Portable electroencephalogram depression detection system in combination with demographic attention mechanism

Abstract

The invention provides a portable electroencephalogram depression detection system in combination with a demographic attention mechanism. On one hand, the accuracy of electroencephalogram signal sequence learning and modeling is improved by using a convolutional neural network, and on the other hand, demographic information of individuals is introduced in combination with the attention mechanism,and more effective depressive disorder detection is realized. The system comprises an electroencephalogram data acquisition module, a data preprocessing module and a depressive disorder detection module, wherein the electroencephalogram data acquisition module is used for acquiring resting state electroencephalogram original data of a subject; the data preprocessing module is used for carrying outdata preprocessing on the collected original data; and the depressive disorder detection module is used for completing depressive disorder detection based on the electroencephalogram data after datapreprocessing, constructing and training a model by adopting an artificial neural network to classify electroencephalogram signals, and fusing the demographic information into a modeling process of the electroencephalogram signals by jointly using convolution operation and the attention mechanism.

Description

technical field [0001] The invention relates to a portable EEG depression detection system combined with a demographic attention mechanism, which belongs to the technical field of auxiliary medical treatment for depression. Background technique [0002] Depressive disorder is also known as depression. According to the latest statistical report of the World Health Organization (WHO), there are currently 322 million depressive disorder patients in the world. China is one of the countries with a serious burden of depressive disorders in the world. The results of the national epidemiological survey of mental disorders show that the lifetime prevalence rate of depression in my country is 6.8%, and the December prevalence rate is 3.6% (note: the lifetime prevalence rate refers to the proportion of patients who have suffered from depression in their lifetime. The rate of the total population, the 12-month prevalence refers to the rate of patients with depression within 12 months of...

AI Technical Summary

Problems solved by technology

[0006] (1) Only use EEG signals for depressive disorder detection, without introducing demographic and other influencing factors

Due to the significant impact of factors such as gender and age on the EEG signal, if the influence of individual differences is ignored during the construction of the detection model and the modeling is based solely on the EEG signal, the generalization performance of the model cannot be guaranteed.

[0007] (2) There are individual systems that combine EEG signals and demographic characteristics for depressive disorder detection, but only gender, age and other factors are input into the classification model together with EEG signal features as independent features, and EEG signals cannot be explored and learned. The complex relationship between signals and factors such as gender and age

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