Electroencephalogram-Based Major Depressive Disorder Classification Using Convolutional Neural Network and Transfer Learning

Year 2023, Volume: 18 Issue: 1, 207 - 214, 29.03.2023

Şuheda Kaya Burak Tasci

https://doi.org/10.55525/tjst.1242881

Cited By: 1

Abstract

Major Depressive Disorder (MDD) is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65% for Channel 1, 87.00% for Channel 2, and 86.94% for Channel 3.

Keywords

Major Depressive Disorder, EEG, ReliefF, kNN

References

• American Psychiatric Association A, Association AP. Diagnostic and statistical manual of mental disorders: DSM-5: Washington, DC: American psychiatric association; 2013.
• Han K-M, De Berardis D, Fornaro M, Kim Y-K. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuro-Psychopharmacol Biol Psychiatry 2019; 91:20-7.
• Mumtaz W, Malik AS, Yasin MAM, Xia L. Review on EEG and ERP predictive biomarkers for major depressive disorder. Biomed Signal Process Control 2015; 22:85-98.
• Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A. Computer-aided diagnosis of depression using EEG signals Eur Neurol 2015; 73:329-36.
• Mahato S, Paul S. Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): a review. Nanoelectronics, Circuits and Communication Systems. 2019; 323-35.
• Liao S-C, Wu C-T, Huang H-C, Cheng W-T, Liu Y-H. Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors (Basel) 2017; 17:1385.
• Mumtaz W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS. Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed Signal Process Control 2017; 31:108-15.
• Sharma G, Parashar A, Joshi AM. DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression. Biomed Signal Process Control 2021; 66:102393.
• Seal A, Bajpai R, Agnihotri J, Yazidi A, Herrera-Viedma E, Krejcar O. DeprNet: A deep convolution neural network framework for detecting depression using EEG. IEEE Trans Instrum Meas 2021; 70:1-13.
• Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: A convolutional neural network and long short-term memory approach. Cognit Neurodyn 2021; 15:239-52.
• Čukić M, Stokić M, Simić S, Pokrajac D. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cognit Neurodyn 2020; 14:443-55.
• Mumtaz W, Qayyum A. A deep learning framework for automatic diagnosis of unipolar depression.. Int J Med Inf 2019; 132:103983.
• Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, et al. Major depressive disorder classification based on different convolutional neural network models: Deep learning approach. Clin EEG Neurosci 2021; 52:38-51.
• Khan DM, Yahya N, Kamel N, Faye I. Automated diagnosis of major depressive disorder using brain effective connectivity and 3D convolutional neural network. IEEE Access. 2021; 9:8835-46.
• Tasci G, Loh HW, Barua PD, Baygin M, Tasci B, Dogan S, et al. Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals. Knowl Based Syst 2023; 260:110190.
• Wang B, Kang Y, Huo D, Chen D, Song W, Zhang F. Depression signal correlation identification from different EEG channels based on CNN feature extraction. Psychiatry Res Neuroimaging 2023; 328:111582.
• Cai H, Gao Y, Sun S, Li N, Tian F, Xiao H, et al. Modma dataset: a multi-modal open dataset for mental-disorder analysis. arXiv preprint arXiv:200209283. 2020.
• De Ryck T, De Vos M, Bertrand A. Change point detection in time series data using autoencoders with a time-invariant representation. IEEE Trans Signal Process 2021; 69:3513-24.
• Mustafa M, Taib MN, Murat ZH, Sulaiman N, Aris SAM. The analysis of eeg spectrogram image for brainwave balancing application using ann. 2011 UkSim 13th International Conference on Computer Modelling and Simulation: IEEE; 2011. 64-8.
• He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016; 770-8.
• Kira K, Rendell LA. A practical approach to feature selection. Machine learning proceedings 1992: Elsevier; 1992. 249-56.
• Kononenko I. Estimating attributes: Analysis and extensions of RELIEF. European conference on machine learning: Springer; 1994; 171-82.
• Peterson LE. K-nearest neighbor. Scholarpedia 2009; 4:1883.
• Vapnik V. The support vector method of function estimation. Nonlinear modeling: Springer; 1998; p. 55-85.
• Rish I. An empirical study of the naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence 2001; 41-6.
• Yegnanarayana B. Artificial neural networks: PHI Learning Pvt. Ltd.; 2009.
• Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M. Logistic regression: Springer; 2002.
• Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 1991; 21:660-74.
• Soni S, Seal A, Yazidi A, Krejcar O. Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression. Comput Biol Med 2022; 145:105420.