3DMKDR:3D Multiscale Kernels CNN Model for Depression Recognition Based on EEG

作     者：Yun Su Zhixuan Zhang Qi Cai Bingtao Zhang Xiaohong Li Yun Su;Zhixuan Zhang;Qi Cai;Bingtao Zhang;Xiaohong Li

作者机构：School of Computer Science and EngineeringNorthwest Normal UniversityLanzhou 730070China School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhou 730070China 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2023年第32卷第2期

页      面：230-241页

核心收录：

学科分类：12[管理学] 07[理学] 08[工学] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 100205[医学-精神病与精神卫生学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.61862058,61962034,and 8226070356) in part by the Gansu Provincial Science&Technology Department(No.20JR10RA076) 

主　　题：major depression disorder(MDD) electroencephalogram(EEG) three-dimensional convolutional neural network(3D-CNN) spatiotemporal features 

摘      要：Depression has become a major health threat around the world,especially for older people,so the effective detection method for depression is a great public health ***(EEG)can be used as a biomarker to effectively explore depression *** by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel,this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition(3DMKDR),which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals.A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode *** the major depressive disorder(MDD)and the multi-modal open dataset for mental-disorder analysis(MODMA)datasets,the experiment shows that the accuracies of depression recognition are up to99.86%and 98.01%in the subject-dependent experiment,and 95.80%and 82.27%in the subjectindependent experiment,which are higher than alternative competitive *** experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.