计算机与现代化Issue(3)：60-65,6.DOI:10.3969/j.issn.1006-2475.2025.03.009
基于多尺度动态卷积与注意力机制的抑郁脑电信号分类方法
Classification Method of EEG Signals for Depression Based on Multi-Scale Dynamic Convolution and Attention Mechanism
摘要
Abstract
Depression is a serious mental disorder that negatively affects the patient's quality of life and social functioning.In or-der to explore an electroencephalogram-based classification method for depression to improve the accuracy of early diagnosis of depression,this paper designes a deep learning model called MDATCNet,which exploits a multi-scale dynamic convolution module capturing the rich features of signals in both spatial and frequency dimensions.To further enhance the representation of the model,this paper integrates the multi-head self-attention mechanism,which allows the model to adaptively focus on the fea-tures that are most helpful for decision-making.Then,the time convolutional layer is responsible for mining the time series pat-terns in the time series data.Finally,the features are passed to a Softmax classifier to classify EEG signals.The feasibility of the model is evaluated on the public depression dataset using the ten-fold cross-validation method,and the recognition accuracy,sensitivity and specificity of the method based on MDATCNet in EEG can achieve 94.71%,99.37%,and 90.34%,respectively,and the experimental results show that the proposed model can effectively help the early diagnosis of depression.关键词
脑电图/抑郁症/深度学习/卷积神经网络/注意力机制Key words
electroencephalogram/depression/deep learning/convolutional neural network/attention mechanisms分类
信息技术与安全科学引用本文复制引用
李浩然,何文雪,徐嘉振,杨帮华..基于多尺度动态卷积与注意力机制的抑郁脑电信号分类方法[J].计算机与现代化,2025,(3):60-65,6.基金项目
国家自然科学基金资助项目(62376149) （62376149）
