the device can read the execution instructions from the readable storage medium, and the execution of the execution instructions by at least one processor enables the device to implement the methods provided by the above various embodiments.

[0263] In the embodiments of the above device, it should be understood that the processor can be a central processing unit (English: Central Processing Unit, abbreviated as: CPU), and can also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated as: DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated as: ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the present invention can be directly embodied as being completed by the execution of a hardware processor, or can be completed by a combination of hardware and software modules in the processor.

[0264] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for assisting diagnosis of the nervous system based on big data, characterized in that: include: Receiving the diagnostic requirements of the diagnostic terminal, parsing the diagnostic requirements to obtain multiple diagnostic time nodes, and obtaining neural waveform diagrams of the target person corresponding to the multiple detection time nodes based on the diagnostic time nodes; Extracting the pathological waveform parameters corresponding to the corresponding neural waveform graph, and generating a completed waveform graph corresponding to the diagnostic time node according to the waveform completion strategy and the corresponding pathological waveform parameters; According to the diagnosis time node, the corresponding completed waveform graph and / or the neural waveform graph is retrieved as a target waveform graph, and the target waveform graph is customized and stored according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface; Obtaining the target pathological band of the target electrode corresponding to the target waveform in the waveform storage interface, and generating a waveform comparison interface according to the waveform alignment strategy and the target pathological band, and sending it to the diagnosis end; The receiving diagnosis requirement of the diagnosis terminal, parsing the diagnosis requirement to obtain a plurality of diagnosis time nodes, and obtaining a neural waveform diagram of a target person corresponding to a plurality of detection time nodes based on the diagnosis time nodes, including: Receiving a diagnostic requirement from a diagnostic terminal, parsing the diagnostic requirement, and obtaining a plurality of diagnostic time nodes; Retrieving a detection repository corresponding to the target person, wherein the detection repository includes a one-to-one correspondence between detection time nodes and neural waveform graphs; When it is determined that the diagnosis time node is consistent with the corresponding detection time node, the corresponding diagnosis time node is used as a matching time node, and based on the matching time node, the neural waveform graph of the corresponding detection time node is retrieved as a matching waveform graph; When it is determined that the diagnosis time node is inconsistent with the detection time node, the corresponding diagnosis time node is used as a completion time node, and the neural waveform graphs of the two detection time nodes adjacent to the completion time node are retrieved as reference waveform graphs; The step of extracting pathological waveform parameters corresponding to the neural waveform diagram includes: Acquire detection time nodes of two adjacent reference waveform graphs, determine the reference waveform graph of the previous detection time node as a first reference graph, and use the reference waveform graph of the next detection time node as a second reference graph; Obtaining a standard amplitude interval corresponding to each electrode, determining electrodes in the first reference reference and the second reference reference as pathological electrodes based on the standard amplitude interval, and determining the remaining electrodes as normal electrodes; According to the standard amplitude interval, a first reference wave band corresponding to the pathological electrode in the first reference image is intercepted, and based on the standard amplitude interval, a second reference wave band corresponding to the pathological electrode in the second reference image is intercepted; Determine the waveforms in the first reference wave band and the second reference wave band that are not in the corresponding standard amplitude interval as abnormal waveforms; Determine a first pathological waveform parameter of a first reference reference based on the abnormal waveform in the first reference band, and determine a second pathological waveform parameter of a second reference reference based on the abnormal waveform in the second reference band; Obtaining a pathological waveform parameter corresponding to the reference waveform graph according to the first pathological waveform parameter and the second pathological waveform parameter; The step of retrieving the corresponding completed waveform graph and / or the neural waveform graph as the target waveform graph according to the diagnosis time node, and customizing and storing the target waveform graph according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface includes: Retrieving the corresponding completed waveform graph and / or the neural waveform graph as the target waveform graph according to the diagnosis time node, and sorting the diagnosis time nodes in ascending order to obtain a diagnosis time series; Retrieving a spatial storage coordinate system, wherein the spatial storage coordinate system includes a longitudinal time series axis, a transverse first plane axis, and a vertical second plane axis; Based on the diagnosis time nodes in the diagnosis time series, storage coordinate points are sequentially constructed on the time series axis of the spatial storage coordinate system, and the storage coordinate points have corresponding diagnosis time nodes; Determine a display plane according to the first plane axis and the second plane axis, and sequentially construct waveform display slots parallel to the display plane based on the stored coordinate points; Displaying the target waveform in the waveform display slot to obtain a waveform storage interface; The step of obtaining the target pathological band of the target electrode corresponding to the target waveform in the waveform storage interface, and generating a waveform comparison interface according to the waveform alignment strategy and the target pathological band and sending it to the diagnosis end includes: Obtaining the origin of the spatial storage coordinate system in the waveform storage interface, taking the origin as a comparison coordinate point, and constructing a transparent comparison layer having the same size as the target waveform image, and setting the transparent comparison layer in the display plane according to the comparison coordinate point; Based on the standard amplitude interval, the pathological electrode in the target waveform is obtained as the target electrode, and the abnormal waveform segment corresponding to the same target electrode in each target waveform is determined as the target pathological band according to the corresponding standard amplitude interval and the preset interval duration; Connecting the left endpoint and the right endpoint of the target pathological band to generate a connecting line segment corresponding to the target pathological band; The center point of the connecting line segment is obtained as the alignment point, and the band corresponding to the target electrode in the target waveform is slid, and the alignment points of the target pathology bands corresponding to the same target electrode are aligned, and corresponding pixel values ​​are configured for the target pathology bands corresponding to each target waveform to display them in the transparent comparison layer, and a waveform comparison interface is generated and sent to the diagnosis end.

2. The method according to claim 1, characterized in that The determining of the first pathological waveform parameter of the first reference reference based on the abnormal waveform in the first reference band, and the determining of the second pathological waveform parameter of the second reference reference based on the abnormal waveform in the second reference band, comprises: Acquire a first band duration corresponding to the first reference band and a first number of abnormal waveforms, and obtain a first abnormal frequency according to a ratio of the first number to the first band duration; Counting the time limits of abnormal waveforms in the first reference wave band to obtain a first total time limit, and obtaining a first average time limit based on a ratio of the first total time limit to the first number; Acquire an extreme value of the abnormal waveform in the first reference band as a first abnormal extreme value, determine an absolute value of the first abnormal extreme value to obtain a first abnormal value, count the first abnormal values ​​of the abnormal waveform to obtain a first total value, and obtain a first average value according to a ratio of the first total value to the first number; Obtaining a first pathological waveform parameter of a first reference reference according to the first waveband duration, the first abnormal frequency, the first average time limit and the first average value; Acquire a second band duration corresponding to the second reference band and a second number of abnormal waveforms, and obtain a second abnormal frequency according to a ratio of the second number to the second band duration; Counting the time limits of abnormal waveforms in the second reference wave band to obtain a second total time limit, and obtaining a second average time limit based on a ratio of the second total time limit to the second number; Acquire an extreme value of the abnormal waveform in the second reference band as a second abnormal extreme value, determine an absolute value of the second abnormal extreme value to obtain a second abnormal value, count the second abnormal values ​​to obtain a second total value, and obtain a second average value according to a ratio of the second total value to the second number; According to the second waveband duration, the second abnormal frequency, the second average time limit and the second average value, the second pathological waveform parameter of the second reference figure is obtained.

3. The method according to claim 2, characterized in that The step of generating a padded waveform diagram corresponding to the diagnosis time node according to the waveform padded strategy and the corresponding pathological waveform parameters includes: Retrieving the pathological waveform parameters corresponding to the reference waveform graph based on the corresponding filling time node, wherein the pathological waveform parameters include a first pathological waveform parameter and a second pathological waveform parameter; Retrieving a first abnormal frequency, a first average time limit, and a first average value in the first pathological waveform parameter, and obtaining a second abnormal frequency, a second average time limit, and a second average value in the second pathological waveform parameter; Acquire the detection time node corresponding to the first pathological waveform parameter as a first time node, and acquire the detection time node corresponding to the second pathological waveform parameter as a second time node; Obtain a total time node according to the sum of the first time node and the second time node, and obtain a node proportion based on a ratio of the corresponding diagnosis time node to the total time node; A total abnormal frequency is obtained based on the sum of the first abnormal frequency and the second abnormal frequency, and a complementary abnormal frequency is obtained according to the product of the node proportion and the total abnormal frequency; Obtain a total average time limit according to the sum of the first average time limit and the second average time limit, and obtain a filling average time limit according to the product of the node proportion and the total average time limit; A total average value is obtained based on the sum of the first average value and the second average value, and a padding average value is obtained according to the product of the node proportion and the total average value; Obtain a total band duration according to the sum of the first band duration and the second band duration, and obtain a padded average duration according to the product of the node proportion and the total band duration; Generate a pathological band for the pathological electrode corresponding to the time node of the supplement according to the supplement abnormal frequency, the average time limit of the supplement, the average value of the supplement and the average time of the supplement; The standard band corresponding to each electrode is retrieved, and the supplemented pathological band of the pathological electrode corresponding to the supplemented time node is supplemented based on the corresponding standard band, and the corresponding standard band is used as the band of the normal electrode to obtain the supplemented waveform diagram of the corresponding supplemented time node.

4. The method according to claim 1, characterized in that: The step of obtaining the target pathological band of the target electrode corresponding to the target waveform in the waveform storage interface, and generating a waveform comparison interface according to the waveform alignment strategy and the target pathological band and sending it to the diagnosis end includes: Obtaining the origin of the spatial storage coordinate system in the waveform storage interface, taking the origin as a comparison coordinate point, and constructing a transparent comparison layer having the same size as the target waveform image, and setting the transparent comparison layer in the display plane according to the comparison coordinate point; Based on the standard amplitude interval, the pathological electrode in the target waveform is obtained as the target electrode, and the abnormal waveform segment corresponding to the same target electrode in each target waveform is determined as the target pathological band according to the corresponding standard amplitude interval and the preset interval duration; Acquire the diagnosis time nodes corresponding to the target pathological bands as analysis time nodes, sort the analysis time nodes in ascending order, and obtain an analysis time series; Based on the order of the analysis time nodes in the analysis time sequence, the corresponding target pathological bands are sequentially sorted at the corresponding target electrodes in the transparent comparison layer, and a waveform comparison interface is generated and sent to the diagnosis end.

5. The method according to claim 1, characterized in that Also includes: The receiving diagnosis end selects the abnormal waveform segment in the waveform storage interface as the selected waveform segment, determines the electrode corresponding to the selected waveform segment as the electrode to be analyzed, and the target waveform graph where the selected waveform segment is located as the selected waveform graph, and uses the remaining target waveform graphs as comparison waveform graphs; Obtaining the waveform extreme value of the electrode to be analyzed corresponding to each target waveform graph in the waveform storage interface, and constructing a rectangular transparent layer based on the waveform extreme value and a preset length; Determine the vertical axis coordinate of the electrode to be analyzed in the second plane axis, and set the rectangular transparent layer at the vertical axis coordinate; Retrieving the abnormal waveform segment corresponding to the electrode to be analyzed in the comparison waveform diagram as the comparison waveform segment, obtaining the center point corresponding to the comparison waveform segment as the comparison point, and taking the center point corresponding to the selected waveform segment as the selected point; The comparison point is aligned with the selected point, and corresponding pixel values ​​are set for each comparison waveform segment to be displayed on the rectangular transparent layer, and an independent electrode analysis interface is generated and sent to the diagnosis end.

6. A big data-based nervous system auxiliary diagnosis system, characterized in that: include: An acquisition module, used for receiving the diagnosis requirements of the diagnosis end, parsing the diagnosis requirements, obtaining multiple diagnosis time nodes, and obtaining the neural waveform diagrams of the target person corresponding to the multiple detection time nodes based on the diagnosis time nodes; A generation module, used to extract the pathological waveform parameters corresponding to the corresponding neural waveform graph, and generate a completed waveform graph corresponding to the diagnostic time node according to the waveform completion strategy and the corresponding pathological waveform parameters; A storage module, used for retrieving the corresponding completed waveform graph and / or the neural waveform graph as a target waveform graph according to the diagnosis time node, customizing and storing the target waveform graph according to the time series axis in the spatial storage coordinate system, and obtaining a waveform storage interface; A sending module, used for obtaining the target pathological band of the target electrode corresponding to the target waveform in the waveform storage interface, generating a waveform comparison interface according to the waveform alignment strategy and the target pathological band and sending it to the diagnosis end; The receiving diagnosis requirement of the diagnosis terminal, parsing the diagnosis requirement to obtain a plurality of diagnosis time nodes, and obtaining a neural waveform diagram of a target person corresponding to a plurality of detection time nodes based on the diagnosis time nodes, including: Receiving a diagnostic requirement from a diagnostic terminal, parsing the diagnostic requirement, and obtaining a plurality of diagnostic time nodes; Retrieving a detection repository corresponding to the target person, wherein the detection repository includes a one-to-one correspondence between detection time nodes and neural waveform graphs; When it is determined that the diagnosis time node is consistent with the corresponding detection time node, the corresponding diagnosis time node is used as a matching time node, and based on the matching time node, the neural waveform graph of the corresponding detection time node is retrieved as a matching waveform graph; When it is determined that the diagnosis time node is inconsistent with the detection time node, the corresponding diagnosis time node is used as a completion time node, and the neural waveform graphs of the two detection time nodes adjacent to the completion time node are retrieved as reference waveform graphs; The step of extracting pathological waveform parameters corresponding to the neural waveform diagram includes: Acquire detection time nodes of two adjacent reference waveform graphs, determine the reference waveform graph of the previous detection time node as a first reference graph, and use the reference waveform graph of the next detection time node as a second reference graph; Obtaining a standard amplitude interval corresponding to each electrode, determining electrodes in the first reference reference and the second reference reference as pathological electrodes based on the standard amplitude interval, and determining the remaining electrodes as normal electrodes; According to the standard amplitude interval, a first reference wave band corresponding to the pathological electrode in the first reference image is intercepted, and based on the standard amplitude interval, a second reference wave band corresponding to the pathological electrode in the second reference image is intercepted; Determine the waveforms in the first reference wave band and the second reference wave band that are not in the corresponding standard amplitude interval as abnormal waveforms; Determine a first pathological waveform parameter of a first reference reference based on the abnormal waveform in the first reference band, and determine a second pathological waveform parameter of a second reference reference based on the abnormal waveform in the second reference band; Obtaining a pathological waveform parameter corresponding to the reference waveform graph according to the first pathological waveform parameter and the second pathological waveform parameter; The step of retrieving the corresponding completed waveform graph and / or the neural waveform graph as the target waveform graph according to the diagnosis time node, and customizing and storing the target waveform graph according to the time series axis in the spatial storage coordinate system to obtain a waveform storage interface includes: Retrieving the corresponding completed waveform graph and / or the neural waveform graph as the target waveform graph according to the diagnosis time node, and sorting the diagnosis time nodes in ascending order to obtain a diagnosis time series; Retrieving a spatial storage coordinate system, wherein the spatial storage coordinate system includes a longitudinal time series axis, a transverse first plane axis, and a vertical second plane axis; Based on the diagnosis time nodes in the diagnosis time series, storage coordinate points are sequentially constructed on the time series axis of the spatial storage coordinate system, and the storage coordinate points have corresponding diagnosis time nodes; Determine a display plane according to the first plane axis and the second plane axis, and sequentially construct waveform display slots parallel to the display plane based on the stored coordinate points; Displaying the target waveform in the waveform display slot to obtain a waveform storage interface; The step of obtaining the target pathological band of the target electrode corresponding to the target waveform in the waveform storage interface, and generating a waveform comparison interface according to the waveform alignment strategy and the target pathological band and sending it to the diagnosis end includes: Obtaining the origin of the spatial storage coordinate system in the waveform storage interface, taking the origin as a comparison coordinate point, and constructing a transparent comparison layer having the same size as the target waveform image, and setting the transparent comparison layer in the display plane according to the comparison coordinate point; Based on the standard amplitude interval, the pathological electrode in the target waveform is obtained as the target electrode, and the abnormal waveform segment corresponding to the same target electrode in each target waveform is determined as the target pathological band according to the corresponding standard amplitude interval and the preset interval duration; Connecting the left endpoint and the right endpoint of the target pathological band to generate a connecting line segment corresponding to the target pathological band; The center point of the connecting line segment is obtained as the alignment point, and the band corresponding to the target electrode in the target waveform is slid, and the alignment points of the target pathology bands corresponding to the same target electrode are aligned, and corresponding pixel values ​​are configured for the target pathology bands corresponding to each target waveform to display them in the transparent comparison layer, and a waveform comparison interface is generated and sent to the diagnosis end.

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