Skip to main content
SpringerLink
Log in

Benchmarks for machine learning in depression discrimination using electroencephalography signals

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Diagnosis of depression using electroencephalography (EEG) is an emerging field of study. When mental health facilities are unavailable, the use of EEG as an objective measure for depression management at an individual level becomes necessary. However, the limited availability of the openly accessible EEG datasets for depression and the non-standard task paradigm confine the scope of the research. This study contributes to the area by presenting a dataset that includes EEG data of subjects in the resting state and Patient Health Questionnaire (PHQ)-9 scores. These recordings incorporate EEG signals under both eyes open (EO) and eyes closed (EC) conditions. Moreover, this work documents high performance on various benchmark depression classification tasks with the help of traditional supervised machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbours, Naive Bayes, Support Vector Machine, Multi-Layer Perceptron, and extreme gradient boosted trees (XGBoost) using the newly created dataset, where the class label of each patient is determined by the PHQ-9 score of the person. Then, feature selection is performed on twenty-three linear, nonlinear, time domain, and frequency domain features using ANOVA test and correlation analysis to identify statistically significant features, which are further fed into algorithms mentioned above separately for distinguishing healthy subjects from depressed. Among these classifiers, the performance of the XGBoost is found to be the best, with an accuracy of 87% for the EO state. The obtained results demonstrate that the proposed method outperforms fourteen existing approaches. The dataset presented in this work can be downloaded via https://drive.google.com/drive/folders/1ANUC-6hq02QG728ZWv2a1UWTLUbRrq_y?usp=sharing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Organization WH, et al. (2001) The world health report: mental disorders affect one in four people. In: The world health report: mental disorders affect one in four people

  2. Kircanski K, Joormann J, Gotlib IH (2012) Cognitive aspects of depression. Wiley Interdiscip Rev Cogn Sci 3(3):301–313

    Google Scholar 

  3. Shahin M, Mulaffer L, Penzel T, Ahmed B (2018) A two stage approach for the automatic detection of insomnia. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 466–469

  4. Álvarez-Estévez D, Moret-Bonillo V (2011) Identification of electroencephalographic arousals in multichannel sleep recordings. IEEE Trans Biomed Eng 58(1):54–63

    Google Scholar 

  5. Klados MA, Paraskevopoulos E, Pandria N, Bamidis PD (2019) The impact of math anxiety on working memory: a cortical activations and cortical functional connectivity eeg study. IEEE Access

  6. Adeli H, Ghosh-Dastidar S, Dadmehr N (2007) A wavelet-chaos methodology for analysis of eegs and eeg subbands to detect seizure and epilepsy. IEEE Trans Biomed Eng 54(2):205–211

    Google Scholar 

  7. Tahaei MS, Jalili M, Knyazeva MG (2012) Synchronizability of eeg-based functional networks in early alzheimer’s disease. IEEE Trans Neural Syst Rehabil Eng 20(5):636–641

    Google Scholar 

  8. Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, Yang J, Feng L, Ding Z, Chen Y et al (2018) A pervasive approach to eeg-based depression detection. Complexity 2018

  9. Soni S, Seal A, Yazidi A, Krejcar O (2022) Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression. Comput Biol Med 145:105420

    Google Scholar 

  10. Wade EC, Iosifescu DV (2016) Using electroencephalography for treatment guidance in major depressive disorder. Biol Psychiatry: Cogn Neurosci Neuroimaging 1(5):411–422

    Google Scholar 

  11. Čukić M, López V, Pavón J (2020) Classification of depression through resting-state electroencephalogram as a novel practice in psychiatry. J Med Internet Res 22(11):e19548

    Google Scholar 

  12. Roh S-C, Park E-J, Shim M, Lee S-H (2016) Eeg beta and low gamma power correlates with inattention in patients with major depressive disorder. J Affect Disord 204:124–130

    Google Scholar 

  13. Feldmann L, Piechaczek CE, Grünewald BD, Pehl V, Bartling J, Frey M, Schulte-Körne G, Greimel E (2018) Resting frontal eeg asymmetry in adolescents with major depression: impact of disease state and comorbid anxiety disorder. Clin Neurophysiol 129(12):2577–2585

    Google Scholar 

  14. de Aguiar Neto FS, Rosa JLG (2019) Depression biomarkers using non-invasive eeg: a review. Neurosci Biobehav Rev 105:83–93

    Google Scholar 

  15. Imperatori C, Farina B, Adenzato M, Valenti EM, Murgia C, Della Marca G, Brunetti R, Fontana E, Ardito RB (2019) Default mode network alterations in individuals with high-trait-anxiety: an eeg functional connectivity study. J Affect Disord 246:611–618

    Google Scholar 

  16. Lee PF, Kan DPX, Croarkin P, Phang CK, Doruk D (2018) Neurophysiological correlates of depressive symptoms in young adults: a quantitative eeg study. J Clin Neurosci 47:315–322

    Google Scholar 

  17. 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

    Google Scholar 

  18. Khodayari-Rostamabad A, Reilly JP, Hasey GM, de Bruin H, MacCrimmon DJ (2013) A machine learning approach using eeg data to predict response to ssri treatment for major depressive disorder. Clin Neurophysiol 124(10):1975–1985

    Google Scholar 

  19. Mohammadi M, Al-Azab F, Raahemi B, Richards G, Jaworska N, Smith D, de la Salle S, Blier P, Knott V (2015) Data mining eeg signals in depression for their diagnostic value. BMC Med Inform Decis Making 15(1):108

    Google Scholar 

  20. Beck AT, Steer RA, Brown GK et al (1996) Manual for the beck depression inventory-ii. San Antonio, TX: Psychol Corp 1:82

    Google Scholar 

  21. Hamilton M (1986) The hamilton rating scale for depression. In: Assessment of depression. Springer, pp 143–152

  22. Spitzer RL, Kroenke K, Williams JB, Löwe B (2006) A brief measure for assessing generalized anxiety disorder: the gad-7. Arch Intern Med 166(10):1092–1097

    Google Scholar 

  23. Bjelland I, Dahl AA, Haug TT, Neckelmann D (2002) The validity of the hospital anxiety and depression scale: an upyeard literature review. J Psychosom Res 52(2):69–77

    Google Scholar 

  24. Kroenke K, Spitzer RL, Williams JB (2001) The phq-9: validity of a brief depression severity measure. J Gen Intern Med 16(9):606–613

    Google Scholar 

  25. Čukić M, Stokić M, Radenković S, Ljubisavljević M, Simić S, Savić D (2020) Nonlinear analysis of eeg complexity in episode and remission phase of recurrent depression. Int J Methods Psychiatr Res 29(2):e1816

    Google Scholar 

  26. Puthankattil SD, Joseph PK (2012) Classification of eeg signals in normal and depression conditions by ann using rwe and signal entropy. J Mech Med Biol 12(04):1240019

    Google Scholar 

  27. Ahmadlou M, Adeli H, Adeli A (2012) Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol 85(2):206–211

    Google Scholar 

  28. Faust O, Ang PCA, Puthankattil SD, Joseph PK (2014) Depression diagnosis support system based on eeg signal entropies. J Mech Med Biol 14(03):1450035

    Google Scholar 

  29. Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Puthankatti SD, Adeli A (2015) A novel depression diagnosis index using nonlinear features in eeg signals. Eur Neurol 74(1-2):79–83

    Google Scholar 

  30. Cai H, Sha X, Han X, Wei S, Hu B (2016) Pervasive eeg diagnosis of depression using deep belief network with three-electrodes eeg collector. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1239–1246

  31. Liao SC, Wu CT, Huang HC, Cheng WT, Liu YH (2017) Major depression detection from eeg signals using kernel eigen-filter-bank common spatial patterns. Sensors 17(6):1385

    Google Scholar 

  32. Sharma M, Achuth P, Deb D, Puthankattil SD, Acharya UR (2018) An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with eeg signals. Cogn Syst Res 52:508–520

    Google Scholar 

  33. Cai H, Qu Z, Li Z, Zhang Y, Hu X, Hu B (2020) Feature-level fusion approaches based on multimodal eeg data for depression recognition. Inf Fusion 59:127–138

    Google Scholar 

  34. Shen J, Zhang X, Hu B, Wang G, Ding Z (2019) An improved empirical mode decomposition of electroencephalogram signals for depression detection. IEEE Trans Affect Comput

  35. Crosson B, Ford A, McGregor KM, Meinzer M, Cheshkov S, Li X, Walker-Batson D, Briggs RW (2010) Functional imaging and related techniques: an introduction for rehabilitation researchers. J Rehabil Res Dev 47(2):vii

    Google Scholar 

  36. Cooper R, Osselton JW, Shaw JC (2014) EEG technology. Butterworth-Heinemann, Oxford, United Kingdom

    Google Scholar 

  37. Silverman D (1963) The rationale and history of the 10-20 system of the international federation. Am J EEG Technol 3(1):17–22

    Google Scholar 

  38. Rachamanee S, Wongupparaj P (2021) Resting-state eeg datasets of adolescents with mild, minimal, and moderate depression. BMC Res Notes 14(1):1–3

    Google Scholar 

  39. Mumtaz W (2016) MDD patients and healthy controls EEG data (new) (11). https://doi.org/10.6084/m9.figshare.4244171.v2. https://figshare.com/articles/dataset/EEG_Data_New/4244171

  40. Cai H, Gao Y, Sun S, Li N, Tian F, Xiao H, Li J, Yang Z, Li X, Zhao Q et al (2020) Modma dataset: a multi-modal open dataset for mental-disorder analysis. arXiv:2002.09283

  41. Shi Q, Liu A, Chen R, Shen J, Zhao Q, Hu B (2020) Depression detection using resting state three-channel eeg signal. arXiv:2002.09175

  42. Karnati M, Seal A, Yazidi A, Krejcar O (2021) Lienet: a deep convolution neural networks framework for detecting deception. IEEE Trans Cogn Dev Syst

  43. Mahajan R, Morshed BI (2015) Unsupervised eye blink artifact denoising of eeg data with modified multiscale sample entropy, kurtosis, and wavelet-ica. IEEE J Biomed Health Inform 19(1):158–165

    Google Scholar 

  44. Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from eeg. IEEE Trans Affect Comput 5(3):327–339

    Google Scholar 

  45. Liang Z, Wang Y, Sun X, Li D, Voss LJ, Sleigh JW, Hagihira S, Li X (2015) EEG entropy measures in anesthesia. Front Comput Neurosci 9:16. https://doi.org/10.3389/fncom.2015.00016

    Article  Google Scholar 

  46. Shete S, Shriram R (2014) Comparison of sub-band decomposition and reconstruction of eeg signal by daubechies9 and symlet9 wavelet. In: 2014 fourth international conference on communication systems and network technologies. IEEE, pp 856–861

  47. Zhang S, Li X, Zong M, Zhu X, Wang R (2018) Efficient knn classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 29(5):1774–1785

    MathSciNet  Google Scholar 

  48. Sanchis A, Juan A, Vidal E (2012) A word-based naïve bayes classifier for confidence estimation in speech recognition. IEEE Trans Audio Speech Lang Process 20(2):565–574

    Google Scholar 

  49. Quinlan JR (1987) Simplifying decision trees. Int J Man-Machine Stud 27(3):221–234

    Google Scholar 

  50. Seal A, Bhattacharjee D, Nasipuri M, Rodráguez-esparragón D, Menasalvas E, Gonzalo-Martin C (2018) Pet-ct image fusion using random forest and à-trous wavelet transform. Int J Numer Methods Biomed Eng 34(3):e2933

    MathSciNet  Google Scholar 

  51. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28

    Google Scholar 

  52. Seal A, Ganguly S, Bhattacharjee D, Nasipuri M, Basu DK (2013) Automated thermal face recognition based on minutiae extraction. Int J Comput Intell Stud 2(2):133–156

    Google Scholar 

  53. Mohan K, Seal A (2021) Deception detection on bag-of-lies: integration of multi-modal data using machine learning algorithms. In: Proceedings of international conference on machine intelligence and data science applications. Springer, pp 445–456

  54. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, pp 785–794

  55. Sharma KK, Seal A (2019) Modeling uncertain data using Monte Carlo integration method for clustering. Expert Syst Appl 137:100–116

    Google Scholar 

  56. Karnati M, Seal A, Sahu G, Yazidi A, Krejcar O A novel multi-scale based deep convolutional neural network for detecting covid-19 from x-rays. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2022.109109. https://www.sciencedirect.com/science/article/pii/S1568494622003866

  57. Ocak H (2009) Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036

    Google Scholar 

  58. Mackey S, Chaarani B, Kan KJ, Spechler PA, Orr C, Banaschewski T, Barker G, Bokde AL, Bromberg U, Büchel C et al (2017) Brain regions related to impulsivity mediate the effects of early adversity on antisocial behavior. Biol Psychiatry 82(4):275–282

    Google Scholar 

  59. Xu Y, Goodacre R (2018) On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2(3):249–262

    Google Scholar 

  60. Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, Acharya UR (2019) Automated depression detection using deep representation and sequence learning with eeg signals. J Med Syst 43(7):205

    Google Scholar 

  61. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018) Automated eeg-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 161:103–113

    Google Scholar 

  62. Thoduparambil PP, Dominic A, Varghese SM (2020) Eeg-based deep learning model for the automatic detection of clinical depression. Phys Eng Sci Med:1–12

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

    Google Scholar 

  64. Sharma M, Patel S, Acharya UR (2020) Automated detection of abnormal eeg signals using localized wavelet filter banks. Pattern Recognit Lett

  65. Sharma G, Parashar A, Joshi AM (2021) Dephnn: a novel hybrid neural network for electroencephalogram (eeg)-based screening of depression. Biomed Signal Process Control 66:102393

    Google Scholar 

  66. Mohan K, Seal A, Krejcar O, Yazidi A (2020) Facial expression recognition using local gravitational force descriptor-based deep convolution neural networks. IEEE Trans Instrum Meas 70:1–12

    Google Scholar 

Download references

Acknowledgements

This work is partially supported by the project “Smart Solutions in Ubiquitous Computing Environments”, Grant Agency of Excellence (under ID: UHKFIM-GE-2022), and the SPEV project “Smart Solutions in Ubiquitous Computing Environments” (under ID: UHK-FIMSPEV-2022-2102), University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic. We are also grateful for the support of PhD student Michal Dobrovolny for consultations.

Author information

Authors and Affiliations

  1. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India

    Ayan Seal, Rishabh Bajpai, Mohan Karnati & Jagriti Agnihotri

  2. Department of Computer Science, OsloMet–Oslo Metropolitan University, Oslo, 460167, Norway

    Anis Yazidi

  3. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, 460167, Norway

    Anis Yazidi

  4. Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, 460167, Norway

    Anis Yazidi

  5. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, 18071, Spain

    Enrique Herrera-Viedma

  6. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Enrique Herrera-Viedma

  7. Center for Basic and Applied Science, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 50003, Hradec Kralove, Czech Republic

    Ondrej Krejcar

  8. Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ondrej Krejcar

  9. Institute of Technology and Business in Ceske Budejovice, Ceske Budejovice, Czechia

    Ondrej Krejcar

Authors
  1. Ayan Seal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rishabh Bajpai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohan Karnati
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jagriti Agnihotri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anis Yazidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Enrique Herrera-Viedma
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ondrej Krejcar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayan Seal.

Ethics declarations

Conflict of Interests

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Seal, A., Bajpai, R., Karnati, M. et al. Benchmarks for machine learning in depression discrimination using electroencephalography signals. Appl Intell 53, 12666–12683 (2023). https://doi.org/10.1007/s10489-022-04159-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04159-y

Keywords

Search

Navigation