An Efficient Framework for the Analysis of Big Brain Signals Data

  • SupriyaEmail author
  • Siuly
  • Hua Wang
  • Yanchun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10837)


Big Brain Signals Data (BBSD) analysis is one of the most difficult challenges in the biomedical signal processing field for modern treatment and health monitoring applications. BBSD analytics has been recently applied towards aiding the process of care delivery and disease exploration. The main purpose of this paper is to introduce a framework for the analysis of BBSD of time series EEG in biomedical signal processing for identification of abnormalities. This paper presents a data analysis framework combining complex network and machine learning techniques for the analysis of BBSD in time series form. The proposed method is tested on an electroencephalogram (EEG) time series database as the implanted electrodes in the brain generate huge amounts of time series data in EEG. The pilot study in this paper has examined that the proposed methodology has the capability to analysis massive size of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the brain to care patients with better outcomes and also help technicians for making intelligent decisions system.


Big data Biomedical signal EEG Complex network Machine learning Feature extraction Classification 


  1. 1.
    Siuly, S., Li, Y., Zhang, Y.: EEG Signal Analysis and Classification: Techniques and Applications. Health Information Science. Springer Nature, Heidelberg (2016). (ISBN 978-3-319-47653-7)CrossRefGoogle Scholar
  2. 2.
    Siuly, S., Zhang, Y.: Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci. Eng. 1(2), 54–64 (2016)CrossRefGoogle Scholar
  3. 3.
    Derlatka, M., Pauk, J.: Data Mining in Analysis of Biomechanical Signals. Solid State Phenom. 147–149, 588–593 (2009)CrossRefGoogle Scholar
  4. 4.
    Belle, A., Thiagarajan, R., Soroushmehr, S.M.R., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. Biomed. Res. Int. 2015, 16 (2015). Article ID 370194CrossRefGoogle Scholar
  5. 5.
    Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., et al.: Addressing big data time series. ACM Trans. Knowl. Discov. Data 7(3), 1–31 (2013)CrossRefGoogle Scholar
  6. 6.
    Herland, M., Khoshgoftaar, T., Wald, R.: A review of data mining using big data in health informatics. J. Big Data 1(1), 2 (2014)CrossRefGoogle Scholar
  7. 7.
    Andrzejak, R., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64(6), 061907 (2001)CrossRefGoogle Scholar
  8. 8.
    Bache, K., Lichman, M.: UCI machine learning repository. University of California, Irvine, School of Information and Computer (2013).
  9. 9.
    Zhang, X., Begleiter, H., Porjesz, B., Wang, W., Litke, A.: Event related potentials during object recognition tasks. Brain Res. Bull. 38(6), 531–538 (1995)CrossRefGoogle Scholar
  10. 10.
    Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80(4), 046103 (2009)CrossRefGoogle Scholar
  11. 11.
    Supriya, S., Siuly, S., Wang, H., Zhuo, G., Zhang, Y.: Analyzing EEG signal data for detection of epileptic seizure: introducing weight on visibility graph with complex network feature. In: Cheema, M., Zhang, W., Chang, L. (eds.) Databases Theory and Applications. LNCS, vol. 9877, pp. 56–66. Springer, Cham (2016). Scholar
  12. 12.
    Supriya, S., Siuly, S., Zhang, Y.: Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network. Electron. Lett. 52(17), 1430–1432 (2016)CrossRefGoogle Scholar
  13. 13.
    Antoniou, I., Tsompa, E.: Statistical: analysis of weighted networks. Discrete Dyn. Nat. Soc. 2008, 1–16 (2008)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang, B., Zhang, Y., Begg, R.: Gait classification in children with cerebral palsy by Bayesian approach. Pattern Recogn. 42(4), 581–586 (2009)CrossRefGoogle Scholar
  15. 15.
    Cao, J., Wu, Z., Mao, B., Zhang, Y.: Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16(5–6), 729–748 (2012)Google Scholar
  16. 16.
    Siuly, Wang, H., Zhang, Y.: Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement 86, 148–158 (2016)CrossRefGoogle Scholar
  17. 17.
    Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3–24 (2007)Google Scholar
  18. 18.
    Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4, 6554–6566 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for Applied Informatics, College of Engineering and ScienceVictoria UniversityMelbourneAustralia

Personalised recommendations