A child neuropsychological development monitoring and management system

Abstract

The application provides a child neuropsychological development monitoring and management system. The system uses brain physiological parameters, brain MRI images, development scale characteristics and corresponding child neuropsychological development suffering grades to construct a child neuropsychological development evaluation model. The constructed child neuropsychological development evaluation model generates a current child neuropsychological development suffering grade of a user according to real-time brain physiological parameters collected by a system terminal, real-time brain MRI images monitored by a hospital and corresponding real-time development scale characteristics, provides a basis for whether the corresponding child needs further monitoring according to the current child neuropsychological development suffering grade, and improves the probability of finding potential abnormal development children. The application uses three kinds of fusion parameters related to child brain nerves, such as brain physiological parameters, brain image characteristics and subjective measurement table characteristics, to construct a high-precision real-time processing model, realizes intelligent monitoring of the morbidity of child neuropsychological development disorders by doctors, and reduces the burden on families.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of neural engineering detection, and particularly relates to a child neuro-psychological development monitoring and management system. BACKGROUND

[0002] In recent years, the incidence of child neuro-psychological development behavior-related diseases has been increasing year by year, and the proportion of children under 5 years of age with developmental delay worldwide is as high as 15%. Studies on the prevalence of ASD (Autism Spectrum Disorder) in children aged 6 to 12 years old estimate that the prevalence of ASD in the target population is about 0.7%, and 68.8% of ASD children have at least one comorbid disease; the prevalence of ADHD (Attention Deficit Hyperactivity Disorder) children is about 5.59%, and there is a trend of increasing year by year. Due to the complex pathogenesis of neurodevelopmental disorders, it is often caused by the complex interaction between predisposing factors and the environment, and the strong heterogeneity of clinical phenotypes, which leads to the difficulty of early identification and diagnosis of such diseases, but if early identification and intervention can be made, the symptoms of most children can be effectively improved.

[0003] Child neuro-psychological development assessment refers to using standardized assessment tools to conduct comprehensive development assessment on the test children to identify the developmental delay or abnormality of the children. Developmental scales, as the most commonly used assessment tools, can comprehensively assess and observe the development behavior of children to assist child care physicians in making correct clinical judgments, and are therefore widely used in group child preventive care work. There are various types of child neurodevelopmental scales, which can be divided into screening tests and diagnostic tests according to their application scope and purpose: developmental screening scales are mostly simple and time-saving, and when the developmental screening result is suspicious or even abnormal, further diagnostic testing is required to assist in diagnosis to determine whether the test children need to receive intervention.

[0004] It can be seen that the existing child neuro-psychological development assessment only relies on subjective scales, and the detection of child neuro-psychological development is single, and the big data model in a specific application scenario is not well utilized to process related data to obtain a precise child neurodevelopmental disorder monitoring method.

[0005] The present application is based on the above problems, designs a child neuropsychological development monitoring and management system, uses the fusion parameters of three characteristics of brain physiological data, subjective measurement table characteristics, constructs a high-precision real-time processing model to realize intelligent monitoring of the prevalence rate of child neurodevelopmental disorder disease by doctors, and reduces the family burden. The present application meets the problems that the number of related child doctors is small, real-time diagnosis for children cannot be well realized, and real-time monitoring of the neuropsychological development of children is not good. The present application ensures the neuropsychological development health and development quality of children through intelligent monitoring and evaluation effect. SUMMARY

[0006] To solve the above technical problems, the present application provides a child neuropsychological development monitoring and management system.

[0007] In the first aspect of the present application, a child neuropsychological development monitoring and management method is provided, characterized in that the method comprises:

[0008] obtaining a first brain physiological parameter, a first brain MRI image, a first development scale characteristic and a corresponding first child neuropsychological development suffering level of a child;

[0009] generating a first child neuropsychological development vector by using the first brain physiological parameter, the first brain MRI image and the first development scale characteristic;

[0010] constructing a child neuropsychological development evaluation model according to the first child neuropsychological development vector and the first child neuropsychological development suffering level;

[0011] receiving a second brain physiological parameter, a second brain MRI image and a second development scale characteristic of a child, generating a second child neuropsychological development vector by using the second brain physiological parameter, the second brain MRI image and the second development scale characteristic, generating a second child neuropsychological development suffering level by using the child neuropsychological development evaluation model according to the second child neuropsychological development vector, and intelligently monitoring the prevalence rate of child neurodevelopmental disorder disease according to the second child neuropsychological development suffering level.

[0012] Further, the first brain physiological parameter or the second brain physiological parameter comprises brain electrical data or brain neurotransmitter data, and the first brain MRI image comprises a historical brain MRI image.

[0013] Further, the brain electrical data is collected by using a wireless electroencephalogram collection device Neuro Scan, the electroencephalogram collection device can collect electroencephalogram signals at 128 brain-computer interfaces, and the first development scale characteristic or the second development scale characteristic Q is obtained by using a pre-school child ability screening table or an ASQ questionnaire.

[0014] Further, the specific brain acquisition region of the brain electrical data is T3, T4, C3, C4, CZ, OZ, O1, O2, P7 and P8;

[0015] Further, the first brain MRI image or the second brain MRI image is processed to obtain a first brain MRI optimized image or a second brain MRI optimized image, and step S1 is as follows:

[0016] The first brain MRI image or the second brain MRI image is acquired by using an MRI device, and the acquired image is subjected to regional subdivision processing, and the acquired isosceles image is divided into 12 equal parts, and the first brain MRI image parameter or the second brain MRI image parameter is the feature processing value of each region image after the 12 equal division of the acquired first brain MRI image or the second brain MRI image.

[0017] Further, the first brain MRI image parameter or the second brain MRI image parameter is processed to obtain a first brain MRI optimized image parameter or a second brain MRI optimized image parameter, and the final step S2 is as follows:

[0018] The first brain MRI image parameter or the second brain MRI image parameter is processed by using an image unification processing method.

[0019] Further, the feature vectors B f , the first brain MRI image parameter or the second brain MRI image parameter obtained by unification processing, and the first developmental scale feature or the second developmental scale feature Q are spliced to obtain the first child neuropsychological development vector or the second child neuropsychological development vector.

[0020] Further, the child neuropsychological development evaluation model adopts a classifier based on Fisher criterion.

[0021] A child neuropsychological development monitoring and management system is also provided, and the system comprises a brain physiological data acquisition module, a developmental scale feature acquisition module, an MRI device acquisition module, a neuropsychological development data storage module, a child neuropsychological development evaluation model construction module, and a child neuropsychological development disorder monitoring module, and the system is characterized in that:

[0022] The brain physiological data acquisition module is used to acquire the first brain physiological parameter or the second brain physiological parameter of a child;

[0023] The first brain physiological parameter or the second brain physiological parameter comprises brain electrical data or brain neurotransmitter data;

[0024] The development scale feature acquisition module is used for acquiring a first development scale feature or a second development scale feature, and the first development scale feature or the second development scale feature is obtained through a pre-school child ability screening table or an ASQ questionnaire.

[0025] The MRI device acquisition module is used for acquiring a first brain MRI image or a second brain MRI image, and the first brain MRI image comprises historical brain MRI images.

[0026] The neuropsychological development data storage module is used for receiving the first brain physiological parameter or the second brain physiological parameter in the brain physiological data acquisition module, the first development scale feature or the second development scale feature in the development scale feature acquisition module, and the first brain MRI image or the second brain MRI image in the MRI device acquisition module, and processing the first brain physiological parameter, the first development scale feature and the first brain MRI image to obtain a first child neuropsychological development vector, and processing the second brain physiological parameter, the second development scale feature and the second brain MRI image to obtain a second child neuropsychological development vector.

[0027] The child neuropsychological development evaluation model construction module is used for acquiring the first child neuropsychological development vector and a corresponding first child neuropsychological development affliction grade, and constructing a child neuropsychological development evaluation model by using the first child neuropsychological development vector and the corresponding first child neuropsychological development affliction grade.

[0028] The child neuropsychological development disorder monitoring module is used for receiving the second child neuropsychological development vector, and calling the child neuropsychological development evaluation model to process a second child neuropsychological development affliction grade, and intelligently monitoring a prevalence rate of a child neuropsychological development disorder disease according to the second child neuropsychological development affliction grade.

[0029] The present application combines brain MRI image data and brain physiological parameter data, especially electrical physiological parameter data, in the detection and management of child neuropsychological development, and can more accurately evaluate the child neuropsychological development condition. Through the monitoring scene of a specific neuropsychological development child, the brain MRI image is subjected to specific image channel unification processing, the data quality of the training model is enhanced, the child neuropsychological development state is monitored by using multi-dimensional data, the change processing of a more accurate targeted management scheme can be achieved, and the brain electrical data can be subjected to wireless real-time transmission and acquisition, so that the monitoring data processing can be more conveniently performed. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a flow chart of a child neuropsychological development monitoring and management method of the present application;

[0031] Figure 2 is a structural schematic diagram of a child neuropsychological development monitoring and management system of the present application;

[0032] Figure 3 is a classification principle diagram of a classifier based on Fisher criterion in the present application;

[0033] Figure 4 is a part of a preschool child ability screening table in an embodiment of the present application;

[0034] Figure 5 is a brain MRI image after processing in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The application will be further described in conjunction with the drawings and specific embodiments.

[0036] First embodiment of the present application:

[0037] To solve the above technical problems, the present application provides a child neuropsychological development monitoring and management system.

[0038] In a first aspect of the present application, a child neuropsychological development monitoring and management method is provided, characterized in that the method comprises:

[0039] obtaining a first brain physiological parameter, a first brain MRI image, a first development scale feature and a corresponding first child neuropsychological development affected level of a child;

[0040] generating a first child neuropsychological development vector by using the first brain physiological parameter, the first brain MRI image and the first development scale feature;

[0041] constructing a child neuropsychological development evaluation model according to the first child neuropsychological development vector and the first child neuropsychological development affected level;

[0042] receiving a second brain physiological parameter, a second brain MRI image and a second development scale feature of a child, generating a second child neuropsychological development vector according to the second brain physiological parameter, the second brain MRI image and the second development scale feature, generating a second child neuropsychological development affected level according to the second child neuropsychological development vector by using the child neuropsychological development evaluation model, and intelligently monitoring the prevalence of a child neuropsychological development disorder disease according to the second child neuropsychological development affected level.

[0043] In the embodiment, the first child neuropsychological development affected grade or the second child neuropsychological development affected grade is divided into three grades of normal, slight and severe, which is set by experts according to the diagnosis of children.

[0044] Further, the first brain physiological parameter or the second brain physiological parameter includes brain electrical data or brain neurotransmitter data, and the first brain MRI image includes a historical brain MRI image.

[0045] Further, the brain electrical data is collected by a wireless electroencephalogram acquisition device Neuro Scan, the electroencephalogram acquisition device can collect brain electrical signals at 128 brain-computer interfaces, and the first development scale feature or the second development scale feature Q is obtained by a pre-school children ability screening table or an ASQ questionnaire.

[0046] In the embodiment, the pre-school children ability screening table is 50 items of screening, wherein Figure 4 Only 12 of them are shown. Each item is set to 0-2 points according to the completion, and the answer gives the score value, which is the first development scale feature or the second development scale feature Q. In the embodiment, Q can be set to 75.

[0047] In the embodiment, since the degree of complete development of children's brain is different, the ratio of mature development days to actual development days of children is set and corrected to obtain a conventional brain electrical physiological parameter, and the deviation after correction of the brain electrical physiological parameter is processed, so that the quality of the subsequent feature vector is higher and fits the children.

[0048] Further, the specific brain acquisition area of the brain electrical data is T3, T4, C3, C4, CZ, OZ, O1, O2, P7 and P8;

[0049] Further, the feature vector B of the first brain physiological parameter or the second brain physiological parameter f The calculation formula is as follows:

[0050]

[0051] In the formula, k is an adjustment coefficient, D is the number of days of child development, B is the number of days of complete brain development, S1, S2,..., S 10 are the amplitudes of the brain electrical signals measured at the 10 brain-computer interfaces of T3, T4, C3, C4, CZ, OZ, O1, O2, P7 and P8 in a period of time, and the unit is μV.

[0052] In the embodiment, the value of B f can be 3.223 μV

[0053] Further, the first brain MRI image or the second brain MRI image is processed to obtain a first brain MRI optimized image or a second brain MRI optimized image, and step S1 is as follows:

[0054] The first brain MRI image or the second brain MRI image is obtained by using an MRI device, and the obtained image is processed by regional subdivision, and the obtained isosceles image is divided into 12 equal parts, and the first brain MRI image parameter or the second brain MRI image parameter is the feature processing value of each regional image after the 12 equal division of the obtained first brain MRI image or the second brain MRI image, and the processing formula is:

[0055]

[0056] F s =(E(i,j),N(i,j),U(i,j))

[0057] In the formula, E(i,j), N(i,j), and U(i,j) are three channel gray scale feature values of the image, k R , k G , and k B are correction coefficients, R n , G n , and B n are image feature values of three color channels of the nth image after equal division.

[0058] Further, the first brain MRI image parameter or the second brain MRI image parameter is processed to obtain a first brain MRI optimized image parameter or a second brain MRI optimized image parameter, and the final step S2 is as follows:

[0059] The first brain MRI image parameter or the second brain MRI image parameter is processed by using an image unification processing method, and the formula of the image unification processing method is:

[0060] T=G*E(i,j)+H*N(i,j)+J*U(i,j)

[0061] In the formula, T is the image feature value after image unification processing, E(i,j), N(i,j), and U(i,j) are three channel gray scale feature values of the image, i and j are pixel values, and G, H, and J are image unification feature weighting value parameters.

[0062] In this embodiment, the image feature value T after unification can be 123.

[0063] Further, the feature vector B f, T and the first or second developmental scale feature Q spliced to obtain the first or second child neuropsychological development vector.

[0064] The first child neuropsychological development vector after splicing in this embodiment is (B f , T, Q) = (3.223, 123, 75).

[0065] Further, the child neuropsychological development assessment model adopts a classifier based on Fisher criterion, and the calculation formula is as follows:

[0066]

[0067] M is the first or second child neuropsychological development vector, D(M) is the output first or second child neuropsychological development affected level, W T is the normal vector perpendicular to the hyperplane, which is obtained by training the first child neuropsychological development vector and the first child neuropsychological development affected level, |V is one of the G, H, J image integration feature weight coefficients selected according to the brightness of the first or second brain MRI image, k is an adjustment coefficient, D is the number of days of child development, and B is the number of days of brain development.

[0068] In this embodiment, the first or second child neuropsychological development affected level is judged according to the value of D(M). When D(M) is less than 0, it indicates that the child's neuropsychological development condition is mild; when D(M) is equal to 0, it indicates that the child's neuropsychological development condition is normal; and when D(M) is greater than 0, it indicates that the child's neuropsychological development condition is severe, and further examination and diagnosis are needed.

[0069] A child neuropsychological development monitoring and management system is also provided, which comprises a brain physiological data acquisition module, a developmental scale feature acquisition module, an MRI device acquisition module, a neuropsychological development data storage module, a child neuropsychological development assessment model construction module, and a child neuropsychological development disorder monitoring module, and the system is characterized in that:

[0070] The brain physiological data acquisition module is used to acquire the first or second brain physiological parameter of the child.

[0071] The first or second brain physiological parameter comprises brain electrical data or brain neurotransmitter data.

[0072] The development scale feature acquisition module is used for acquiring a first development scale feature or a second development scale feature, wherein the first development scale feature or the second development scale feature is obtained through a pre-school child ability screening table or an ASQ questionnaire.

[0073] The MRI device acquisition module is used for acquiring a first brain MRI image or a second brain MRI image, wherein the first brain MRI image comprises a historical brain MRI image.

[0074] The neuropsychological development data storage module is used for receiving the first brain physiological parameter or the second brain physiological parameter in the brain physiological data acquisition module, the first development scale feature or the second development scale feature in the development scale feature acquisition module, and the first brain MRI image or the second brain MRI image in the MRI device acquisition module, and processing the first brain physiological parameter, the first development scale feature and the first brain MRI image to obtain a first child neuropsychological development vector, and processing the second brain physiological parameter, the second development scale feature and the second brain MRI image to obtain a second child neuropsychological development vector.

[0075] The child neuropsychological development evaluation model construction module is used for acquiring the first child neuropsychological development vector and a corresponding first child neuropsychological development affliction grade, and constructing a child neuropsychological development evaluation model by using the first child neuropsychological development vector and the corresponding first child neuropsychological development affliction grade.

[0076] The child neuropsychological development disorder monitoring module is used for receiving the second child neuropsychological development vector, and calling the child neuropsychological development evaluation model to process a second child neuropsychological development affliction grade, and intelligently monitoring a prevalence rate of a child neuropsychological development disorder disease according to the second child neuropsychological development affliction grade.

[0077] The present application combines brain MRI image data and brain physiological parameter data, especially electro-physiological parameter data, in the detection and management of child neuropsychological development, and can more accurately evaluate the child neuropsychological development condition. Through the monitoring scene of a specific neuropsychological development child, the brain MRI image is processed by specific channel unification, the data quality of the training model is enhanced, the child neuropsychological development state is monitored by using multi-dimensional data, the change processing of a more accurate targeted management scheme can be achieved, and the electroencephalogram data can be wirelessly and real-timely collected, so that the monitoring data processing can be more conveniently performed.

[0078] The combination of the embodiments of the present application can realize all the effects described above, but it is not required that each embodiment of the present application realizes all the advantages and effects described above, because each embodiment of the present application can constitute a separate technical solution and make one or more contributions to the prior art.

[0079] The module structure of the part of the present application which is not particularly clear is subject to the content described in the prior art. The prior art mentioned in the foregoing background section and the specific embodiment section of the present application can be used as a part of the present application to understand the meaning of some technical features or parameters. The scope of protection of the present application is subject to the content actually described in the claims.

Claims

1. A method of monitoring and managing neuro-psychological development of a child, characterized in that, The method comprises: obtaining first brain physiological parameters, first brain MRI images, first developmental scale characteristics and corresponding first child neuropsychological development suffering levels of a child; generating a first child neuropsychological development vector by using the first brain physiological parameters, the first brain MRI images and the first developmental scale characteristics; constructing a child neuropsychological development evaluation model according to the first child neuropsychological development vector and the first child neuropsychological development suffering level; receiving second brain physiological parameters, second brain MRI images and second developmental scale characteristics of a child, generating a second child neuropsychological development vector according to the second brain physiological parameters, the second brain MRI images and the second developmental scale characteristics, generating a second child neuropsychological development suffering level according to the second child neuropsychological development vector by using the child neuropsychological development evaluation model, and intelligently monitoring the prevalence of a child neuropsychological development disorder disease according to the second child neuropsychological development suffering level; a feature vector of the first brain physiological parameter or the second brain physiological parameter The formula for calculating is as follows: ； In the formula, k is an adjustment coefficient, D is the number of days of child development, B is the number of days of complete brain development, , , is the amplitude of the electroencephalogram signal measured at the 10 brain-computer interface positions of T3, T4, C3, C4, CZ, OZ, O1, O2, P7 and P8 in a period of time, in units of ; feature processing the first brain MRI images or the second brain MRI images to obtain first brain MRI optimized images or second brain MRI optimized images, and step S1 is as follows: obtaining first brain MRI images or second brain MRI images by using an MRI device, and performing regional subdivision processing on the obtained images, performing 12 equal division on the obtained equal area pictures, and the first brain MRI image parameters or the second brain MRI image parameters are feature processing values of each regional image after 12 equal division of the obtained first brain MRI images or second brain MRI images, and the processing formula is: ； ； ； ； In the formula, , is the gray feature value of the three channels of the image, , , is the correction coefficient, , , is the image feature value of the three color channels of the n-th image after equalization; feature processing the first brain MRI image parameters or the second brain MRI image parameters to obtain first brain MRI image optimized parameters or second brain MRI image optimized parameters, and the final step S2 is as follows: processing the first brain MRI image parameters or the second brain MRI image parameters by using an image unification processing method, and the formula of the image unification processing method is: ； In the formula is the image feature value after image unification processing, , is the gray feature value of the three channels of the image, are all pixel values, and G, H, and J are image unification feature weighting value parameters.

2. The child neuropsychological development monitoring and management method according to claim 1, characterized in that: the first brain physiological parameters or the second brain physiological parameters comprise brain electrical data, and the first brain MRI images comprise historical brain MRI images.

3. The child neuropsychological development monitoring and management method according to claim 2, characterized in that: the brain electrical data is collected by using a wireless brain electrical collection device Neuro Scan, the brain electrical collection device can collect brain electrical signals at 128 brain-computer interfaces, and the first developmental scale characteristics or the second developmental scale characteristics Q are obtained by using a pre-school child ability screening table or an ASQ questionnaire.

4. The child neuropsychological development monitoring and management method according to claim 3, characterized in that: a feature vector of the first brain physiological parameter or the second brain physiological parameter , T obtained by the corresponding first brain MRI image parameter or second brain MRI image parameter unification processing and the first development scale feature or the second development scale feature Q splicing to obtain the first child neuropsychological development vector or the second child neuropsychological development vector.

5. The child neuropsychological development monitoring and management method according to claim 4, characterized in that: the child neuropsychological development evaluation model adopts a classifier based on Fisher criterion.

6. A child neuropsychological development monitoring and management system, the system implements the method of any one of claims 1-5, the system comprises a brain physiological data acquisition module, a development scale feature acquisition module, an MRI device acquisition module, a neuropsychological development data storage module, a child neuropsychological development assessment model construction module, a child neuropsychological development disorder monitoring module, characterized in that: the brain physiological data acquisition module is used to acquire first brain physiological parameters or second brain physiological parameters of a child; the first brain physiological parameters or the second brain physiological parameters comprise brain electrical data; the development scale feature acquisition module is used to acquire first development scale features or second development scale features, the first development scale features or the second development scale features are obtained through a pre-school child ability screening table or an ASQ questionnaire; the MRI device acquisition module is used to acquire first brain MRI images or second brain MRI images, the first brain MRI images comprise historical brain MRI images; the neuropsychological development data storage module is used to receive the first brain physiological parameters or the second brain physiological parameters in the brain physiological data acquisition module, the first development scale features or the second development scale features in the development scale feature acquisition module, and the first brain MRI images or the second brain MRI images in the MRI device acquisition module, and process the first brain physiological parameters, the first development scale features, and the first brain MRI images to obtain a first child neuropsychological development vector, and process the second brain physiological parameters, the second development scale features, and the second brain MRI images to obtain a second child neuropsychological development vector; the child neuropsychological development assessment model construction module acquires the first child neuropsychological development vector and a corresponding first child neuropsychological development affliction level, and constructs a child neuropsychological development assessment model by using the first child neuropsychological development vector and the corresponding first child neuropsychological development affliction level; and the child neuropsychological development disorder monitoring module receives the second child neuropsychological development vector, and processes the second child neuropsychological development vector by calling the child neuropsychological development assessment model to obtain a second child neuropsychological development affliction level, and intelligently monitors a prevalence rate of a child neuropsychological development disorder disease according to the second child neuropsychological development affliction level. ​ ​ ​ ​ ​ ​ ​

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