Severe depression identification system based on brain-computer interface and deep learning and application

Abstract

The invention discloses a severe depression identification system based on a brain-computer interface and deep learning and application. The system comprises a portable electroencephalogram acquisition device, an electroencephalogram analysis system and a classification and recognition system, the portable electroencephalogram acquisition device acquires EEG electroencephalogram signals from the brain of a testee. After the acquisition processes, the electroencephalogram analysis system is used for analyzing the acquired EEG electroencephalogram signals and establishes a multi-layer brain complex network. The classification and recognition system accurately recognizes the brain state on the basis of a multi-layer brain complex network, and completes the recognition process. The system gives out the possibility that a testee belongs to a normal person or severe depression through the collection process and the recognition process for specified times, and then completes the recognition task of a patient suffering from severe depression. According to the system, relatively high severe depression identification accuracy can be achieved, a system can rapidly and accurately carry out preliminary identification on a patient suffering from severe depression, and a basis is further provided for later diagnosis.

Description

technical field [0001] The invention relates to a system for identifying severe depression. In particular, it involves a severe depression identification system and application based on brain-computer interface and deep learning. Background technique [0002] Major depressive disorder is a global, serious mental illness. Unlike mild mood swings, major depressive disorder carries a higher risk of serious health problems. Studies have shown that the prevalence of major depressive disorder in Chinese children and adolescents is about 1.3%, which has brought a huge burden to adolescent mental health work. In addition, suicide rates were significantly higher in patients with major depressive disorder than in healthy individuals. Therefore, accurate and timely identification of patients with severe depression is of great significance to scientific research and the development of human society. At present, more and more attention has been paid to the identification of patients ...

AI Technical Summary

Problems solved by technology

[0004] The brain is a complex system, and the EEG signal has the characteristics of nonlinearity and unsteady state, so it is difficult to capture its effective characteristic information by using traditional signal processing theory

Images