Skip to main content

Advertisement

SpringerLink
Log in

Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Psychotic disorders are mental disorders that negatively affect human life. Diagnosis of psychotic patients is usually done in consultation with the patient, and this is a time-consuming process. In this study, a Computer Aided Diagnosis (CAD) system that will support expert opinion with automatic diagnosis of schizophrenia (SZ) disease, which is the leading psychotic disorder, is presented. In this study, Hilbert Huang Transform (HHT) method was used to analyze the non-stationary and non-periodic structure of EEG (Electroencephalograph) signals in the best way. The data set we used in our study includes 19-channel EEG signals from 28 (14 SZ and 14 healthy controls) participants, and the second data set includes 16-channel EEG signals from 84 (45 SZ and 39 healthy controls) participants. First of all, HS (Hilbert Spectrum) images of the first four Intrinsic Mode Functions (IMF) components obtained by applying Empirical Mode Decomposition (EMD) to EEG signals were created. These images were then classified with the VGG16 pre-trained Convolutional Neural Network (CNN) network. With our proposed method, the highest classification performance was obtained as 98.2% for Dataset I and 96.02% for Dataset II, respectively, by training the HS images obtained from the IMF 1 component with the VGG16 pre-trained CNN network. In the next step, classification performances were tested with VGG16, XCeption, DenseNet121, ResNet152 and Inception V3 pre-trained CNN networks. The high classification success achieved by the proposed method in our study demonstrates the accuracy of the model in distinguishing between SZ and healthy control.

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

Data availability

The datasets used in this study can be accessed from: (dataset I) https://repod.pon.edu.pl/dataset/eeg-in-schizophrenia (dataset II) http://brain.bio.msu.ru/eeg_schizophrenia.htm web addresses.

References

  1. Mercy, “What are Psychotic Disorders?” [Online]. Available: https://www.mercy.net/service/psychotic-schizophrenia-disorder/. [Accessed: 02-Mar-2021].

  2. WHO_, “Schizophrenia_,” https//www.who.int/mental_health/management/schizophrenia/en/. accessed 24 Sept. 2020., 2020.

  3. Devia C et al (2019) Eeg classification during scene free-viewing for schizophrenia detection. IEEE Trans Neural Syst Rehabil Eng 27(6):1193–1199. https://doi.org/10.1109/TNSRE.2019.2913799

    Article  Google Scholar 

  4. Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR (2019) Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl Sci 9(14):2870. https://doi.org/10.3390/app9142870

    Article  Google Scholar 

  5. Siuly S, Alcin OF, Bajaj V, Sengur A, Zhang Y (2018) Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure. IET Sci Meas Technol 13(1):35–41. https://doi.org/10.1049/iet-smt.2018.5358

    Article  Google Scholar 

  6. Talo M, Baloglu UB, Yıldırım Ö, Acharya UR (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res, https://doi.org/10.1016/j.cogsys.2018.12.007

  7. Gudigar A, Raghavendra U, San TR, Ciaccio EJ, Acharya UR (2019) Application of multiresolution analysis for automated detection of brain abnormality using MR images: A comparative study. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.08.008

    Article  Google Scholar 

  8. H. W. Loh, C. P. Ooi, S. G. Dhok, M. Sharma, A. A. Bhurane, and U. R. Acharya, “Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network,” Appl. Intell., pp. 1–15, 2021.

  9. S. Farashi, “Analysis of vertical eye movements in Parkinson’s disease and its potential for diagnosis,” Appl. Intell., pp. 1–11, 2021. https://link.springer.com/article/https://doi.org/10.1007/s10489-021-02364-9

  10. H. R. Al Ghayab, Y. Li, S. Siuly, and S. Abdulla, “A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification,” J. Neurosci. Methods, vol. 312, pp. 43–52, 2019. https://doi.org/10.1016/j.jneumeth.2018.11.014

  11. Shim M, Hwang H-J, Kim D-W, Lee S-H, Im C-H (2016) Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophr Res 176(2–3):314–319. https://doi.org/10.1016/j.schres.2016.05.007

    Article  Google Scholar 

  12. B. Cao et al., “Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity,” Mol. Psychiatry, pp. 1–8, 2018.

  13. Huang NE et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci., vol. 454, no. 1971, pp. 903–995. https://doi.org/10.1098/rspa.1998.0193

  14. Bouchikhi A, Boudraa A-O (2012) Multicomponent AM–FM signals analysis based on EMD–B-splines ESA. Signal Process 92(9):2214–2228. https://doi.org/10.1016/j.sigpro.2012.02.014

    Article  Google Scholar 

  15. Dinarès-Ferran J, Ortner R, Guger C, Solé-Casals J (2018) A new method to generate artificial frames using the empirical mode decomposition for an EEG-based motor imagery BCI. Front Neurosci 12:308. https://doi.org/10.3389/fnins.2018.00308

    Article  Google Scholar 

  16. H. Wang, M. Du, F. Yang, and Z. Zhang, “Score-cam: Improved visual explanations via score-weighted class activation mapping,” arXiv Prepr. arXiv1910.01279, 2019.

  17. Kim JW, Lee YS, Han DH, Min KJ, Lee J, Lee K (2015) Diagnostic utility of quantitative EEG in un-medicated schizophrenia. Neurosci Lett 589:126–131. https://doi.org/10.1016/j.neulet.2014.12.064

    Article  Google Scholar 

  18. 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. https://doi.org/10.1016/j.jneumeth.2003.10.009

    Article  Google Scholar 

  19. Dvey-Aharon Z, Fogelson N, Peled A, Intrator N (2015) Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach. PLoS ONE 10(4):e0123033. https://doi.org/10.1371/journal.pone.0123033

    Article  Google Scholar 

  20. Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans signal Process 44(4):998–1001. https://doi.org/10.1109/78.492555

    Article  Google Scholar 

  21. Johannesen JK, Bi J, Jiang R, Kenney JG, Chen C-MA (2016) Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr Electrophysiol. https://doi.org/10.1186/s40810-016-0017-0

    Article  Google Scholar 

  22. Santos-Mayo L, San-José-Revuelta LM, Arribas JI (2016) A computer-aided diagnosis system with EEG based on the P3b wave during an auditory odd-ball task in schizophrenia. IEEE Trans Biomed Eng 64(2):395–407. https://doi.org/10.1109/TBME.2016.2558824

    Article  Google Scholar 

  23. P. A. Devijver and J. Kittler, Pattern recognition: A statistical approach. Prentice hall, 1982.

  24. Aslan Z, Akin M (2019) Detection of schizophrenia on eeg signals by using relative wavelet energy as a feature extractor. Proc. B

  25. B. Thilakvathi, S. Shenbaga Devi, K. Bhanu, and M. Malaippan, “EEG signal complexity analysis for schizophrenia during rest and mental activity,” 2017.

  26. Piryatinska A, Darkhovsky B, Kaplan A (2017) Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions. Comput Methods Programs Biomed 152:131–139. https://doi.org/10.1016/j.cmpb.2017.09.001

    Article  Google Scholar 

  27. Sui J, “Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection”, in, et al (2014) 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2014:2014. https://doi.org/10.1109/EMBC.2014.6944473

    Article  Google Scholar 

  28. Boostani R, Sadatnezhad K, Sabeti M (2009) An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2008.07.037

    Article  Google Scholar 

  29. Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y (2020) A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals. IEEE Trans Neural Syst Rehabil Eng. https://doi.org/10.1109/TNSRE.2020.3022715

    Article  Google Scholar 

  30. Sun J et al (2021) A hybrid deep neural network for classification of schizophrenia using EEG Data. Sci Rep 11(1):1–16. https://doi.org/10.1038/s41598-021-83350-6

    Article  Google Scholar 

  31. Phang CR, Ting CM, Noman F, Ombao H (2019) Classification of EEG-based brain connectivity networks in schizophrenia using a multi-domain connectome convolutional neural network,” arXiv. https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.1109%2FJBHI.2019.2941222&v=18340120

  32. Z. ASLAN and M. AKIN (2020) Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals,” Trait. du Signal, https://doi.org/10.18280/ts.370209

  33. Olejarczyk E, Jernajczyk W (2017) Graph-based analysis of brain connectivity in schizophrenia. PLoS ONE 12(11):e0188629. https://doi.org/10.1371/journal.pone.0188629

    Article  Google Scholar 

  34. S. V. Borisov, A. Y. Kaplan, N. L. Gorbachevskaya, and I. A. Kozlova, “Analysis of EEG structural synchrony in adolescents with schizophrenic disorders,” Hum. Physiol., 2005.

  35. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  36. C. A. T. Naira and C. J. L. Del Alamo (2019) Classification of people who suffer schizophrenia and healthy people by EEG signals using deep learning,” Int. J. Adv. Comput. Sci. Appl

  37. Shalbaf A, Bagherzadeh S, Maghsoudi A (2020) Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med 43(4):1229–1239. https://doi.org/10.1007/s13246-020-00925-9

    Article  Google Scholar 

  38. A. N. Chandran, K. Sreekumar, and D. P. Subha, “EEG-based automated detection of schizophrenia using long short-term memory (LSTM) network,” in Advances in Machine Learning and Computational Intelligence, Springer, 2021, pp. 229–236. https://doi.org/10.1007/978-981-15-5243-4_19

  39. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv Prepr. arXiv1412.6980, 2014.

Download references

Author information

Authors and Affiliations

  1. Vocational School of Technical Sciences, Gaziantep University, Gaziantep, 27310, Turkey

    Aslan Zülfikar

  2. Electrical-Electronics Engineering, Faculty of Engineering, Dicle University, Diyarbakır, 21280, Turkey

    Akin Mehmet

Authors
  1. Aslan Zülfikar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akin Mehmet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aslan Zülfikar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zülfikar, A., Mehmet, A. Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals. Appl Intell 52, 12103–12115 (2022). https://doi.org/10.1007/s10489-022-03252-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03252-6

Keywords

Search

Navigation