Non-invasive EEG based Feature Extraction framework for Major Depressive Disorder analysis

Nayab Bashir; Sanam Narejo; Bushra Naz; Mohammad Moazzam Jawed; Shahnawaz Talpur; Khurshid Aliev

Authors

Nayab Bashir Department of Biomedical Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
Sanam Narejo Department of Computer Systems, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
Bushra Naz Department of Computer Systems, Mehran University of Engineering and Technology, Jamshoro, Pakistan
Mohammad Moazzam Jawed Department of Computer Systems, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
Shahnawaz Talpur Department of Computer Systems, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
Khurshid Aliev Department of Electronics and Telecommunication, Politecnico Di Torino, Italy

Keywords:

Major Depressive Disorder, electroencephalogram, K-Nearest Neighbors, Support Vector Machine, Convolutional neural Network, Power Spectral Density, Differential Entropy.

Abstract

Depression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (SVM) had acquired 95.7% accuracy. The developed Deep Learning approach, convolution neural network (CNN), achieved 99.6% accuracy. With these promising results, this study establishes the viability of an Electroencephalogram based diagnosis of MDD.

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References

Mathers, C. D., & Loncar, D. (2006). Projections of global mortality and burden of disease from 2002 to 2030. PLoS medicine, 3(11), e442.

World Federation for Mental Health, Depression: a global crisis, Occoquan, VA, USA, (2012).

World Health Organization, Depression, obtained from http://www.who.int/ mediacentre/factsheets/fs369/en/, 2017.

Alhaj H, Wisniewski G, McAllister-Williams RH (2011) the use of the EEG in measuring therapeutic drug action: focus on depression and antidepressants. J Psychopharmacol 25:1175–1191

Mumtaz W, Malik AS, Yasin MAM, Xia L (2015) Review on EEG and ERP predictive biomarkers for major depressive disorder. Biomed Signal Process Control 22:85–98

Mumtaz W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS (2017) Electroencephalogram (EEG)-based computer-aided techniqueto diagnose major depressive disorder (MDD). Biomed Signal Process Control 31:108–115

Olbrich S, Arns M (2013) EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response. Int Rev Psychiatry 25:604–618

Narejo S, Pasero E, Kulsoom F. EEG based eye state classification using deep belief network and stacked autoencoder. International Journal of Electrical and Computer Engineering (IJECE). 2016 Dec 1;6(6):3131-41.

Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Puthankatti SD et al (2015) A novel depression diagnosis index using nonlinear features in EEG signals. Eur Neurol 74:79–83

A. Bashashati, M. Fatourechi, R. K. Ward, and G. E. Birch, “A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals,” Journal of Neural Engineering, vol. 4, no. 2, pp. R32–R57, 2007.

N. Brodu, F. Lotte, and A. Lécuyer, “Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity,” Neurocomputing, vol. 79, pp. 87–94, 2012.

Berman MG, Peltier S, Nee DE, Kross E, Deldin PJ, Jonides J. Depression, rumination and the default network. Soc Cogn Affect Neurosci 2011 Oct;6(5):548-555

Faust O, Ang PC, Puthankattil SD, Joseph PK. Depression diagnosis support system based on EEG signal entropies. J Mech Med Biol 2014 Mar 13;14(03):1450035.

Liao S, Wu C, Huang H, Cheng W, Liu Y. Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors (Basel) 2017 Jun 14;17(6):1-18

[Mumtaz W, Xia L, Ali SS, Yasin MA, Hussain M, Malik AS. Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomedical Signal Processing and Control 2017 Jan;31:108-115.

Mumtaz W, Xia L, Mohd Yasin MA, Azhar Ali SS, Malik AS. A wavelet-based technique to predict treatment outcome for major depressive disorder. PLoS One 2017;12(2):e0171409

Mumtaz W, Ali SS, Yasin MA, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput 2018 Feb;56(2):233-246.

Bachmann M, Lass J, Suhhova A, Hinrikus H. Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram. Comput Math Methods Med 2013;2013:251638

Mohammadi M, Al-Azab F, Raahemi B, Richards G, Jaworska N, Smith D, et al. Data mining EEG signals in depression for their diagnostic value. BMC Med Inform Decis Mak 2015 Dec 23;15:108

Čukić M, Pokrajac D, Stokić M, Simić S, Radivojević V, Ljubisavljević M. EEG machine learning with Higuchi’s fractal dimension and Sample Entropy as features for successful detection of depression. arXiv 2018

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. Cogn Neurodyn 2020 Aug;14(4):443-455.

Mumtaz W, Xia LK, Ali SSA, Yasin MAM, Hussain M, Malik AS Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD) (2017).

Qin Y, Xu P, Yao D ,A comparative study of different references for EEG default mode network: the use of the infinity reference. Clin Neurophysiol 121:1981–1991 (2010).

N. Brodu, F. Lotte, and A. Lécuyer, “Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity,” Neurocomputing, vol. 79, pp. 87–94, 2012.

C. Mosquera, A. M. Trigueros, J. I. Franco, and Á. Navia-Vázquez, “New feature extraction approach for epileptic EEG signal detection using time-frequency distributions,” Medical & Biological Engineering & Computing, vol. 48, no. 4, pp. 321–330, 2010.

L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, “Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier,” Computer Methods & Programs in Biomedicine, vol. 108, no. 1, pp. 10–19, 2012.

S. Deivanayagi, M. Manivannan, and P. Fernandez, “Spectral analysis of EEG signals during hypnosis,” International Journal of Systemics, Cybernetics and Informatics, pp. 75–80, 2007.

A. Subasi, E. Erçelebi, A. Alkan, and E. Koklukaya, “Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection,” Computers in Biology and Medicine, vol. 36, no. 2, pp. 195–208, 2006.

M. Naji, M. Firoozabadi, and P. Azadfallah, “Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram,” Cognitive Computation, vol. 6, no. 2, pp. 241–252, 2014.

L. Duan, H. Zhong, J. Miao, Z. Yang, W. Ma, and X. Zhang, “A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data,” Cognitive Computation, vol. 6, no. 3, pp. 477–483, 2014.

K. Kirschfeld, “The physical basis of alpha waves in the electroencephalogram and the origin of the "berger effect",” Biological Cybernetics, vol. 92, no. 3, pp. 177–185, 2005

R. Grave De Peralta Menendez, Q. Noirhomme, F. Cincotti, D. Mattia, F. Aloise, and S. González Andino, “Modern electrophysiological methods for brain-computer interfaces,” Computational Intelligence and Neuroscience, vol. 2007, Article ID 56986, 8 pages, 2007.

A. Webb, Statistical Pattern Recognition, Oxford University Press Inc, New York, 1999

Jolliffe JL (2002) Principal component analysis, 2nd edn. Springer, New York, pp 10–150

Furqan, M., Rajput, H., Narejo, S., Ashraf, A. and Awan, K., Heart Disease Prediction using Machine Learning Algorithms. Second International Conference on Computational Science And Technology (INCCST) 2020