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Multi-parameter mental stress evaluation method based on BP neural network algorithm

A BP neural network, psychological stress technology, applied in biological neural network models, psychological devices, electrical digital data processing, etc., can solve the problem that the weight is not easy to determine, the calculation process of eigenvectors and eigenvalues ​​is complicated, and the judgment matrix is ​​highly subjective. and other problems, to achieve the effect of small mean square error and improved accuracy

Inactive Publication Date: 2017-10-10
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

The existing algorithm for objectively evaluating mental stress is Analytic Hierarchy Process, which has defects such as relatively complicated calculation process of eigenvectors and eigenvalues, difficult determination of weights, and strong subjectivity of judgment matrix.

Method used

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  • Multi-parameter mental stress evaluation method based on BP neural network algorithm
  • Multi-parameter mental stress evaluation method based on BP neural network algorithm
  • Multi-parameter mental stress evaluation method based on BP neural network algorithm

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Embodiment Construction

[0033] The BP neural network algorithm includes an input layer, a hidden layer and an output layer. Firstly, the structure of the BP neural network, the number of hidden layers and the number of output samples are determined. After determining the structure of the neural network, it is necessary to train the network through input samples and output samples, and to learn and modify the threshold and weight of the network, so that the network can realize a given input-output mapping relationship.

[0034] The multi-parameter psychological stress assessment method based on BP neural network algorithm provided by the present embodiment comprises the following steps:

[0035] Step 1: Obtain the HRV signal of the subject to be tested, and perform frequency domain, time domain and nonlinear analysis on the HRV signal respectively to obtain the input vector G={G 1 ,G 2 ,G 3}, the expected output vector is the anxiety value D;

[0036] Among them, G 1 ={G 11 ,G 12} is the HRV sig...

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Abstract

The invention discloses a multi-parameter mental stress evaluation method based on a BP neural network algorithm. The method comprises the steps that an HRV signal of a person to be tested is subjected to frequency domain, time domain and nonlinear analysis to obtain mental stress influence factor parameter set, according to the parameter set, the BP learning algorithm is adopted, an error function is adopted for learning according to a gradient descent method, the mean square error between an actual output value and an expected output value of a network is made to be the minimum, and an accurate mental stress evaluation result of the person to be tested is obtained. By adopting changes of HRV physiological parameters of the person to be tested, the mental stress state of the person to be tested is monitored, and the influence caused by different subjective factors and cognitive levels of the person to be tested on the monitoring result is effectively avoided; the mental stress evaluation accuracy and reasonability of the person to be tested are effectively improved.

Description

technical field [0001] The invention relates to the field of medical psychological pressure measurement and evaluation, in particular to a multi-parameter psychological pressure evaluation method based on BP neural network algorithm. Background technique [0002] In today's diversified information age, the vast majority of people are more or less faced with various pressures, and being in this state for a long time often leads to various mental illnesses, which not only endanger the physical and mental health of individuals, It will also increase the burden on society. [0003] The assessment of stressful situations today is often inseparable from experienced medical staff. The existing algorithm for objectively evaluating mental stress is Analytic Hierarchy Process, which has defects such as relatively complicated calculation process of eigenvectors and eigenvalues, difficult determination of weights, and strong subjectivity of judgment matrix. Contents of the invention ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/16G06F19/00G06N3/02
CPCA61B5/165A61B5/7264G06N3/02
Inventor 郭乔陈德民斯星童席静
Owner CHONGQING UNIV OF POSTS & TELECOMM
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