EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal Feature

Electroencephalography (EEG) have been proved to be effective in the field of depression recognition, however, the application of EEG-based mild depression detection is still in its infancy. Our work mainly focused on mild depression recognition of college students, based on high-density 128-channel EEG recordings from 24 mild depression individuals and 24 normal subjects using facial expression as experimental materials. In order to prevent individual performance differences on the convolution kernel, and to integrate time information and spatial information instead of simply combining, we proposed a new deep learning model with multiple convolution kernels and a Long Short-Term Memory (LSTM) strategy based on convolution. Batch normalization has been widely used and proved to be effective in some research areas, for example computer vision. Our findings show that for EEG data, batch normalization will reduce the accuracy due to the special data characteristics of EEG. It was found that the proposed model achieved an accuracy of 83.47% with the 8-fold cross-validation, and Batch Normalization will reduce the accuracy because it eliminated the difference between depression and normal. Our findings cast a new light to recognize mild depression accurately and quickly, it could be used as auxiliary tools to diagnose and predict mild depression in the future.

[1]  Subha D. Puthankattil,et al.  CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY , 2012 .

[2]  Joel E. W. Koh,et al.  A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals , 2015, European Neurology.

[3]  WangGang,et al.  Recent advances in convolutional neural networks , 2018 .

[4]  Reda A. El-Khoribi,et al.  EEG-Based Emotion Recognition using 3D Convolutional Neural Networks , 2018 .

[5]  D. Hu,et al.  Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. , 2012, Brain : a journal of neurology.

[6]  U. Raghavendra,et al.  A deep learning approach for Parkinson’s disease diagnosis from EEG signals , 2018, Neural Computing and Applications.

[7]  Gro Harlem Brundtland,et al.  Mental Health: New Understanding, New Hope , 2001 .

[8]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[9]  Jeffrey G Ojemann,et al.  Comparing Noninvasive Dense Array and Intracranial Electroencephalography for Localization of Seizures , 2010, Neurosurgery.

[10]  K. Peltzer,et al.  Depression and Associated Factors Among University Students in Western Nigeria , 2013 .

[11]  U. Rajendra Acharya,et al.  Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network , 2017, J. Comput. Sci..

[12]  Zhijiang Wan,et al.  HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination , 2020, IEEE Access.

[13]  S.Dhananjay Kumar,et al.  Prediction of Depression from EEG Signal Using Long Short Term Memory(LSTM) , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

[14]  D. Tucker,et al.  Scalp electrode impedance, infection risk, and EEG data quality , 2001, Clinical Neurophysiology.

[15]  Shuai Li,et al.  RNN Models for Dynamic Matrix Inversion: A Control-Theoretical Perspective , 2018, IEEE Transactions on Industrial Informatics.

[16]  R. Schiffer,et al.  Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG , 2001, Clinical Neurophysiology.

[17]  C. Glazebrook,et al.  Analysis of an Egyptian study on the socioeconomic distribution of depressive symptoms among undergraduates , 2012, Social Psychiatry and Psychiatric Epidemiology.

[18]  Yang Yang,et al.  The functional architectures of addition and subtraction: Network discovery using fMRI and DCM , 2017, Human brain mapping.

[19]  Sanchita Paul,et al.  Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry , 2019, Journal of Medical Systems.

[20]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[21]  HuBin,et al.  EEG-based mild depressive detection using feature selection methods and classifiers , 2016 .

[22]  K. Peltzer,et al.  Depression among university students in Kenya: prevalence and sociodemographic correlates. , 2014, Journal of affective disorders.

[23]  Aamir Saeed Malik,et al.  A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD) , 2018, Medical & Biological Engineering & Computing.

[24]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[25]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[26]  Juri D. Kropotov,et al.  Independent component approach to the analysis of EEG recordings at early stages of depressive disorders , 2010, Clinical Neurophysiology.

[27]  Bin Hu,et al.  EEG-based mild depressive detection using feature selection methods and classifiers , 2016, Comput. Methods Programs Biomed..

[28]  C. Adams,et al.  A systematic review of studies of depression prevalence in university students. , 2013, Journal of psychiatric research.

[29]  K. Oppong Asante,et al.  Prevalence and determinants of depressive symptoms among university students in Ghana. , 2015, Journal of affective disorders.

[30]  Luo Yuejia,et al.  Revision of the Chinese Facial Affective Picture System , 2011 .

[31]  Bin Hu,et al.  Feature-level fusion approaches based on multimodal EEG data for depression recognition , 2020, Inf. Fusion.

[32]  Lei Guo,et al.  Deep Learning in Edge of Vehicles: Exploring Trirelationship for Data Transmission , 2019, IEEE Transactions on Industrial Informatics.

[33]  U. Rajendra Acharya,et al.  An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals , 2018, Cognitive Systems Research.

[34]  A. Beck,et al.  Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. , 1996, Journal of personality assessment.

[35]  Ning Wang,et al.  HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification , 2020, Journal of neural engineering.

[36]  Alan D. Lopez,et al.  Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study , 1997, The Lancet.

[37]  Jing Zhu,et al.  Depression recognition using machine learning methods with different feature generation strategies , 2019, Artif. Intell. Medicine.

[38]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[39]  Jing Zhu,et al.  Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  U. Rajendra Acharya,et al.  Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals , 2019, Journal of Medical Systems.