传感技术学报2024，Vol.37Issue(4)：658-664,7.DOI:10.3969/j.issn.1004-1699.2024.04.014
基于特征融合与注意力机制的CNN抑郁症识别
Depression Recognition with CNN Based on Feature Fusion and Attention Mechanism
尚照岩 1乔晓艳1
作者信息
- 1. 山西大学物理电子工程学院,山西 太原 030006
- 折叠
摘要
Abstract
It is of great practical significance to quickly and accurately identify,screen and early warn mild depression.By using EEG data and deep learning algorithm mental and psychological diseases can be machine-identified.A convolutional neural network(CNN)model based on feature fusion to effectively recognize depression.The attention mechanism is introduced into the CNN model to extract efficient spatio-temporal feature maps,enhance feature diversity and reduce the impact of individual differences.The results show that the average recognition accuracy of the model for depression reaches(99.39±0.14)%using EEG gamma rhythm.In addition,through the visual analysis of the convolutional layer feature map,the EEG differential electrodes of depression and normal subjects are ob-tained,and the depression classified with few electrodes,with the recognition accuracy of(91.41±1.11)%,showing that the deep learning model can effectively identify and screen mild depression.关键词
机器学习/抑郁症识别/卷积神经网络/注意力机制/特征融合Key words
machine learning/depression recognition/convolution neural network/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
尚照岩,乔晓艳..基于特征融合与注意力机制的CNN抑郁症识别[J].传感技术学报,2024,37(4):658-664,7.
