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Depression diagnosis using machine intelligence based on spatiospectrotemporal analysis of multi-channel EEG

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Abstract

Depression diagnosis is a challenging clinical task currently conducted mostly using subjective criteria. It is well known that depression alters the neural activity in the brain, so that the corresponding neurophysiological signature may be measured using non-invasive electroencephalography (EEG) signals. These, in turn, may be possible to decode using machine learning algorithms. Despite the extensive literature, the existing techniques rely on several channels and obtrusive systems. In this paper, and for the first time, the diagnostic power of each EEG channel for depression detection is analyzed using Neighborhood Component Analysis (NCA). Our results indicate that a mere two features collected from one EEG channel suffice for reliable diagnosis. To evaluate the performance of the proposed method, a dataset comprising seven minutes of EEG recording from 84 subjects is used. The data was divided into two separate sets, one for feature selection and one for diagnostic classification. We delineate brain regions that have the strongest discriminative power linked to depression diagnosis. Thus, we identified one electrode (i.e., AF4) located on the frontal lobe, which can be used to diagnose depression with high accuracy. After evaluation of a series of shallow machine learning methods, we achieved the classification accuracy of 80.8%, sensitivity of 60% and specificity of 99.7% with two features from one electrode. We also achieved the highest classification accuracy of 91.8%, the specificity of 93.5%, and sensitivity of 90% with two electrodes and three features. Our findings show that it is possible to significantly reduce the complexity of algorithms to diagnose depression with the motivation of use in highly accessible wearable devices.

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References

  1. Perini G, Cotta Ramusino M, Sinforiani E, Bernini S, Petrachi R, Costa A (2019) Cognitive impairment in depression: recent advances and novel treatments. Neuropsychiatr Dis Treat 15:1249–1258. https://pubmed.ncbi.nlm.nih.gov/31190831/. Accessed 20 May 2021

  2. Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, Yang J, Feng L, Ding Z, Chen Y, Gutknecht J (2018) A pervasive approach to EEG-based depression detection. Complexity 2018(5238028). https://doi.org/10.1155/2018/5238028

  3. 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

    Article  Google Scholar 

  4. World Health Organization (2018) World Health Organization. https://www.who.int/en/news-room/fact-sheets/detail/depression. Accessed 15 Mar 2019

  5. Evans J, Macrory, Randall C (2016) Measuring national well-being: LIfe in the UK. Office for National Statistics, pp 1–52

  6. McManus S, Bebbington P, Jenkins R, Brugha T (2016) Mental health and wellbeing in England: Adult Psychiatric Morbidity Survey 2014. Leeds: NHS Digital. Eur J Pharm Sci. http://dx.doi.org/10.1016/j.ejps.2012.04.019

  7. Center for Behavioral Health Statistics and Quality (2018) 2017 National survey on drug use and health: detailed tables, substance abuse and mental health services administration, no. September, pp 1–2871. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHDetailedTabs2017/NSDUHDetailedTabs2017.pdf. Accessed 7 June 2019

  8. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. Washington, DC

  9. Pampouchidou A, Simos P, Marias K, Meriaudeau F, Yang F, Pediaditis M, Tsiknakis M (2017) Automatic assessment of depression based on visual cues: a systematic review. IEEE Trans Affect Comput XX(c):1–27

  10. Larry Culpepper M (2014) Misdiagnosis of bipolar depression in primary care practices. J Clin Psychiatry

  11. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198

    Article  CAS  Google Scholar 

  12. Beck A, Steer R, Brown G (1996) Manual for the beck depression inventory-II. Psychological Corporation, TX, San Antonio

    Google Scholar 

  13. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23(1):56–62

    Article  CAS  Google Scholar 

  14. Mahato S, Paul S (2019) Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): a review. In: Nath V, Mandal JK (eds) Nanoelectronics, Circuits and Communication Systems. Springer Singapore, Singapore, pp 323–335

    Chapter  Google Scholar 

  15. de Aguiar Neto FS, Rosa JLG (2019) Depression biomarkers using non-invasive EEG: a review. Neurosci Biobehav Rev 105:83–93. https://www.sciencedirect.com/science/article/pii/S0149763419303823. Accessed 5 Mar 2022

  16. Ke H, Chen D, Shi B, Zhang J, Liu X, Zhang X, Li X (2019) Improving brain E-health Services via high-performance EEG classification with grouping Bayesian optimization. IEEE Trans Serv Comput, 1

  17. Hu B, Peng H, Zhao Q, Hu B, Majoe D, Zheng F, Moore P (2015) Signal quality assessment model for wearable EEG sensor on prediction of mental stress. IEEE Trans Nanobiosci 14(5):553–561

    Article  Google Scholar 

  18. SEMEOTICONS (2014) SEMEiotic oriented technology for individual’s CardiOmetabolic risk self-assessmeNt and self-monitoring. http://www.semeoticons.eu/. Accessed 8 May 2019

  19. Northrup CM, Lantz J, Hamlin T (2016) Wearable stress sensors for children with Autism Spectrum Disorder with in situ alerts to caregivers via a mobile phone. Iproceedings 2(1):e9. http://www.iproc.org/2016/1/e9/. Accessed 18 Nov 2017

  20. Al-Shargie F, Kiguchi M, Badruddin N, Dass SC, Hani AFM, Tang TB (2016) Mental stress assessment using simultaneous measurement of EEG and fNIRS. Biomed Opt Express 7(10):3882. https://www.osapublishing.org/abstract.cfm?URI=boe-7-10-3882. Accessed 19 Feb 2020

  21. Ahmadlou M, Adeli H, Adeli A (2012) Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol 85(2):206–211. https://doi.org/10.1016/j.ijpsycho.2012.05.001

    Article  PubMed  Google Scholar 

  22. Logesparan L, Rodriguez-Villegas E, Casson AJ (2015) The impact of signal normalization on seizure detection using line length features. Med Biol Eng Comput 53:929–942. https://doi.org/10.1007/s11517-015-1303-x

    Article  PubMed  Google Scholar 

  23. Logesparan L, Casson AJ, Rodriguez-Villegas E (2016) Erratum to: optimal features for online seizure detection. Med Biol Eng Comput 54:1295. https://doi.org/10.1007/s11517-016-1535-4

    Article  PubMed  Google Scholar 

  24. Imtiaz SA, Logesparan L, Rodriguez-Villegas E (2015) Performance-power consumption tradeoff in wearable epilepsy monitoring systems. IEEE J Biomed Health Inform 19:1019–1028. https://doi.org/10.1109/JBHI.2014.2342501

    Article  PubMed  Google Scholar 

  25. Iranmanesh S, Rodriguez-Villegas E (2017) A 950 nW Analog-Based data reduction chip for wearable EEG systems in epilepsy. IEEE J Solid-state Circuits 52:2362–2373. https://doi.org/10.1109/JSSC.2017.2720636

    Article  Google Scholar 

  26. Hirschauer TJ, Adeli H, Buford JA (2015) Computer-aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J Med Syst 39(11)

  27. Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E (2016) Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia. Behav Brain Res 298:248–260

    Article  CAS  Google Scholar 

  28. Kim HT, Kim BY, Park EH, Kim JW, Hwang EW, Han SK, Cho S (2005) Computerized recognition of Alzheimer disease-EEG using genetic algorithms and neural network. Futur Gener Comput Syst 21(7):1124–1130

    Article  Google Scholar 

  29. Tylova L, Kukal J, Vysata O (2013) Predictive models in diagnosis of Alzheimer’s disease from EEG. Acta Polytechnica 53(2)

  30. Raghavendra B, Dutt D (2010) A study of long-range correlations in schizophrenia EEG using detrended fluctuation analysis. 2010 International Conference on Signal Processing and Communications (SPCOM 2010)

  31. Zhao Q, Hu B, Li Y, Peng H, Li L, Liu Q, Li Y, Shi Q, Feng J (2013) An Alpha resting EEG study on nonlinear dynamic analysis for schizophrenia. 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER 2013)

  32. O’Keeffe J, Carlson B, DeStefano L, Wenger M, Craft M, Hershey L, Hughes J, Wu D, Ding L, Yuan H (2017) EEG fluctuations of wake and sleep in mild cognitive impairment. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

  33. Gomez C, Ruiz-Gomez S, Poza J, Maturana-Candelas A, Nunez P, Pinto N, Tola-Arribas M, Cano M, Hornero R (2018) Assessment of EEG connectivity patterns in mild cognitive impairment using phase slope index. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

  34. Ferrillo F, Plazzi G, Nobili L, Beelke M, De Carli F, Cortelli P, Tinuper P, Avoni P, Vandi S, Gambetti P, Lugaresi E, Montagna P (2001) Absence of sleep EEG markers in fatal familial insomnia healthy carriers: a spectral analysis study. Clin Neurophysiol 112(10):10

    Article  Google Scholar 

  35. Giannakaki K, Giannakakis G, Farmaki C, Sakkalis V (2017) Emotional state recognition using advanced machine learning techniques on EEG data. 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

  36. H. Polat and M. ÖZerdem (2018) Automatic Detection of Emotional State from EEG Signal by Gamma Coherence Approach. 2018 Innovations in Intelligent Systems and Applications Conference (ASYU). Proceedings

  37. Xiaowei Li, Bin Hu, Shuting Sun, Hanshu Cai (2016) EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed 136:11

    Google Scholar 

  38. Peng H, Xia C, Wang Z, Zhu J, Zhang X, Sun S, Li J, Huo X, Li X (2019) Multivariate pattern analysis of EEG-based functional connectivity: a study on the identification of depression. IEEE Access 7:92630–92641

    Article  Google Scholar 

  39. Li X, La R, Wang Y, Niu J, Zeng S, Sun S, Zhu J (2019) EEG-based mild depression recognition using convolutional neural network. Med Biol Eng Comput (Germany) 57(6):1341–52. https://doi.org/10.1007/s11517-019-01959-2

    Article  Google Scholar 

  40. Bachmann M, Päeske L, Kalev K, Aarma K, Lehtmets A, Ööpik P, Lass J, Hinrikus H (2018) Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Comput Methods Prog Biomed 155:11–17

    Article  Google Scholar 

  41. Shen J, Zhao S, Yao Y, Wang Y, Feng L (2017) A novel depression detection method based on pervasive EEG and EEG splitting criterion. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1860–1867

  42. Zhang X, Shen J, Din ZU, Liu J, Wang G, Hu B (2019) Multimodal depression detection: Fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble. IEEE Journal of Biomedical and Health Informatics 23:2265–2275. Classification results; Classifier ensembles; Depressive symptom; Detection methods; Individual Differences; Late fusion; Multi-agent strategy; Multi-modal. https://doi.org/10.1109/JBHI.2019.2938247

  43. H. Cai, X. Sha, X. Han, S. Wei, and B. Hu, Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector, in Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, ser. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Sch. of Inf. Sci. Eng., Lanzhou Univ., Lanzhou, China BT - 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 15–18 Dec. 2016: IEEE, 2016, pp 1239–1246. http://dx.doi.org/10.1109/BIBM.2016.7822696

  44. Cai H, Zhang X, Zhang Y, Wang Z, Hu B (2018) A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2018.2801289

    Article  Google Scholar 

  45. Goverdovsky V, von Rosenberg W, Nakamura T, Looney D, Sharp DJ, Papavassiliou C, Morrell MJ, Mandic DP (2017) Hearables: multimodal physiological in-ear sensing. Sci Rep 7(1):6948. http://www.nature.com/articles/s41598-017-06925-2. Accessed 15 Sept 2017

  46. Hammour G, Yarici M, Rosenberg WV, Mandic DP (2019) Hearables: feasibility and validation of in-ear electrocardiogram. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp 5777–5780

  47. Yang W, Wang K, Zuo W (2012) Neighborhood component feature selection for high-dimensional data. J Comput 7(1):162–168

    Article  Google Scholar 

  48. Delorme A, Makeig S (2004) EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  Google Scholar 

  49. MATLAB, 9.11.0.1873467 (R2021b) Update 3. Natick, Massachusetts: The MathWorks Inc., 2021

  50. Cavanagh JF, Napolitano A, Wu C, Mueen A (2017) The Patient Repository for EEG Data + Computational Tools (PRED+CT). Front Neuroinform 11(November):1–9. http://journal.frontiersin.org/article/10.3389/fninf.2017.00067/full. Accessed 13 Feb 2019

  51. Cavanagh JF, Bismark AJ, Frank MJ, Allen JJB (2011) Larger error signals in major depression are associated with better avoidance learning. Front Psychol 2(NOV):1–6

  52. Cavanagh JF (2019) Electrophysiology as a theoretical and methodological hub for the neural sciences. Psychophysiology 56(2):1–13

    Google Scholar 

  53. Singh A, Richardson SP, Narayanan N, Cavanagh JF (2018) Mid-frontal theta activity is diminished during cognitive control in Parkinson’s disease. Neuropsychologia 117(February):113–122. https://doi.org/10.1016/j.neuropsychologia.2018.05.020

    Article  PubMed  PubMed Central  Google Scholar 

  54. Patrick KC, Imtiaz SA, Bowyer S, Rodriguez-Villegas E (2016) An algorithm for automatic detection of drowsiness for use in wearable EEG systems. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol 2016-Octob. EMBS, pp 3523–3526

  55. Imtiaz SA, Rodriguez-Villegas E (2015) Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems. Thesis, no. December, p 258. https://spiral.imperial.ac.uk/bitstream/10044/1/29459/1/Imtiaz-SA-2016-PhD-Thesis.pdf. Accessed 10 Nov 2017

  56. Erguzel TT, Sayar GH, Tarhan N (2016) Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput Applic 27(6):1607–1616. https://doi.org/10.1007/s00521-015-1959-z

    Article  Google Scholar 

  57. Mumtaz W, Ali SSA, Yasin MAM, Malik AS (2018) A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput 56(2):233–246. https://doi.org/10.1007/s11517-017-1685-z

    Article  PubMed  Google Scholar 

  58. Zhao S, Zhao Q, Zhang X, Peng H, Yao Z, Shen J, Yao Y, Jiang H, Hu B (2017) Wearable EEG-based real-time system for depression monitoring. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 10654. LNAI, pp 190–201

  59. Katyal Y, Alur SV, Dwivedi S, Menaka R (2014) EEG signal and video analysis based depression indication, in Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014, ser. 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies (ICACCCT), no. 978. ECE, VIT Univ., Chennai, India BT - 2014 International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT), 8–10 May 2014: IEEE, pp 1353–1360. https://doi.org/10.1109/ICACCCT.2014.7019320

  60. Puk KM, Gandy KC, Wang S, Park H (2016) Pattern Classification and Analysis of Memory Processing in Depression Using EEG Signals. In: Lecture Notes in Computer Science - Brain Informatics and Health. International Conference, BIH 2016, 13–16 Oct. 2016, ser. Brain Informatics and Health. International Conference, BIH 2016. Proceedings: LNAI 9919, vol. 1. Dept. of Ind., Manuf., Syst. Eng., Univ. of Texas at Arlington, Arlington, TX, United States BT - Brain Informatics and Health. International Conference, BIH 2016, 13–16 Oct. 2016: Springer International Publishing, pp 124–137. https://doi.org/10.1007/978-3-319-47103-7_13

  61. Mohan Y, Chee SS, Xin DKP, Foong LP (2016) Artificial neural network for classification of depressive and normal in EEG. In: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 4–8 Dec. 2016, ser. 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). Dept. of Mechatron. Biomed. Eng., Univ. Tunku Abdul Rahman, Kajang, Malaysia BT - 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 4–8 Dec. 2016: IEEE, pp. 286–290. https://doi.org/10.1109/IECBES.2016.7843459

  62. 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). pp 1239–1246

  63. Zhang X, Diao W, Cheng Z (2007) Wavelet transform and singular value decomposition of EEG signal for pattern recognition of complicated hand activities. In: Duffy VG (ed) Digital Human Modeling. Berlin, Heidelberg: Springer, Berlin Heidelberg, pp 294–303

  64. Kumar SD, Subha DP (2019) Prediction of depression from EEG signal using long short term memory(LSTM). In: Proceedings of the International Conference on Trends in Electronics and Informatics, vol 2019-April. ICOEI 2019, Tirunelveli, India, pp 1248–1253

  65. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12(5):535–540

    Article  CAS  Google Scholar 

  66. Kwon H, Kang S, Park W, Park J, Lee Y (2019) Deep learning based pre-screening method for depression with imagery frontal EEG channels. In: 2019 International Conference on Information and Communication Technology Convergence (ICTC), pp 378–80. Depression; Imagery frontal EEG channels; Mental illness; Prefrontal brain wave asymmetry-based image; Deep learning-based model; Deep learning based prescreening method

  67. Bachmann M, Paeske L, Kalev K, Aarma K, Lehtmets A, Oopik P, Lass J, Hinrikus H (2018) Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer Methods and Programs in Biomedicine 155:11–17. Depression; Non-linear signal processing; Power variability; Relative gamma power; Spectral asymmetry index. https://doi.org/10.1016/j.cmpb.2017.11.023

  68. Cai H, Zhang Y, Sha X, Hu B (2017) Study on depression classification based on electroencephalography data collected by wearable devices BT - brain informatics. Brain Informatics 3:244–253

    Article  Google Scholar 

  69. Bachmann M, Lass J, Hinrikus H (2017) Single channel EEG analysis for detection of depression. Biomed Signal Process Control 31:391–397. https://www.engineeringvillage.com/share/document.url?mid=cpx_M3fc78bf51576c2a465aM728610178163171&database=cpx. Accessed 16 May 2018

  70. Koessler L, Maillard L, Benhadid A, Vignal JP, Felblinger J, Vespignani H, Braun M (2009) Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. Neuroimage 46(1):64–72. https://doi.org/10.1016/j.neuroimage.2009.02.006

    Article  CAS  PubMed  Google Scholar 

  71. Kaiser DA (2010) Cortical cartography. Biofeedback 38(1):9–12

    Article  Google Scholar 

  72. Seal A, Bajpai R, Agnihotri J, Yazidi A, Herrera-Viedma E, Krejcar O (2021) Deprnet: a deep convolution neural network framework for detecting depression using EEG. IEEE Trans Instrum Meas 70:1–13

    Article  Google Scholar 

  73. Li X, La R, Wang Y, Hu B, Zhang X (2020) A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography. Front Neurosci 14:192. https://www.frontiersin.org/article/10.3389/fnins.2020.00192

  74. Wan Z, Huang J, Zhang H, Zhou H, Yang J, Zhong N (2020) Hybrideegnet: a convolutional neural network for EEG feature learning and depression discrimination. IEEE Access 8:30332–30342

    Article  Google Scholar 

  75. Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S (2016) Sleep quality prediction from wearable data using deep learning. JMIR mHealth and uHealth 4(4):e125–e125. https://pubmed.ncbi.nlm.nih.gov/27815231www.ncbi.nlm.nih.gov/pmc/articles/PMC5116102/. Accessed 20 May 2021

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  1. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Amir Nassibi & Christos Papavassiliou

  2. Department of Mechanical and Aerospace Engineering, New York University, New York, NY, USA

    S. Farokh Atashzar

  3. Department of Electrical and Computer Engineering, New York University (NYU), New York City, NY, USA

    S. Farokh Atashzar

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Nassibi, A., Papavassiliou, C. & Atashzar, S.F. Depression diagnosis using machine intelligence based on spatiospectrotemporal analysis of multi-channel EEG. Med Biol Eng Comput 60, 3187–3202 (2022). https://doi.org/10.1007/s11517-022-02647-4

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