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Brain signal complexity based individual age prediction method

A prediction method and technology of complexity, applied in the directions of diagnostic recording/measurement, medical science, diagnosis using light, etc., can solve the problems of inability to correspond to the brain network status, and many factors affecting the brain network status, so as to achieve convenient operation and ensure reliability. Sex, the effect of simple testing process

Active Publication Date: 2019-12-10
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

However, there are many factors affecting the brain network status, and it is not possible to directly correspond the brain network status to age.

Method used

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  • Brain signal complexity based individual age prediction method
  • Brain signal complexity based individual age prediction method

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Embodiment 1

[0019] One embodiment of the present invention provides a method for predicting individual age based on brain signal complexity, including:

[0020] 1) Obtain the transmitted light intensity data of the brain tissue of the subject through the near-infrared imaging system;

[0021] 2) Convert the transmitted light intensity data into brain physiological information data, and calculate multi-scale entropy based on the brain physiological information data;

[0022] 3) Calculate the multi-scale entropy value corresponding to each detection point in the brain area of ​​each subject, and then calculate the corresponding multi-scale entropy value of each brain function network according to the position of each detection point, and then represent all The multi-scale entropy values ​​of the test points are averaged to obtain the multi-scale entropy index based on the brain function network;

[0023] 4) Use support vector regression to predict age: the multiscale entropy index of each ...

Embodiment 2

[0039] This embodiment further illustrates the technical solution of the present invention by enumerating specific experimental data. Specific steps are as follows:

[0040] Near-infrared imaging equipment with wavelengths of 670nm and 830nm was used to collect the resting-state near-infrared imaging light intensity data of 107 healthy subjects, and the subjects were divided into the following three groups according to age: 6-7 years old, 8-10 years old and 11-13 years old, then convert the infrared imaging light intensity data to obtain HbO concentration data, then remove motion artifacts and physiological noise and perform band-pass filter processing, and use the processed HbO concentration data to calculate the multi-scale corresponding to each test point entropy value.

[0041] The time series of sample entropy values ​​of each group of subjects is as follows: figure 1 As shown, the entropy value of each group of samples increases with time; for each time scale, the mult...

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Abstract

The invention relates to a brain signal complexity based individual age prediction method which comprises the following steps of acquiring transmission light intensity data of a brain tissue of a testee through a near-infrared imaging system; converting the transmission light intensity data to brain physiological information data, and calculating a multi-scale entropy based on the brain physiological information data; calculating a multi-scale entropy value corresponding to each detection point in a brain region of each testee, classifying the test points according to various brain function networks, and averaging the multi-scale entropy values for representating all the test points in one brain function network to obtain a brain function network based multi-scale entropy index; and takingthe multi-scale entropy index of each brain function network as the input feature of a support vector regression method, and using a linear kernel function to obtain a correlation equation of the brain signal complexity and an age. The method can accurately predict the age of the testee according to the complexity of a resting state brain signal as well as is simple in testing process and convenient to operate.

Description

technical field [0001] The invention relates to a method for predicting individual age based on brain signal complexity, and belongs to the field of brain function testing. Background technique [0002] The speed of human brain information processing ability is mainly closely related to the state of brain functional network. The state of brain functional network is mainly characterized by the complexity of brain signals. Brain signal complexity is an indicator that acts on time series. A time series can be processed through a specific complexity algorithm to obtain a complexity value, which represents the degree of irregularity of the sequence. Generally, the irregularity of the sequence changes The complexity is high, and the complexity of sequences with relatively simple patterns such as regular sequences and periodic sequences is low. Brain signal complexity is usually calculated using standard deviation and multiscale entropy (MSE) methods. Among them, the standard de...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/1455
CPCA61B5/0059A61B5/14553A61B5/4064A61B5/7246A61B5/7267A61B5/7275A61B2503/06
Inventor 牛海晶胡振燕
Owner BEIJING NORMAL UNIVERSITY
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