Skip to main content

Advertisement

Log in

Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning

  • Research
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinking, perception, and behavior. Early identification of schizophrenia can help prevent potential complications and facilitate effective treatment and management of the condition. This paper proposes a computer aided diagnosis system for the early detection of schizophrenia using 19-channel Electroencephalography (EEG) signals from 28 subjects, leveraging statistical moments of Mel-frequency Cepstral Coefficients (MFCC) and ensemble learning. Initially, the EEG signals are passed through a high-pass filter to mitigate noise and remove extraneous data. The feature extraction technique is then employed to extract MFC coefficients from the filtered EEG signals. The dimensionality of these coefficients is reduced by computing their statistical moments, which include the mean, standard deviation, skewness, kurtosis, and energy. Subsequently, the Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is applied to identify pertinent features from the statistical moments of the MFC coefficients. These SVM-RFE-based selected features serve as input for three base classifiers: Support Vector Machine, k-Nearest Neighbors, and Logistic Regression. Additionally, an ensemble learning approach, which combines the predictions of the three classifiers through majority voting, is introduced to enhance schizophrenia detection performance and generalize the results of the proposed approach. The study’s findings demonstrate that the ensemble model, combined with SVM-RFE-based selected statistical moments of MFCC, achieves encouraging detection performance, highlighting the potential of machine learning techniques in advancing the diagnostic process of schizophrenia.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Algorithm 1
Algorithm 2
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

The EEG signal dataset used in the research have been acquired from Repository for Open Data (RepOD).

References

  • Agarwal, M., & Singhal, A. (2023). Fusion of pattern-based and statistical features for schizophrenia detection from eeg signals. Medical Engineering & Physics, 112, 103949.

    Article  Google Scholar 

  • Ahmed, N., Natarajan, T., & Rao, K. R. (1974). Discrete cosine transform. IEEE Transactions on Computers, 100(1), 90–93.

    Article  Google Scholar 

  • Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Robinson, J. E., Sridharan, S., Johnston, P. J., Laurens, K. R., & Fookes, C. (2020). Identification of children at risk of schizophrenia via deep learning and eeg responses. IEEE Journal of Biomedical and Health Informatics, 25(1), 69–76.

    Article  Google Scholar 

  • Akbari, H., Ghofrani, S., Zakalvand, P., & Sadiq, M. T. (2021). Schizophrenia recognition based on the phase space dynamic of eeg signals and graphical features. Biomedical Signal Processing and Control, 69, 102917.

    Article  Google Scholar 

  • Aslan, Z., Akin, M. (2020). Automatic detection of schizophrenia by applying deep learning over spectrogram images of eeg signals. Traitement du Signal,37(2)

  • Aydemir, E., Dogan, S., Baygin, M., Ooi, C. P., Barua, P. D., Tuncer, T., & Acharya, U. R. (2022). Cgp17pat: Automated schizophrenia detection based on a cyclic group of prime order patterns using eeg signals. In Healthcare (Vol. 10, p. 643). MDPI.

  • Baygin, M. (2021). An accurate automated schizophrenia detection using tqwt and statistical moment based feature extraction. Biomedical Signal Processing and Control, 68, 102777.

    Article  Google Scholar 

  • Berardi, M., Brosch, K., Pfarr, J.-K., Schneider, K., Sültmann, A., Thomas-Odenthal, F., Wroblewski, A., Usemann, P., Philipsen, A., Dannlowski, U., et al. (2023). Relative importance of speech and voice features in the classification of schizophrenia and depression. Translational Psychiatry, 13(1), 298.

    Article  PubMed  PubMed Central  Google Scholar 

  • Berger, H. (1929). Über das elektroenkephalogramm des menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87(1), 527–570.

    Article  Google Scholar 

  • Berger, H. (1929). Über das elektroenkephalogramm des menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87(1), 527–570.

    Article  Google Scholar 

  • Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39(227), 357–365.

    CAS  Google Scholar 

  • Bhadra, S., & Kumar, C. J. (2024). Enhancing the efficacy of depression detection system using optimal feature selection from ehr. Computer Methods in Biomechanics and Biomedical Engineering, 27(2), 222–236.

    Article  PubMed  Google Scholar 

  • Buettner, R., Beil, D., Scholtz, S., & Djemai, A. (2020) Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute eeg recordings. In Hawaii International Conference on System Sciences (p. 10). https://api.semanticscholar.org/CorpusID:210847454

  • Buettner, R., Hirschmiller, M., Schlosser, K., Rössle, M., Fernandes, M., & Timm, I. J. (2019). High-performance exclusion of schizophrenia using a novel machine learning method on eeg data. In 2019 IEEE International conference on E-health networking, application & services (HealthCom) (pp. 1–6). IEEE.

  • Collaborators, G. M. D., et al. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. The Lancet Psychiatry, 9(2), 137–150.

    Article  Google Scholar 

  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

    Article  Google Scholar 

  • Cramer, J. (2002). The origins of logistic regression. Tinbergen Institute, Tinbergen Institute Discussion Papers.[SPACE]https://doi.org/10.2139/ssrn.360300

    Article  Google Scholar 

  • Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357–366.

    Article  Google Scholar 

  • de Miras, J. R., Ibáñez-Molina, A. J., Soriano, M. F., & Iglesias-Parro, S. (2023). Schizophrenia classification using machine learning on resting state eeg signal. Biomedical Signal Processing and Control, 79, 104233.

    Article  Google Scholar 

  • Desai, K. (2023). Using electroencephalographic signal processing and machine learning binary classification to diagnose schizophrenia. Research Square. https://doi.org/10.21203/rs.3.rs-2715657/v1

  • Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1–15). Springer.

  • Diogo, V. S., Ferreira, H. A., Prata, D., & Initiative, A. D. N. (2022). Early diagnosis of alzheimer’s disease using machine learning: A multi-diagnostic, generalizable approach. Alzheimer’s Research & Therapy, 14(1), 107.

    Article  Google Scholar 

  • Drucker, H. (1997). Improving regressors using boosting techniques. In Icml (Vol. 97, p. e115). Citeseer.

  • Escabí, M. A. (2005) 10 - biosignal processing. In J. D. Enderle, S. M. Blanchard, & J. D. Bronzino (Eds.), Introduction to biomedical engineering (Second edition) (2nd ed., pp. 549–625). Biomedical Engineering, Academic Press, Boston. https://doi.org/10.1016/B978-0-12-238662-6.50012-4. https://www.sciencedirect.com/science/article/pii/B9780122386626500124

  • Fleischmann, A., & De Leo, D. (2014). The world health organization’s report on suicide: A fundamental step in worldwide suicide prevention. The Journal of Crisis Intervention and Suicide Prevention, 35(5), 289–291.

    Article  Google Scholar 

  • Grattan-Guinness, I. (2005). Joseph fourier, théorie analytique de la chaleur (1822). In Landmark Writings in Western Mathematics 1640-1940 (pp. 354–365). Elsevier.

  • Green, D. M., Swets, J. A., et al. (1966). Signal detection theory and psychophysics (Vol. 1). Wiley New York.

  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422.

    Article  Google Scholar 

  • Hajian-Tilaki, K. (2013). Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine, 4(2), 627.

    PubMed  PubMed Central  Google Scholar 

  • Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18–28.

    Article  Google Scholar 

  • Institute of health metrics and evaluation (ihme). global health data exchange (ghdx). (2019). http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/27a7644e8ad28e739382d31e77589dd7. Accessed 18 March 2024.

  • Jahmunah, V., Oh, S. L., Rajinikanth, V., Ciaccio, E. J., Cheong, K. H., Arunkumar, N., & Acharya, U. R. (2019). Automated detection of schizophrenia using nonlinear signal processing methods. Artificial Intelligence in Medicine, 100, 101698.

    Article  CAS  PubMed  Google Scholar 

  • Keeley, B. (2021). The state of the world’s children 2021: On my mind–promoting, protecting and caring for children’s mental health. UNICEF.

  • Kim, J.-Y., Lee, H. S., & Lee, S.-H. (2020). Eeg source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity—a machine learning approach. Journal of Clinical Medicine, 9(12), 3934.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ko, D.-W., & Yang, J.-J. (2022). Eeg-based schizophrenia diagnosis through time series image conversion and deep learning. Electronics, 11(14), 2265.

    Article  Google Scholar 

  • Krishnapriya, B. (2024). Eeg-based identification of schizophrenia using deep learning techniques. In Computational intelligence and Network Systems: First International Conference, CINS 2023, Dubai, United Arab Emirates, October 18-20, 2023, Proceedings (p. 26). Springer Nature.

  • Kumar, J. S., & Bhuvaneswari, P. (2012). Analysis of electroencephalography (eeg) signals and its categorization-a study. Procedia engineering, 38, 2525–2536.

    Article  Google Scholar 

  • Kumar, T. S., Rajesh, K. N., Maheswari, S., Kanhangad, V., & Acharya, U. R. (2023). Automated schizophrenia detection using local descriptors with eeg signals. Engineering Applications of Artificial Intelligence, 117, 105602.

    Article  Google Scholar 

  • Kumar, T. S., Rajesh, K. N., Maheswari, S., Kanhangad, V., & Acharya, U. R. (2023). Automated schizophrenia detection using local descriptors with eeg signals. Engineering Applications of Artificial Intelligence, 117, 105602.

    Article  Google Scholar 

  • Mental health. (2022). https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response. Accessed 18 Mar 2024.

  • Mikolas, P., Marxen, M., Riedel, P., Bröckel, K., Martini, J., Huth, F., Berndt, C., Vogelbacher, C., Jansen, A., Kircher, T., et al. (2024). Prediction of estimated risk for bipolar disorder using machine learning and structural mri features. Psychological Medicine, 54(2), 278–288.

    Article  PubMed  Google Scholar 

  • Moitra, M., Santomauro, D., Degenhardt, L., Collins, P. Y., Whiteford, H., Vos, T., & Ferrari, A. (2021). Estimating the risk of suicide associated with mental disorders: A systematic review and meta-regression analysis. Journal of Psychiatric Research, 137, 242–249.

    Article  PubMed  PubMed Central  Google Scholar 

  • Najafzadeh, H., Esmaeili, M., Farhang, S., Sarbaz, Y., & Rasta, S. H. (2021). Automatic classification of schizophrenia patients using resting-state eeg signals. Physical and Engineering Sciences in Medicine, 44(3), 855–870.

    Article  PubMed  Google Scholar 

  • Oh, S. L., Vicnesh, J., Ciaccio, E. J., Yuvaraj, R., & Acharya, U. R. (2019). Deep convolutional neural network model for automated diagnosis of schizophrenia using eeg signals. Applied Sciences, 9(14), 2870.

    Article  Google Scholar 

  • Olejarczyk, E., & Jernajczyk, W. (2017). EEG in schizophrenia. (2017). https://doi.org/10.18150/repod.0107441

  • Olejarczyk, E., & Jernajczyk, W. (2017). Graph-based analysis of brain connectivity in schizophrenia. PLoS ONE, 12(11), 1–28.

    Article  Google Scholar 

  • Richhariya, B., Tanveer, M., Rashid, A. H., Initiative, A. D. N., et al. (2020). Diagnosis of alzheimer’s disease using universum support vector machine based recursive feature elimination (usvm-rfe). Biomedical Signal Processing and Control, 59, 101903.

    Article  Google Scholar 

  • Schizophrenia. (2022). https://www.who.int/news-room/fact-sheets/detail/schizophrenia. Accessed 18 March 2024.

  • Shams, A. M., & Jabbari, S. (2024). A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory. Biomedical Engineering Letters, 1–13.

  • Sharma, A., & Verbeke, W. J. (2020). Improving diagnosis of depression with xgboost machine learning model and a large biomarkers dutch dataset (n= 11,081). Frontiers in big Data, 3, 15.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shen, M., Wen, P., Song, B., & Li, Y. (2024). 3d convolutional neural network for schizophrenia detection using as eeg-based functional brain network. Biomedical Signal Processing and Control, 89, 105815.

    Article  Google Scholar 

  • Shoeibi, A., Ghassemi, N., Khodatars, M., Moridian, P., Khosravi, A., Zare, A., Gorriz, J. M., Chale-Chale, A. H., Khadem, A., & Rajendra Acharya, U. (2023). Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fmri modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cognitive Neurodynamics, 17(6), 1501–1523.

    Article  PubMed  Google Scholar 

  • Singh, T. (2019) Fourier transform. (2019). https://medium.com/@tanveer9812/mfccs-made-easy-7ef383006040. Accessed 10 Feb 2024

  • Soria, C., Arroyo, Y., Torres, A. M., Redondo, M. Á., Basar, C., & Mateo, J. (2023). Method for classifying schizophrenia patients based on machine learning. Journal of Clinical Medicine, 12(13), 4375.

    Article  PubMed  PubMed Central  Google Scholar 

  • Soria, C., Arroyo, Y., Torres, A. M., Redondo, M. Á., Basar, C., & Mateo, J. (2023). Method for classifying schizophrenia patients based on machine learning. Journal of Clinical Medicine, 12(13), 4375.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sun, J., Cao, R., Zhou, M., Hussain, W., Wang, B., Xue, J., & Xiang, J. (2021). A hybrid deep neural network for classification of schizophrenia using eeg data. Scientific Reports, 11(1), 4706.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Supakar, R., Satvaya, P., & Chakrabarti, P. (2022). A deep learning based model using rnn-lstm for the detection of schizophrenia from eeg data. Computers in Biology and Medicine, 151, 106225.

    Article  PubMed  Google Scholar 

  • Teixeira, F. L., Costa, M. R. e., Abreu, J. P., Cabral, M., Soares, S. P., & Teixeira, J. P. (2023). A narrative review of speech and eeg features for schizophrenia detection: Progress and challenges. Bioengineering,10(4), 493.

  • Tyagi, A., Singh, V. P., & Gore, M. M. (2021). Improved detection of coronary artery disease using dt-rfe based feature selection and ensemble learning. In International conference on advanced network technologies and intelligent computing (pp. 425–440). Springer.

  • Tyagi, A., Singh, V. P., & Gore, M. M. (2022). Machine learning approaches for the detection of schizophrenia using structural mri. In International conference on advanced network technologies and intelligent computing (pp. 423–439). Springer.

  • Tyagi, A., Singh, V. P., & Gore, M. M. (2023). Towards artificial intelligence in mental health: A comprehensive survey on the detection of Schizophrenia. Multimedia Tools and Applications, 82(13), 20343–20405.

  • Tyagi, A., Singh, V. P., & Gore, M. M. (2023). An efficient automated detection of schizophrenia using k-nn and bag of words features. SN Computer Science, 4(5), 518.

    Article  Google Scholar 

  • Vyškovskỳ, R., Schwarz, D., Churová, V., & Kašpárek, T. (2022). Structural mri-based schizophrenia classification using autoencoders and 3d convolutional neural networks in combination with various pre-processing techniques. Brain Sciences, 12(5), 615.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, L. (2019). Eeg signals classification using machine learning for the identification and diagnosis of schizophrenia. In 2019 41st annual international conference of the ieee engineering in medicine and biology society (EMBC) (pp. 4521–4524). IEEE.

  • Zhang, J., Yang, H., Li, W., Li, Y., Qin, J., & He, L. (2022). Automatic schizophrenia detection using multimodality media via a text reading task. Frontiers in Neuroscience, 16, 933049.

  • Zhang, J., Zhongde, P., Chao, G., Jie, Z., & Donghong, C. (2016). Clinical investigation of speech signal features among patients with Schizophrenia. Shanghai Archives of Psychiatry, 28(2), 95.

    PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

The author contributions are listed below. Ashima Tyagi: Conceptualization, Methodology, Software, Investigation, and formal analysis Vibhav Prakash Singh: Conceptualization, Methodology, Validation, and Supervision Manoj Madhava Gore: Resources, Supervision, and Validation

Corresponding author

Correspondence to Ashima Tyagi.

Ethics declarations

Conflict of Interest

No conflict of interest.

Ethical Approval

This article does not incorporate research involving human subjects or animals conducted by any of the authors.

Competing Interests

The authors declare no competing interests.

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 (e.g. a society or other partner) 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

Tyagi, A., Singh, V.P. & Gore, M.M. Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning. Neuroinform 22, 499–520 (2024). https://doi.org/10.1007/s12021-024-09684-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-024-09684-4

Keywords

Navigation