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Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

Zhijiang Wan

Corresponding author.

Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0864, Japan

E-mail Address: [email protected]

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Hao Zhang

College of Economics and Management, Nanjing Forestry University, Nanjing Jiangsu 210037, P. R. China

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Jiajin Huang

College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, P. R. China

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Haiyan Zhou

College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, P. R. China

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Jie Yang

Beijing Anding Hospital of Capital Medical University, Beijing 100088, P. R. China

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and

Ning Zhong

Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0864, Japan

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https://doi.org/10.1142/S0219622019500342Cited by:5

Abstract

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

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References

1. R. H. Belmaker and G. Agam , Major depressive disorder, New England Journal of Medicine 358(1) (2008) 55–68, https://doi.org/10.1056/NEJMra073096. Crossref, Google Scholar
2. W. H. Organization, Depression (Fact Sheet N. 369) (2012), www.who.int/news-room/fact-sheets/detail/depression. Google Scholar
3. R. Kerestes, C. G. Davey, K. Stephanou, S. Whittle and B. J. Harrison , Functional brain imaging studies of youth depression: A systematic review, NeuroImage Clinical 4 (2014) 209–231, https://doi.org/10.1016/j.nicl.2013.11.009. Crossref, Google Scholar
4. O. van der Stelt and A. Belger , Application of electroencephalography to the study of cognitive and brain functions in schizophrenia, Schizophrenia Bulletin 33(4) (2007) 955–970, https://doi.org/10.1093/schbul/sbm016. Crossref, Google Scholar
5. S. Olbrich and M. Arns , EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response, International Review of Psychiatry 25(5) (2013) 604–618, https://doi.org/10.3109/09540261.2013.816269. Crossref, Google Scholar
6. Brainclinics. Depression history of EEG and QEEG findings (2017), https://www.brainclinics.com/depresssion-history-of-eeg-researchx. Google Scholar
7. M. Mohammadi, F. Al-Azab, B. Raahemi, G. Richards, N. Jaworska, D. Smith, S. de la Salle, P. Blier and V. Knott , Data mining EEG signals in depression for their diagnostic value, BMC Medical Informatics and Decision Making 15(1) (2015), https://doi.org/10.1186/s12911-015-0227-6. Crossref, Google Scholar
8. M. Bairy, S. Bhat, L. Wei Jie Eugene, U. Niranjan, S. D. Puthankattil and P. Joseph , Automated classification of depression electroencephalographic signals using discrete cosine transform and nonlinear dynamics, Journal of Medical Imaging and Health Informatics 5(3) (2015) 635–640, https://doi.org/10.1166/jmihi.2015.1418. Crossref, ISI, Google Scholar
9. B. Hosseinifard, M. Moradi and R. Rostami , Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal, Computer Methods and Programs in Biomedicine 109(3) (2012) 339–345. Crossref, Google Scholar
10. S. D. Puthankattil and P. K. Joseph , Analysis of EEG signals using wavelet entropy and approximate entropy: A case study on depression patients, International Journal of Medical, Health, Biomedical, Bioengineering 8(7) (2014) 420–424. Google Scholar
11. S. D. Puthankattil and P. K. Joseph , Classification of eeg signals in normal and depression conditions by ann using rwe and signal entropy, Journal of Mechanics in Medicine and Biology 12(4) (2012) 1240019, https://doi.org/10.1142/S0219519412400192. Link, ISI, Google Scholar
12. S. C. Liao, C. T. Wu, H. C. Huang et al., Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns, Sensors 17(6) (2017) 1385, https://doi.org/10.3390/s17061385. Crossref, Google Scholar
13. J. M. Rogers, S. J. Johnstone, A. Aminov, J. Donnelly and P. H. Wilson , Test-retest reliability of a single-channel, wireless EEG system, International Journal of Psychophysiology 106 (2016) 87–96, https://doi.org/10.1016/j.ijpsycho.2016.06.006. Crossref, Google Scholar
14. C. T. Lin, C. H. Chuang, Z. Cao, A. K. Singh, C. S. Hung, Y. H. Yu, M. Nascimben, Y. T. Liu, J. T. King and T. P. Su , Forehead EEG in support of future feasible personal healthcare solutions: Sleep management, headache prevention, and depression treatment, IEEE Access 5 (2017) 10612–10621, https://doi.org/10.1109/access.2017.2675884. Crossref, Google Scholar
15. H. Cai, J. Han, Y. Chen, X. Sha, Z. Wang, B. Hu, J. Yang, L. Feng, Z. Ding and Y. Chen , A pervasive approach to EEG-based depression detection, Complexity 2018 (5238028) (2018) 1–13, https://doi.org/10.1155/2018/5238028. Google Scholar
16. E. Walter and P. Dassonville , Activation in a frontoparietal cortical network underlies individual differences in the performance of an embedded figures task, Plos One 6(7) (2011) e20742, https://doi.org/10.1371/journal.pone.0020742. Crossref, Google Scholar
17. S. Tekin and J. L. Cummings , Frontal–subcortical neuronal circuits and clinical neuropsychiatry: An update, Journal of Psychosomatic Research 53(2) (2002) 647–654, https://doi.org/10.1016/s0022-3999(02)00428-2. Crossref, Google Scholar
18. V. Ljubojevic, P. Luu, P. X. Gill, L. A. Beckett et al., Cholinergic modulation of frontoparietal cortical network dynamics supporting supramodal attention, Journal of NeuroScience 38(16) (2018) 3988–4005, https://doi.org/10.1523/JNEUROSCI.2350-17.2018. Crossref, Google Scholar
19. J. I. Ekandem, I. D. Alvarez, M. T. James and J. E. Gilbert , Evaluating the ergonomics of BCI devices for research and experimentation, Ergonomics 55(5) (2012) 592–598, https://doi.org/10.1080/00140139.2012.662527. Crossref, Google Scholar
20. S. J. Johnstone, J. M. Blackman, R Fau - Bruggemann and J. M. Bruggemann , EEG from a single-channel dry-sensor recording device, Clinical EEG Neuroscience 43(2) (2012) 112–120, https://doi.org/10.1177/1550059411435857. Crossref, Google Scholar
21. Brain Products Company (2018), https://www.brainproducts.com/. Google Scholar
22. NeuroSky (2018), https://store.neurosky.com. Google Scholar
23. X. Li, B. Hu, S. Shuting and H. Cai , EEG-based mild depressive detection using feature selection methods and classifiers, Computer Methods and Programs in Biomedicine 136 (2016) 151–161, https://doi.org/10.1016/j.cmpb.2016.08.010. Crossref, Google Scholar
24. M. Bachmann et al., Methods for classifying depression in single channel eeg using linear and nonlinear signal analysis, Computer Methods and Programs in Biomedicine 155 (2018) 11–17, https://doi.org/10.1016/j.cmpb.2017.11.023. Crossref, Google Scholar
25. M. Ahmadlou, H. Adeli and A. Adeli , Fractality analysis of frontal brain in major depressive disorder, International Journal of Psychophysiology 85(2) (2012) 206–211, https://doi.org/10.1016/j.ijpsycho.2012.05.001. Crossref, ISI, Google Scholar
26. T. Tekin Erguzel, C. Tas and M. Cebi , A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders, Computers in Biology and Medicine 64 (2015) 127–137, https://doi.org/10.1016/j.compbiomed.2015.06.021. Crossref, Google Scholar
27. W. Mumtaz, L. Xia, M. A. Mohd Yasin, S. S. Azhar Ali and A. S. Malik , A wavelet-based technique to predict treatment outcome for major depressive disorder, Plos One 12(2) (2017) e0171409, https://doi.org/10.1371/journal.pone.0171409. Crossref, Google Scholar
28. S. Devaraj and P. Sathurappan , An efficient feature subset selection algorithm for classification of multidimensional dataset, The World Scientific Journal 2015 (2015) 1–9, https://doi.org/10.1155/2015/821798. Crossref, Google Scholar
29. S. Akdemir Akar, S. Kara, S. Agambayev and V. Bilgic , Nonlinear analysis of EEGs of patients with major depression during different emotional states, Computers in Biology and Medicine 67 (2015) 49–60, https://doi.org/10.1016/j.compbiomed.2015.09.019. Crossref, Google Scholar
30. T. Takahashi , Complexity of spontaneous brain activity in mental disorders, Progress in Neuro-Psychopharmacology and Biological Psychiatry 45 (2013) 258–266, https://doi.org/10.1016/j.pnpbp.2012.05.001. Crossref, ISI, Google Scholar
31. M. A. Mendez, R. Zuluaga, C. Hornero et al., Complexity analysis of spontaneous brain activity: Effects of depression and antidepressant treatment, Journal of Psychopharmacology 26(5) (2011) 636–643, https://doi.org/10.1177/0269881111408966. Crossref, Google Scholar
32. U. R. Acharya, V. K. Sudarshan, H. Adeli, J. Santhosh, J. E. W. Koh, S. D. Puthankatti and A. Adeli , A novel depression diagnosis index using nonlinear features in EEG signals, European Neurology 74(2) (2015) 79–83, https://doi.org/10.1159/000438457. Crossref, Google Scholar
33. G. Kou, X. Chao, Y. Peng, F. E. Alsaadi and E. Herrera-Viedma , Machine learning methods for systemic risk analysis in financial sectors, Technological and Economic Development of Economy (2019) 1–27, https://doi.org/10.3846/tede.2019.8740. Google Scholar
34. G. Kou, Y. Peng and G. Wang , Evaluation of clustering algorithms for financial risk analysis using MCDM methods, Information Sciences 275(11) (2014) 1–12, https://doi.org/10.1016/j.ins.2014.02.137. Crossref, ISI, Google Scholar
35. G. Kou, Y. Lu, Y. Peng et al., Evaluation of classification algorithms using mcdm and rank correlation, International Journal of Information Technology & Decision Making 11(1) (2012) 197–225, https://doi.org/10.1142/S0219622012500095. Link, ISI, Google Scholar
36. S. Manish et al., An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals, Cognitive Systems Research 52 (2018) 508–520, https://doi.org/10.1016/j.cogsys.2018.07.010. Crossref, Google Scholar
37. X. Zhang, B. Hu, L. Zhou, P. Moore and J. Chen , An EEG based pervasive depression detection for females, in Proc. 2012 Int. Conf. Pervasive Computing and the Networked World, Lecture Notes in Computer Science, Vol. 7719 (2012), pp. 848–861, https://doi.org/10.1007/978-3-642-37015-1_74. Google Scholar
38. U. R. Acharya, S. L. Oh, Y. Hagiwara et al., Automated EEG-based screening of depression using deep convolutional neural network, Computer Methods and Programs in Biomedicine 161 (2018) 103–113, https://doi.org/10.1016/j.cmpb.2018.04.012. Crossref, Google Scholar

Published: 18 September 2019

Vol. 18, No. 05

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Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

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References

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