[1]王建尚,张冰涛,王小敏,等.基于频空融合与3D-CNN-Attention的抑郁症识别[J].中国医学物理学杂志,2024,41(10):1307-1314.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.016]
　WANG Jianshang,ZHANG Bingtao,WANG Xiaomin,et al.Depression recognition based on frequency-space domain fusion and 3D-CNN-Attention[J].Chinese Journal of Medical Physics,2024,41(10):1307-1314.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.016]
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基于频空融合与3D-CNN-Attention的抑郁症识别()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第10期
页码:
1307-1314
栏目:
医学人工智能
出版日期:
2024-10-25

文章信息/Info

Title:
Depression recognition based on frequency-space domain fusion and 3D-CNN-Attention
文章编号:
1005-202X（2024）10-1307-08
作者:
王建尚; 张冰涛; 王小敏; 严大川
兰州交通大学电子与信息工程学院， 甘肃 兰州730070
Author(s):
WANG Jianshang;  ZHANG Bingtao;  WANG Xiaomin;  YAN Dachuan
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
关键词:
抑郁症; EEG; 频谱; 脑功能网络; 3D-CNN-Attention
Keywords:
Keywords: depressive disorder electroencephalogram frequency spectrum brain functional network 3D-CNN-Attention
分类号:
R318；TP399
DOI:
DOI:10.3969/j.issn.1005-202X.2024.10.016
文献标志码:
A
摘要:
提出了一种基于频谱信息的三维特征构建方法，根据电极位置将每个通道的功率值排列成二维特征向量。将不同频段特征排列成三维积分特征张量，提取频域信息，同时，为了减少容积导体效应影响，利用功能连接将时序脑电（EEG）数据映射到空间脑功能网络，提取空间信息。通过对特征与目标类之间关系的分析，提出一种3D-CNN-Attention网络模型，在3D-CNN网络中加入Attention机制，以增强EEG特征学习能力。在公开数据集上的系列对比实验，结果表明基于3D-CNN-Attention网络框架的抑郁症检测性能优于其他方法，获得了最高为96.32%的准确率。本文方法能够为抑郁症检测提供一种有效的解决方案。
Abstract:
Abstract: A three-dimensional feature construction method based on spectral information is presented, in which the power values of each channel are arranged into two-dimensional feature vectors based on electrode positions. The different frequency band features are arranged into a three-dimensional integral feature tensor to extract the information in frequency domain. Meanwhile, in order to reduce the influence of volume conductor effect, functional connectivity is utilized to map the temporal electroencephalogram data to the spatial brain functional network for extracting the spatial information. By analyzing the relationship between features and target classes, a 3D-CNN-Attention network model is proposed to incorporate an Attention mechanism in 3D-CNN network to enhance the electroencephalogram feature learning capability. A series of comparative experiments on publicly available datasets show that 3D-CNN-Attention network framework outperforms other methods in depression detection, obtaining an accuracy rate of up to 96.32%. The proposed method provides an effective solution for depression detection.

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备注/Memo

备注/Memo:
【收稿日期】2024-05-10 【基金项目】国家自然科学基金（62362047，61962034） 【作者简介】王建尚，硕士研究生，研究方向：脑功能网络，E-mail:1663465993@qq.com 【通信作者】张冰涛，博士，研究方向：人工智能与医学信号处理，E-mail: zhangbingtao321@163.com
更新日期/Last Update: 2024-10-29