Neurological disease auxiliary analysis method, device and equipment

Abstract

The invention is suitable for the technical field of neurology, and particularly relates to a neurology disease auxiliary analysis method, device and equipment, and the method comprises the steps: obtaining the feature parameters of the neural data of a user in each physiological region in a preset physiological region, and an abnormal data index corresponding to each physiological region; obtaining an abnormal risk index corresponding to each physiological region according to the feature parameters of the neural data of the user in each physiological region; and according to each abnormal risk index and the abnormal data index corresponding to each abnormal risk index, obtaining a neural anomaly risk level. The neurological disease auxiliary analysis method provided by the invention can solve the problem that in the traditional neurological disease auxiliary analysis process, integral indexes are often depended, fusion analysis is not carried out on indexes of each physiological region of the brain of the user, and the accuracy of final auxiliary analysis is influenced.

Description

Technical Field

[0001] The present application belongs to the technical field of neurology, and in particular, relates to a method, device and equipment for auxiliary analysis of neurological diseases. Background Art

[0002] Neurological disorders refer to diseases involving the central nervous system (brain and spinal cord) and the peripheral nervous system. Neurological disorders can affect the user's neurological function, causing varying degrees of motor, sensory, cognitive and behavioral impairments.

[0003] The traditional auxiliary analysis process of neurological diseases often relies on overall indicators (such as the average activity value of the whole brain), and fails to integrate and analyze the indicators of each physiological area of ​​the user's brain, which affects the accuracy of the final auxiliary analysis. Summary of the invention

[0004] The embodiments of the present application provide a method, device and equipment for auxiliary analysis of neurological diseases, which can solve the problem that in the traditional auxiliary analysis process of neurological diseases, the indicators of each physiological area of ​​the user's brain are often relied on for analysis, which affects the accuracy of the final auxiliary analysis.

[0005] In a first aspect, the present application provides an auxiliary analysis method for neurological diseases, comprising: Acquire characteristic parameters of each physiological region of the user's neural data in the preset physiological regions, and abnormal data indicators corresponding to each physiological region; wherein, based on the user's brain model, each physiological region is divided into the same region; the abnormal data indicator corresponding to one physiological region is used to reflect the possibility that each abnormal neural data associated with neurology accumulates and occurs in one physiological region; According to the characteristic parameters of the user's neural data in each physiological area, an abnormal risk index corresponding to each physiological area is obtained; wherein the abnormal risk index corresponding to one physiological area is used to reflect the risk level of each abnormal neural data in one physiological area; According to each of the abnormal risk indexes and the abnormal data indicators corresponding to each of the abnormal risk indexes, a neurological abnormality risk level is obtained; wherein, the abnormal data indicator corresponding to one of the abnormal risk indexes is used to reflect the abnormal data indicators accumulated in a designated physiological region corresponding to one of the abnormal risk indexes, and the designated physiological region is a physiological region where the abnormal risk index exceeds a preset index.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The auxiliary analysis method for neurological diseases provided by the present application can divide the brain into multiple physiological regions by obtaining the characteristic parameters of each physiological region of the user's neural data in the preset physiological region and the abnormal data indicators corresponding to each physiological region, thereby avoiding the limitation of over-reliance on holistic indicators in traditional methods. At the same time, the abnormal data indicators of each physiological region are used as part of the auxiliary analysis of neurology, which can support the analysis of the state of the brain or nervous system from multiple dimensions, further avoiding the limitation of over-reliance on holistic indicators in traditional methods, and helping to improve the accuracy of the final auxiliary analysis. According to the characteristic parameters of the user's neural data in each physiological region, the abnormal risk index corresponding to each physiological region is obtained, and the risk level corresponding to each region can be accurately understood, rather than relying on a unified average value, thereby helping to improve the accuracy of the final auxiliary analysis. According to each abnormal risk index and the abnormal data indicators corresponding to each abnormal risk index, the abnormal risk level of the nerves can be obtained, and the abnormal risk index and the abnormal data indicators can be combined to comprehensively analyze the nervous system from multiple dimensions, which helps to comprehensively analyze the nerve risk, thereby improving the accuracy of the final auxiliary analysis.

[0007] In a possible implementation of the first aspect, obtaining characteristic parameters of each physiological region of the user's neural data within a preset physiological region includes: Acquire neural data of the user; wherein the neural data is data generated by unprocessed brain neuron activity; Extracting characteristic data of the neural data in a preset physiological area according to the neural data; wherein the characteristic data is used to reflect the concentration degree of characteristic values ​​in each value range; The characteristic parameter corresponding to the physiological region is determined according to the characteristic data of the neural data on the preset physiological region.

[0008] In a possible implementation manner of the first aspect, obtaining an abnormal data indicator corresponding to each physiological region includes: Determine the association time corresponding to each target area according to the neural data; wherein each target area is an area consisting of the physiological area corresponding to the abnormal neural data with consistent frequency; the association time corresponding to the target area is the time when each abnormal neural data occurs in the target area within a scanning window of the neurological disease auxiliary analysis device; Obtaining the abnormal data indicator corresponding to each target area according to the associated time and the scanning window corresponding to each target area; According to the abnormal data indicator corresponding to each target area and the area corresponding to the physiological area on the user's brain model, the abnormal data indicator corresponding to each physiological area is obtained.

[0009] In a possible implementation manner of the first aspect, determining the characteristic parameter corresponding to the physiological region according to the characteristic data of the neural data on the preset physiological region includes: The characteristic values ​​corresponding to the characteristic data are averaged with the values ​​having the largest concentration in each value range to obtain the characteristic parameter corresponding to the physiological area.

[0010] In a possible implementation manner of the first aspect, the method further includes: Identifying the preset physiological region according to a stored database; or, The user's head movement artifacts are obtained, and the effective data coverage area after the artifacts are eliminated is determined as the preset physiological area; wherein the effective data coverage is used to reflect the ratio of the effective data duration to the preset observation duration.

[0011] In a possible implementation manner of the first aspect, the method further includes: When the coverage rate corresponding to the valid data coverage area after the artifacts are eliminated does not exceed the first threshold, obtaining the coverage rates corresponding to other valid data coverage areas after the artifacts are eliminated; When the coverage rates corresponding to the valid data coverage areas after other artifacts are eliminated do not exceed the first threshold, obtaining the number of neural data of the user detected in two adjacent valid data coverage areas; When the number of neural data of the user detected in multiple groups of two adjacent valid data coverage areas exceeds a preset number, and the overlap rate between multiple groups of two adjacent valid data coverage areas and the user's brain functional areas exceeds a preset threshold, the multiple groups of two adjacent valid data coverage areas are merged into multiple groups of target physiological areas.

[0012] In a possible implementation manner of the first aspect, after merging multiple groups of two adjacent valid data coverage areas into multiple groups of target physiological areas, the method further includes: Determine whether there is a target physiological region that meets a preset condition in the multiple groups of target physiological regions; wherein the preset condition is that there is at least one complete nerve conduction pathway in the target physiological region and the delay corresponding to the nerve data meets a preset delay condition; In the case that it is determined that there is a target physiological region satisfying the preset condition among the multiple groups of target physiological regions, the target physiological region satisfying the preset condition is determined as the preset physiological region.

[0013] In a possible implementation manner of the first aspect, the method further includes: When the coverage rate corresponding to the valid data coverage area after the artifacts are eliminated exceeds a first threshold, the valid data coverage area with the coverage rate exceeding the first threshold is determined as the preset physiological area.

[0014] In a second aspect, an embodiment of the present application provides a neurological disease auxiliary analysis device, which is used to implement the neurological disease auxiliary analysis method described in any one of the first aspects above, and the neurological disease auxiliary analysis device is applied to a neurological disease auxiliary analysis device, and the neurological disease auxiliary analysis device includes: An acquisition unit, used to acquire characteristic parameters of each physiological region of the user's neural data in a preset physiological region, and an abnormal data index corresponding to each physiological region; wherein, based on the user's brain model, each physiological region is divided into the same region; and the abnormal data index corresponding to one physiological region is used to reflect the possibility that each abnormal neural data associated with neurology accumulates and occurs in one physiological region; A calculation unit, used for obtaining an abnormal risk index corresponding to each physiological region according to the characteristic parameters of the user's neural data in each physiological region; wherein the abnormal risk index corresponding to one physiological region is used to reflect the risk level of each abnormal neural data in one physiological region; An analysis unit is used to obtain a neurological abnormality risk level according to each of the abnormal risk indexes and the abnormal data indicators corresponding to each of the abnormal risk indexes; wherein the abnormal data indicator corresponding to one of the abnormal risk indexes is used to reflect the abnormal data indicators accumulated in a designated physiological region corresponding to one of the abnormal risk indexes, and the designated physiological region is a physiological region where the abnormal risk index exceeds a preset index.

[0015] In a third aspect, an embodiment of the present application provides an auxiliary analysis device for neurological diseases, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the auxiliary analysis method for neurological diseases described in any one of the first aspects above is implemented.

[0016] It can be understood that the beneficial effects of the second to third aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flowchart of a method for assisting analysis of neurological diseases provided in one embodiment of the present application; Figure 2 This is a schematic diagram of an implementation flow of determining the risk level of neurological abnormality in a neurological disease auxiliary analysis method provided in an embodiment of the present application; Figure 3 This is a schematic diagram of the implementation process of determining a preset physiological region in a neurological disease auxiliary analysis method provided in an embodiment of the present application; Figure 4 is a schematic diagram of the structure of a neurological disease auxiliary analysis device provided in an embodiment of the present application; Figure 5 It is a structural schematic diagram of the control device of the neurological disease auxiliary analysis equipment provided in the embodiment of the present application. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0020] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0021] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0023] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0025] In the related art, neurological diseases refer to diseases involving the central nervous system (brain and spinal cord) and the peripheral nervous system. Neurological diseases can affect the user's neurological function, leading to varying degrees of motor, sensory, cognitive and behavioral disorders.

[0026] The traditional auxiliary analysis process of neurological diseases often relies on overall indicators (such as the average activity value of the whole brain), and fails to integrate and analyze the indicators of each physiological area of ​​the user's brain, which affects the accuracy of the final auxiliary analysis.

[0027] To solve the above problems, the embodiments of the present application provide a method, device and equipment for auxiliary analysis of neurological diseases.

[0028] In this method, by obtaining the characteristic parameters of each physiological region of the user's neural data in the preset physiological region and the abnormal data indicators corresponding to each physiological region, the brain can be divided into multiple physiological regions, avoiding the limitation of over-reliance on overall indicators in traditional methods. At the same time, the abnormal data indicators of each physiological region are used as part of the auxiliary analysis of neurology, which can support the analysis of the state of the brain or nervous system from multiple dimensions, further avoiding the limitation of over-reliance on overall indicators in traditional methods, and helping to improve the accuracy of the final auxiliary analysis. According to the characteristic parameters of the user's neural data in each physiological region, the abnormal risk index corresponding to each physiological region is obtained, and the risk level corresponding to each region can be accurately understood instead of relying on a unified average value, thereby helping to improve the accuracy of the final auxiliary analysis. According to each abnormal risk index and the abnormal data indicators corresponding to each abnormal risk index, the abnormal risk level of the nerves can be obtained, and the abnormal risk index and the abnormal data indicators can be combined to comprehensively analyze the nervous system from multiple dimensions, which helps to comprehensively analyze the nerve risk, thereby improving the accuracy of the final auxiliary analysis.

[0029] The auxiliary analysis method for neurological diseases provided in the embodiment of the present application can be applied to an auxiliary analysis device for neurological diseases. In this case, the auxiliary analysis device for neurological diseases is the executor of the auxiliary analysis method for neurological diseases provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the auxiliary analysis device for neurological diseases.

[0030] For example, the neurological disease auxiliary analysis equipment may include a neurological disease auxiliary analysis device and a control device electrically connected to the neurological disease auxiliary analysis device. The neurological disease auxiliary analysis device may be an electroencephalogram device (used to obtain real-time neural data of the user's brain to monitor and record brain electrical activity), a functional magnetic resonance imaging device (used to monitor the activities of different brain regions in real time to identify abnormal activities in different brain regions), etc. The control device may read the data obtained by the neurological disease auxiliary analysis device, and analyze the neural data obtained by the neurological disease auxiliary analysis device to obtain the risk level of neural abnormality.

[0031] For example, the control device can be a single chip microcomputer, a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a computing device or a computer connected to a wireless modem, a laptop computer, a handheld communication device, a handheld computing device, etc.

[0032] In order to better understand the auxiliary analysis method for neurological diseases provided in the embodiment of the present application, the specific implementation process of the auxiliary analysis method for neurological diseases provided in the embodiment of the present application is exemplarily introduced below.

[0033] Figure 1 A schematic flow chart of the auxiliary analysis method for neurological diseases provided in an embodiment of the present application is shown. The auxiliary analysis method for neurological diseases includes: S100, obtaining characteristic parameters of each physiological region of the user's neural data in the preset physiological region, and abnormal data indicators corresponding to each physiological region. Wherein, each physiological region is divided into the same region based on the user's brain model. The abnormal data indicator corresponding to a physiological region is used to reflect the possibility that each abnormal neural data associated with neurology accumulates in a physiological region.

[0034] It can be understood that neural data can be data reflecting the activity of brain neurons, for example, electrical activity data collected by the electrodes of an electroencephalogram device on the scalp. The preset physiological area can be a pre-set area for auxiliary analysis. For example, the area can be calibrated using an anatomical atlas of the brain. After calibrating the preset physiological area, the preset physiological area can be divided into multiple physiological areas of the same size based on the user's brain model. Abnormal neural data can be electrical activity data that exceeds a preset threshold. Optionally, the characteristic parameters of each physiological area can be obtained based on the ratio of neural data to standard reference data (for example, mean) in the same physiological area.

[0035] For example, the possibility of abnormal neural data occurring in a physiological area can be obtained based on the ratio of the number of scans of the auxiliary analysis device for neurological diseases in a scanning window to the number of times abnormal neural data occurs in a physiological area. For example, the number of scans in a scanning window is 10 times, and the number of times abnormal neural data occurring in a physiological area is scanned is 5 times, then the possibility of abnormal neural data occurring in a physiological area is 0.5. It can also be to count the time when each abnormal neural data occurs in the corresponding target area. For example, in a 30-minute scanning window, the abnormal neural data at the high-frequency level in a certain brain area lasts for a total of 10 minutes, then the corresponding association time of the target area is 10 minutes, then the possibility of abnormal neural data occurring in a physiological area is 0.3.

[0036] Optionally, abnormal neural data may include a data, b data, c data, etc. The probability that a data occurs in the target physiological area is 0.2, the probability that b data occurs in the target physiological area is 0.25, and the probability that c data occurs in the target physiological area is 0.25. Then the abnormal data index corresponding to the target physiological area can be 0.2+0.25+0.25=0.7, and so on, to calculate the abnormal data index corresponding to other physiological areas. It should be noted that the abnormal data index corresponding to the target physiological area is used to reflect the abnormal degree of neural activity and the abnormal frequency of the target physiological area.

[0037] With such a setting, by obtaining the characteristic parameters of the user's neural data in each preset physiological area, and the abnormal data indicators corresponding to each physiological area, the brain can be divided into multiple physiological areas, avoiding the limitations of traditional methods that rely too much on holistic indicators. At the same time, using the abnormal data indicators of each physiological area as part of the auxiliary analysis of neurology can support the analysis of the state of the brain or nervous system from multiple dimensions, further avoiding the limitations of traditional methods that rely too much on holistic indicators, and helping to improve the accuracy of the final auxiliary analysis.

[0038] In a possible implementation, S100, obtaining characteristic parameters of each physiological region of the user's neural data in a preset physiological region includes: S110, obtaining the user's neural data, wherein the neural data is the unprocessed data generated by the brain's neuron activities.

[0039] It is understood that neural data can be action potentials and synaptic potentials of neurons. The electrical activity of the brain can be recorded by an electroencephalogram device.

[0040] Such a setting can provide data support for subsequent analysis.

[0041] S120, extracting characteristic data of the neural data in a preset physiological region according to the neural data, wherein the characteristic data is used to reflect the concentration degree of the characteristic value in each value range.

[0042] It can be understood that within the preset physiological area, the neural data of each physiological area are extracted, and characteristic values ​​are calculated, such as the mean (used to reflect the overall level of neural activity in the physiological area) and the standard deviation (used to measure the degree of deviation of the data from the mean). The standard deviation is compared with the mean to obtain the characteristic data of the neural data in each physiological area within the preset physiological area, and store it in the database. If the ratio of the standard deviation to the mean is large, it means that the standard deviation fluctuates less relative to the mean, the data is more concentrated, and the distribution is more uniform, indicating that the stability of the physiological area is higher. On the contrary, it means that the stability of the physiological area is lower. Mean u= , standard deviation = , where xi is each data point and n is the total number of data points.

[0043] This setting allows for comprehensive analysis of multiple data rather than relying on a unified average value, which helps improve the accuracy of the final auxiliary analysis.

[0044] S130, determining characteristic parameters corresponding to the physiological region according to characteristic data of the neural data on the preset physiological region.

[0045] It can be understood that the characteristic parameters corresponding to the physiological region can be obtained by matching the target neural data in the database. The database includes neural data in multiple physiological regions and characteristic data corresponding to multiple physiological regions. That is, the characteristic data corresponding to each physiological region in the preset physiological region is the characteristic parameter corresponding to each physiological region.

[0046] Such a setting can provide data support for subsequent comprehensive analysis of multiple data, thereby helping to improve the accuracy of the final auxiliary analysis.

[0047] In a possible implementation, S130, determining characteristic parameters corresponding to the physiological region according to characteristic data of the neural data on the preset physiological region, includes: The characteristic values ​​corresponding to the characteristic data are averaged with the highest concentration in each value range to obtain the characteristic parameters corresponding to the physiological area.

[0048] It can be understood that after comparing the standard deviation with the mean and obtaining the characteristic data, the characteristic values ​​corresponding to the characteristic data can be averaged in each value range with the largest concentration to obtain the characteristic parameters corresponding to the physiological area. For example, assuming that the value range of a certain characteristic data (such as nerve activity intensity) is 0 to 1, and is divided into 5 value ranges: value range 1 is 0-0.2, value range 2 is 0.2-0.4, value range 3 is 0.4-0.6, value range 4 is 0.6-0.8, and value range 5 is 0.8-1.0. The maximum concentration of value range 1 is 0.18, the maximum concentration of value range 2 is 0.35, the maximum concentration of value range 3 is 0.55, the maximum concentration of value range 4 is 0.72, and the maximum concentration of value range 5 is 0.92. Then the characteristic parameter is (0.18+0.35+0.55+0.72+0.92)÷5=0.544.

[0049] Such a setting can provide data support for subsequent comprehensive analysis of multiple data, thereby helping to improve the accuracy of the final auxiliary analysis.

[0050] In a possible implementation, S100, obtaining an abnormal data indicator corresponding to each physiological region includes: S140, determining the association time corresponding to each target area according to the neural data. Each target area is an area consisting of physiological areas corresponding to abnormal neural data with consistent frequency. The association time corresponding to the target area is the time when each abnormal neural data occurs in the target area within a scanning window of the neurological disease auxiliary analysis device.

[0051] It is understood that the target area can be an area consisting of physiological areas where abnormal neural data with consistent frequencies are located. The scanning window can be a window for capturing neural activity. Abnormal neural data with consistent frequencies can be abnormal neural data with the same number of occurrences within the same time window, or abnormal neural data with consistent waveforms. Optionally, a timer can be used to obtain the time when each abnormal neural data occurs in the target area.

[0052] With this setting, by obtaining the appearance time of each abnormal neural data in the target area, the duration of the event can be analyzed and quantified more efficiently, which is helpful for the processing of time series in subsequent analysis.

[0053] S150, obtaining an abnormal data indicator corresponding to each target area according to the associated time and scanning window corresponding to each target area.

[0054] It is understood that the association time corresponding to each target area can be divided by the scanning window to obtain the abnormal data index corresponding to each target area. The abnormal data index corresponding to the target area is used to indicate the possibility that abnormal neural data associated with neurology accumulates in a target area.

[0055] Such a setting helps to provide data support for subsequent analysis.

[0056] S160, obtaining the abnormal data indicator corresponding to each physiological area according to the abnormal data indicator corresponding to each target area and the area corresponding to the physiological area on the user's brain model.

[0057] It can be understood that, when the abnormal data indicator corresponding to each target area is obtained, the abnormal data indicator corresponding to each physiological area can be obtained based on the area corresponding to the physiological area on the user's brain model.

[0058] For example, the physiological region corresponds to an area of ​​10 mm on the user's brain model. 2 The target area corresponding to the physiological area corresponds to an area of ​​120mm on the user's brain model. 2 Therefore, the abnormal data index corresponding to the physiological area can be multiplied by 10 and divided by 120 to obtain the abnormal data index corresponding to the physiological area. That is, the abnormal data index corresponding to the physiological area = the abnormal data index corresponding to the physiological area × 10 ÷ 120.

[0059] With this setting, if only the abnormal data indicators corresponding to the target area are analyzed, the impact of the actual size of the physiological area on the data may not be fully considered. By calculating the area of ​​the physiological area, the impact of abnormal signals in the area can be more accurately reflected, ensuring that the data indicators can more truly reflect the status of neural activity, which in turn helps to improve the accuracy of the final auxiliary analysis.

[0060] S200, obtaining an abnormal risk index corresponding to each physiological region according to characteristic parameters of the user's neural data in each physiological region, wherein the abnormal risk index corresponding to a physiological region is used to reflect the risk level of each abnormal neural data in a physiological region.

[0061] It can be understood that the abnormal risk index corresponding to each physiological region can be obtained based on the actual value of the characteristic parameter and the abnormal risk index of the lower and upper risk thresholds corresponding to the characteristic parameter. , Si is the abnormal risk index corresponding to the ith physiological region, Fi is the actual value of the ith characteristic parameter, Ti,min and Ti,max are the lower and upper limits of the risk threshold of the ith characteristic parameter, respectively. The abnormal risk index corresponding to each physiological region can also be obtained according to the number of abnormal events (e.g., the number of times abnormal neural data occurs in a physiological region), intensity (e.g., the difference between neural data and standard reference data in the same physiological region), and duration (e.g., the duration of abnormal neural data occurring in a physiological region). For example, Si=w1×a+w2×b+w3×c, w1, w2, w3 are the corresponding weighting coefficients, which can be adjusted according to actual conditions. a is the number of abnormal events, b is the intensity, and c is the duration.

[0062] With this setting, the abnormal risk index corresponding to each physiological area is obtained based on the characteristic parameters of the user's neural data in each physiological area, which can accurately understand the risk level corresponding to each area instead of relying on a unified average value, thereby helping to improve the accuracy of the final auxiliary analysis.

[0063] S300, obtaining a neurological abnormality risk level according to each abnormal risk index and the abnormal data indicator corresponding to each abnormal risk index. The abnormal data indicator corresponding to an abnormal risk index is used to reflect the accumulated abnormal data indicator in a designated physiological region corresponding to the abnormal risk index, and the designated physiological region is a physiological region whose abnormal risk index exceeds a preset index.

[0064] For example, the preset physiological regions are divided into physiological region 1, physiological region 2, physiological region 3, and physiological region 4. The abnormal data index corresponding to physiological region 1 is 0.7, and the corresponding abnormal risk index is 10. The abnormal data index corresponding to physiological region 2 is 0.6, and the corresponding abnormal risk index is 9. The abnormal data index corresponding to physiological region 3 is 0.9, and the corresponding abnormal risk index is 13. The abnormal data index corresponding to physiological region 4 is 0.8, and the corresponding abnormal risk index is 12. Among them, the physiological regions whose abnormal risk index exceeds the preset index (for example, 11) are physiological region 3 and physiological region 4. Therefore, the neurological abnormality risk level is 0.9+0.8=1.7, that is, the accumulated abnormal data index in the specified physiological region corresponding to the abnormal risk index.

[0065] It should be noted that for the same user, when other conditions are the same (for example, activity level, sleep conditions, and abnormal data indicators corresponding to other abnormal risk indices except the compared abnormal risk index), the higher the abnormal data indicators corresponding to the same abnormal risk index obtained in different periods (for example, different times), the higher the neurological abnormality risk level; otherwise, the lower the neurological abnormality risk level.

[0066] For example, for the abnormal data indicators corresponding to the abnormal risk index at time a and time b, if the abnormal data indicator corresponding to the abnormal risk index at time a is greater than the abnormal data indicator corresponding to the abnormal risk index at time b, and time a is less than time b, then it can be inferred that the risk level of neural abnormality increases with time; otherwise, it can be inferred that the risk level of neural abnormality decreases with time.

[0067] With this setting, the neural abnormality risk level can be obtained according to each abnormal risk index and the abnormal data indicator corresponding to each abnormal risk index. The abnormal risk index and the abnormal data indicator can be combined to comprehensively analyze the nervous system from multiple dimensions, which is helpful for a comprehensive analysis of neural risks, thereby improving the accuracy of the final auxiliary analysis.

[0068] In a possible implementation, the neurological disease auxiliary analysis method further includes: S400, identifying a preset physiological region according to a stored database.

[0069] It is understood that in the database, physiological regions can be stored by identifiers (such as IDs, names or codes), and the identifiers correspond to standard anatomical or functional region mapping relationships. For example, different regions of the brain may have standard identifiers, such as "PFC" (prefrontal cortex region), "OFC" (orbitofrontal cortex region), "M1" (motor region), etc. The region information in the database can be retrieved based on the stored identifiers (such as region IDs) through SQL queries or data retrieval tools to obtain preset physiological regions.

[0070] With this arrangement, information about the target physiological region can be quickly and accurately located by querying through the identifier, thus avoiding the inefficiency of manual search.

[0071] Alternatively, S500, the user's head motion artifacts are obtained, and the effective data coverage area after the artifacts are eliminated is determined as the preset physiological area. The effective data coverage is used to reflect the ratio of the effective data duration to the preset observation duration.

[0072] It is understood that when collecting neural data, head movement is monitored using head motion sensors (such as video eye trackers, accelerometers, gyroscopes) or artifact markers in electroencephalogram (EEG) signals to capture the movement of the user's head in real time. After obtaining the head movement data, it is possible to determine which data is an artifact caused by head movement by correlating it with signals such as EEG or fMRI. The effective data coverage area can be the area where the duration of effective data after removing the artifacts accounts for the highest proportion of the preset observation time.

[0073] For example, head movement or posture changes can cause noise in EEG signals, or appear as motion artifacts in fMRI. Eye movement can cause electrooculographic artifacts, for example, eye movement, eye opening and closing, etc. Muscle movement (such as opening the mouth, coughing, frowning, etc.) can cause artifacts.

[0074] For example, a threshold of signal quality (e.g., signal strength, frequency, etc.) can be set, and data below the threshold is considered as an artifact, and the remaining part after removal is the valid data. It is also possible to determine whether there is an artifact by checking the volatility and persistence of the data, and the valid data is smooth and stable. After removing the artifact, the ratio of the duration of the valid data to the preset observation duration can be calculated, that is, the ratio of the time period of the valid data to the time period of the preset observation duration, and the area covered by the valid data with the highest ratio is determined as the preset physiological area.

[0075] Such a setting can avoid data errors and deviations caused by artifacts, thereby improving the accuracy of the analysis.

[0076] In a possible implementation, the neurological disease auxiliary analysis method further includes: S510, when the coverage rate corresponding to the valid data coverage area after the artifacts are eliminated does not exceed the first threshold, obtain the coverage rates corresponding to other valid data coverage areas after the artifacts are eliminated.

[0077] It can be understood that the coverage rate can be the ratio of the number of valid data points to the total number of data points acquired in the entire preset area or scanning window. When the coverage rate corresponding to the valid data coverage area after artifact elimination does not exceed the first threshold, it means that the data removed during the artifact elimination process accounts for the majority, that is, the amount of valid data in this area is small, which also means that although the ratio of the time period of valid data to the time period of the preset observation time is large, only the ratio of the time period of 1 or 2 valid data to the time period of the preset observation time is large. Therefore, the coverage rates corresponding to other valid data coverage areas after artifact elimination can be obtained.

[0078] This setting can avoid using areas with less valid data for analysis, thereby improving the reliability and accuracy of subsequent auxiliary analysis.

[0079] S520, when the coverage rates corresponding to other valid data coverage areas after artifact elimination do not exceed the first threshold, obtain the number of neural data of the user detected in two adjacent valid data coverage areas.

[0080] It can be understood that when the coverage rates corresponding to other valid data coverage areas after artifact elimination do not exceed the first threshold, it means that the amount of valid data in other areas is also small, that is, the current scenario is under a relatively scarce valid data environment. Therefore, two adjacent valid data coverage areas can be merged into a group of physiological areas to obtain multiple groups of two adjacent valid data coverage areas. For each group of two adjacent valid data coverage areas, a counter can be used to count the number of neural data points detected in the combination. It should be noted that "adjacent" areas are areas that are spatially contacting or very close, and adjacent areas can share boundaries or partial areas.

[0081] With this setting, when valid data is scarce, merging adjacent regions can help alleviate the lack of data in a single region, thereby improving the reliability of auxiliary analysis.

[0082] S530, when the number of neural data of the user detected in multiple groups of two adjacent valid data coverage areas exceeds a preset number, and the overlap rate between the multiple groups of two adjacent valid data coverage areas and the user's brain functional areas exceeds a preset threshold, the multiple groups of two adjacent valid data coverage areas are merged into multiple groups of target physiological areas.

[0083] It can be understood that if the number of neural data of the user detected in two adjacent valid data coverage areas exceeds the preset number, it means that the amount of valid data in the area is large, that is, the neural activity in the area is more significant and continuous, which means that there is a strong neural response or activity in the area. The overlap rate can be the area of ​​the overlapping area, for example, the sum of the two adjacent valid data coverage areas accounts for the size of the user's brain functional area. Therefore, multiple groups of two adjacent valid data coverage areas can be screened according to the conditions that the number of neural data exceeds the preset number and the overlap rate exceeds the preset threshold to obtain multiple groups of target physiological areas.

[0084] Such a setting can ensure that the selected effective data coverage area has clear physiological or functional significance, which helps to improve the accuracy of subsequent analysis.

[0085] In a possible implementation, in step S530, when the amount of neural data of the user detected in the multiple groups of two adjacent valid data coverage areas exceeds a preset amount, and the overlap rate between the multiple groups of two adjacent valid data coverage areas and the brain functional areas of the user exceeds a preset threshold, after merging the multiple groups of two adjacent valid data coverage areas into multiple groups of target physiological areas, the neurological disease auxiliary analysis method further includes: S531, determining whether there is a target physiological region that meets a preset condition in the multiple groups of target physiological regions, wherein the preset condition is that there is at least one complete nerve conduction pathway in the target physiological region and the delay corresponding to the nerve data meets a preset delay condition.

[0086] It can be understood that a nerve conduction pathway can be a pathway that is transmitted from one area to another area. For example, a group of target physiological areas includes target physiological area t1 and target physiological area t2, where nerve conduction pathway z is a pathway that is transmitted from target physiological area t1 to target physiological area t2, then there is at least one complete nerve conduction pathway in the target physiological area; otherwise, there is no at least one complete nerve conduction pathway in the target physiological area. The delay corresponding to the neural data can be the time required for the neural data (e.g., action potential, signal frequency) to be transmitted from one area to another.

[0087] This setting can accurately screen the area that needs to be analyzed, provide a clear target area for subsequent analysis, and help improve the accuracy of subsequent analysis.

[0088] S532: When it is determined that there is a target physiological region satisfying a preset condition among the multiple groups of target physiological regions, determine the target physiological region satisfying the preset condition as a preset physiological region.

[0089] It can be understood that for each target physiological region, it can be confirmed whether there is an effective conduction path from one region to another region, and at the same time, the delay of the neural data is analyzed to determine whether it is within a preset threshold. A new list or database is established to record the target physiological regions that meet the preset conditions, and an identifier is set for the target physiological region to facilitate subsequent search.

[0090] Such a setting can ensure that the screened preset physiological areas have a high degree of accuracy to ensure the accuracy of subsequent analysis.

[0091] In a possible implementation, the neurological disease auxiliary analysis method further includes: When the coverage rate corresponding to the valid data coverage area after artifact elimination exceeds the first threshold, the valid data coverage area whose coverage rate exceeds the first threshold is determined as the preset physiological area.

[0092] It can be understood that when the coverage rate corresponding to the effective data coverage area after artifact elimination exceeds the first threshold, it means that the data removed during the artifact elimination process accounts for a small number, that is, the amount of effective data in this area is large, and it also means that the ratio of the time period of effective data to the time period of the preset observation time is large, and the ratio of the time period of multiple (for example, 10, 15, etc.) effective data to the time period of the preset observation time is large. Therefore, the effective data coverage area with a coverage rate exceeding the first threshold can be determined as the preset physiological area.

[0093] Such a setting can ensure that the screened preset physiological areas have a high degree of accuracy to ensure the accuracy of subsequent analysis.

[0094] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0095] Corresponding to the auxiliary analysis method for neurological diseases described in the above embodiment, the embodiment of the present application also provides an auxiliary analysis device for neurological diseases, and each unit of the device can implement each step of the auxiliary analysis method for neurological diseases. Figure 4 A structural block diagram of a neurological disease auxiliary analysis device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0096] Reference Figure 4 , the auxiliary analysis device for neurological diseases includes: The acquisition unit is used to obtain the characteristic parameters of the user's neural data in each physiological area within the preset physiological area, and the abnormal data index corresponding to each physiological area. Wherein, each physiological area is divided into the same area based on the user's brain model. The abnormal data index corresponding to a physiological area is used to reflect the possibility that each abnormal neural data associated with neurology accumulates in a physiological area.

[0097] The calculation unit is used to obtain the abnormal risk index corresponding to each physiological area according to the characteristic parameters of the user's neural data in each physiological area. The abnormal risk index corresponding to a physiological area is used to reflect the risk level of each abnormal neural data in a physiological area.

[0098] The analysis unit is used to obtain the neurological abnormality risk level according to each abnormal risk index and the abnormal data index corresponding to each abnormal risk index. The abnormal data index corresponding to an abnormal risk index is used to reflect the accumulated abnormal data index in a designated physiological region corresponding to the abnormal risk index, and the designated physiological region is a physiological region whose abnormal risk index exceeds a preset index.

[0099] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0100] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units is used as an example for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0101] The present application also provides a neurological disease auxiliary analysis device, Figure 5 This is a schematic diagram of the structure of a control device for a neurological disease auxiliary analysis device provided in one embodiment of the present application. Figure 5 As shown, the control device 6 of this embodiment includes: at least one processor 60 ( Figure 5 Only one is shown), at least one memory 61 ( Figure 5 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the control device 6 implements the steps in any of the above-mentioned embodiments of the auxiliary analysis method for neurological diseases, or implements the functions of the units in the above-mentioned device embodiments.

[0102] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the control device 6.

[0103] The neurological disease auxiliary analysis equipment may include a neurological disease auxiliary analysis device and a control device 6 electrically connected to the neurological disease auxiliary analysis device. The neurological disease auxiliary analysis device may be an electroencephalogram device (used to obtain real-time neural data from the user's brain to monitor and record brain electrical activity), a functional magnetic resonance imaging device (used to monitor the activities of different brain regions in real time to identify abnormal activities in different brain regions), etc. The control device 6 can read the data obtained by the neurological disease auxiliary analysis device, and analyze the neural data obtained by the neurological disease auxiliary analysis device to obtain a risk level of neural abnormality. The control device 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that Figure 5 It is only an example of the control device 6 and does not constitute a limitation on the control device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0104] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] In some embodiments, the memory 61 may be an internal storage unit of the control device 6, such as a hard disk or memory of the control device 6. In other embodiments, the memory 61 may also be an external storage device of the control device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the control device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the control device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0106] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0107] An embodiment of the present application provides a computer program product. When the computer program product is run on a neurological disease auxiliary analysis device, the neurological disease auxiliary analysis device implements the steps in any of the above-mentioned method embodiments.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the auxiliary analysis device for neurological diseases, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0109] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] In the embodiments provided in the present application, it should be understood that the disclosed neurological disease auxiliary analysis device, neurological disease auxiliary analysis equipment and neurological disease auxiliary analysis method can be implemented in other ways. For example, the above-described neurological disease auxiliary analysis device and neurological disease auxiliary analysis device embodiments are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A neurological disease auxiliary analysis method, characterized in that: The method comprises: Acquire characteristic parameters of each physiological region of the user's neural data in the preset physiological regions, and abnormal data indicators corresponding to each physiological region; wherein, based on the user's brain model, each physiological region is divided into the same region; the abnormal data indicator corresponding to one physiological region is used to reflect the possibility that each abnormal neural data associated with neurology accumulates and occurs in one physiological region; According to the characteristic parameters of the user's neural data in each physiological area, an abnormal risk index corresponding to each physiological area is obtained; wherein the abnormal risk index corresponding to one physiological area is used to reflect the risk level of each abnormal neural data in one physiological area; According to each of the abnormal risk indexes and the abnormal data indicators corresponding to each of the abnormal risk indexes, a neurological abnormality risk level is obtained; wherein, the abnormal data indicator corresponding to one of the abnormal risk indexes is used to reflect the abnormal data indicators accumulated in a designated physiological region corresponding to one of the abnormal risk indexes, and the designated physiological region is a physiological region where the abnormal risk index exceeds a preset index.

2. The neurological disease auxiliary analysis method according to claim 1, characterized in that: Obtain the characteristic parameters of each physiological area of ​​the user's neural data in the preset physiological area, including: Acquire neural data of the user; wherein the neural data is data generated by unprocessed brain neuron activity; Extracting characteristic data of the neural data in a preset physiological area according to the neural data; wherein the characteristic data is used to reflect the concentration degree of characteristic values ​​in each value range; The characteristic parameter corresponding to the physiological region is determined according to the characteristic data of the neural data on the preset physiological region.

3. The neurological disease auxiliary analysis method according to claim 1, characterized in that: Obtaining abnormal data indicators corresponding to each of the physiological regions, including: Determine the association time corresponding to each target area according to the neural data; wherein each target area is an area consisting of the physiological area corresponding to the abnormal neural data with consistent frequency; the association time corresponding to the target area is the time when each abnormal neural data occurs in the target area within a scanning window of the neurological disease auxiliary analysis device; Obtaining the abnormal data indicator corresponding to each target area according to the associated time and the scanning window corresponding to each target area; According to the abnormal data indicator corresponding to each target area and the area corresponding to the physiological area on the user's brain model, the abnormal data indicator corresponding to each physiological area is obtained.

4. The neurological disease auxiliary analysis method according to claim 2, characterized in that: Determining the characteristic parameter corresponding to the physiological region according to the characteristic data of the neural data on the preset physiological region includes: The characteristic values ​​corresponding to the characteristic data are averaged with the values ​​having the largest concentration in each value range to obtain the characteristic parameter corresponding to the physiological area.

5. The neurological disease auxiliary analysis method according to claim 1, characterized in that: The method further comprises: Identifying the preset physiological region according to a stored database; or, The user's head movement artifacts are obtained, and the effective data coverage area after the artifacts are eliminated is determined as the preset physiological area; wherein the effective data coverage is used to reflect the ratio of the effective data duration to the preset observation duration.

6. The neurological disease auxiliary analysis method according to claim 5, characterized in that: The method further comprises: When the coverage rate corresponding to the valid data coverage area after the artifacts are eliminated does not exceed the first threshold, obtaining the coverage rates corresponding to other valid data coverage areas after the artifacts are eliminated; When the coverage rates corresponding to the valid data coverage areas after other artifacts are eliminated do not exceed the first threshold, obtaining the number of neural data of the user detected in two adjacent valid data coverage areas; When the number of neural data of the user detected in multiple groups of two adjacent valid data coverage areas exceeds a preset number, and the overlap rate between multiple groups of two adjacent valid data coverage areas and the user's brain functional areas exceeds a preset threshold, the multiple groups of two adjacent valid data coverage areas are merged into multiple groups of target physiological areas.

7. The neurological disease auxiliary analysis method according to claim 6, characterized in that: After merging multiple groups of two adjacent valid data coverage areas into multiple groups of target physiological areas, the method further includes: Determine whether there is a target physiological region that meets a preset condition in the multiple groups of target physiological regions; wherein the preset condition is that there is at least one complete nerve conduction pathway in the target physiological region and the delay corresponding to the nerve data meets a preset delay condition; In the case that it is determined that there is a target physiological region satisfying the preset condition among the multiple groups of target physiological regions, the target physiological region satisfying the preset condition is determined as the preset physiological region.

8. The neurological disease auxiliary analysis method according to claim 5, characterized in that: The method further comprises: When the coverage rate corresponding to the valid data coverage area after the artifacts are eliminated exceeds a first threshold, the valid data coverage area with the coverage rate exceeding the first threshold is determined as the preset physiological area.

9. A neurological disease auxiliary analysis device, characterized in that: Used to implement the neurological disease auxiliary analysis method according to any one of claims 1 to 8, the neurological disease auxiliary analysis device is applied to neurological disease auxiliary analysis equipment, and the neurological disease auxiliary analysis device comprises: An acquisition unit, used to acquire characteristic parameters of each physiological region of the user's neural data in a preset physiological region, and an abnormal data index corresponding to each physiological region; wherein, based on the user's brain model, each physiological region is divided into the same region; and the abnormal data index corresponding to one physiological region is used to reflect the possibility that each abnormal neural data associated with neurology accumulates and occurs in one physiological region; A calculation unit, used for obtaining an abnormal risk index corresponding to each physiological region according to the characteristic parameters of the user's neural data in each physiological region; wherein the abnormal risk index corresponding to one physiological region is used to reflect the risk level of each abnormal neural data in one physiological region; An analysis unit is used to obtain a neurological abnormality risk level according to each of the abnormal risk indexes and the abnormal data indicators corresponding to each of the abnormal risk indexes; wherein the abnormal data indicator corresponding to one of the abnormal risk indexes is used to reflect the abnormal data indicators accumulated in a designated physiological region corresponding to one of the abnormal risk indexes, and the designated physiological region is a physiological region where the abnormal risk index exceeds a preset index.

10. A neurological disease auxiliary analysis device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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