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Brain age assessment method for Rolandic epilepsy children based on machine learning

A machine learning, epilepsy technology, used in sensors, medical science, telemetry patient monitoring, etc.

Pending Publication Date: 2021-10-26
AFFILIATED HOSPITAL OF ZUNYI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] However, so far, most of the current research on the development of the Rolandic epileptic brain is based on brain imaging data on its structural and / or functional development, with or without cognition-related studies, and there is little research on the developmental maturity of the Rolandic epileptic brain. related research reports

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  • Brain age assessment method for Rolandic epilepsy children based on machine learning
  • Brain age assessment method for Rolandic epilepsy children based on machine learning
  • Brain age assessment method for Rolandic epilepsy children based on machine learning

Examples

Experimental program
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Effect test

Embodiment 1

[0053] The basic method of the present invention to realize the brain age assessment of children with Rolandic epilepsy is:

[0054] S100. Collect the MRI data of the subjects in the control group, and perform effective model feature extraction through dimensionality reduction;

[0055] S200. Construct a prediction model based on the effective model features extracted in S100;

[0056] S300. Collect the MRI data of the epileptic child and extract the characteristics of the tested model, input the characteristics of the tested model into the prediction model and output the prediction result;

[0057] Wherein, the MRI data in S100 includes brain gray matter thickness, brain gray matter surface area, and brain gray matter volume, and the effective model features include at least one of the gray matter thickness, surface area of ​​gray matter, and gray matter volume in each subdivided part index and mixed index.

[0058] see Figure 14 , the more specific implementation method i...

Embodiment 2

[0064] Referring to the more specific implementation method described in Example 1, this example will describe the implementation process in detail.

[0065] 1. Object selection

[0066] Fifty children with Rolandic epilepsy and 50 healthy control children matched in gender, age and years of education were selected.

[0067] Case group inclusion and exclusion criteria:

[0068] (1) Inclusion criteria:

[0069] 1) Meet the Rolandic epilepsy diagnosis;

[0070] 2) Age 6-16 years old;

[0071] (2) Exclusion criteria:

[0072] 1) Those with MRI contraindications and those with claustrophobia;

[0073] 2) Heart, lung and other vital organ failure;

[0074] 3) History of neuropsychiatric diseases other than Rolandic epilepsy, such as trauma, tumor, infection, etc.;

[0075] 4) Those with abnormal brain routine MRI examination;

[0076] 5) MRI image artifacts seriously affect image analysts.

[0077] Inclusion and exclusion criteria for the control group:

[0078] (1) Inclu...

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Abstract

The invention discloses a brain age assessment method for Rolandic epilepsy children based on machine learning, which comprises the following steps: S100, collecting MRI data of a control group object, and performing effective model feature extraction through dimension reduction; S200, constructing a prediction model based on the effective model features extracted in S100; and S300, collecting MRI data of epilepsy children, extracting features of the test model, inputting the features of the test model into the prediction model, and outputting a prediction result, wherein the MRI data in the S100 comprises the thickness of the brain grey matter, the surface area of the brain grey matter and the volume of the brain grey matter; and the effective model features comprise at least one of respective subdivision part indexes and mixing indexes in the thickness of the brain grey matter, the surface area of the brain grey matter and the volume of the brain grey matter. According to the invention, the SVR brain age prediction model based on the features of the brain grey matter morphological indexes is adopted; and the brain age and the brain maturity of the Rolandic epilepsy children can be preliminarily evaluated.

Description

technical field [0001] The invention belongs to the technical field of functional index evaluation, in particular to a machine learning-based brain age evaluation method for children with Rolandic epilepsy. Background technique [0002] Rolandic epilepsy, also known as benign childhood epilepsy with centrotemporal spikes (BECTS), is the most common childhood epilepsy syndrome, accounting for about 8-23% of childhood epilepsy, and the onset age of Rolandic epilepsy is 3 -13 years old, 8-10 years old is the onset peak, the male to female ratio is 6:4, most children with Rolandic epilepsy can spontaneously remit before the age of 16. In the past, Rolandic epilepsy was considered to be a benign syndrome with few neurological or intellectual deficits, natural regression in puberty, and low recurrence rate after disappearance. [0003] With the deepening of research content and the development of research technology, there is now sufficient evidence that children with Rolandic ep...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/055
CPCA61B5/4064A61B5/4094A61B5/055A61B5/0042A61B5/0033A61B5/7267A61B5/7271
Inventor 刘衡王富勤常永虎刘军委李同欢
Owner AFFILIATED HOSPITAL OF ZUNYI UNIV
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