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Epileptic seizure detection using fuzzy-rules-based sub-band specific features and layered multi-class SVM

  • S. Ramakrishnan
  • A. S. Muthanantha Murugavel
Theoretical Advances
  • 93 Downloads

Abstract

In this paper, a new epileptic seizure detection method using fuzzy-rules-based sub-band specific features and layered directed acyclic graph support vector machine (LDAG-SVM) is proposed for classification of electroencephalogram (EEG) signals. Wavelet transformation is used to decompose the input EEG signals into various sub-bands. The nonlinear features, namely approximate entropy, largest Lyapunov exponent and correlation dimension, are extracted from each sub-band. In this proposed work, sub-band specific feature subset that is reduced in size and capable of discriminating samples is selected by employing fuzzy rules. For classification purpose, a new LDAG-SVM is used for detecting epileptic seizure. Every sub-band has its own characteristics. If appropriate features which characterize the specific sub-band are selected, then the classification accuracy is improved and computational complexity is reduced. The important advantage of the fuzzy logic is its close relation to human thinking. Due to the lengthy record and intra-professional variability, automation of epileptic detection is inevitable. Fuzzy rules are the natural choice of employing human expertise to build machine learning system. Performances of the proposed methods are evaluated using two different benchmark EEG datasets, namely Bonn and CHB-MIT. The performance measures such as classification accuracy, sensitivity, specificity, execution time and receiver operating characteristics are used to measure and analyze the performances of the proposed classifier. The proposed LDAG-SVM with fuzzy-rules-based selected sub-band specific features provides better performance in terms of improved classification accuracy with reduced execution time compared to existing methods.

Keywords

EEG classification Feature selection Fuzzy rules Wavelet transformation Seizure detection Support vector machine 

Notes

Acknowledgment

The authors wish to thank Council of Scientific & Industrial Research (CSIR) for granting this research project (Sanction Letter Ref. No. 22(0726)/17/EMR-II). Also authors would like to thank the Management, Secretary and Principal of our institution for supporting us during this research work.

References

  1. 1.
    Fisher R, van Emde BW, Blume W, Elger C, Genton P, Lee P, Engel J (2010) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46(4):470–472CrossRefGoogle Scholar
  2. 2.
    Ruiz RAS, Ranta R, Louis-Dorr V (2013) EEG montage analysis in the blind source separation framework. Biosignal Process Control 6(1):77–84CrossRefGoogle Scholar
  3. 3.
    Coyle D, McGinnity TM, Prasad G (2012) Improving the separability of multiple EEG features for a BCI by neural-time-series-prediction-preprocessing. Biosignal Process Control 5(3):196–204CrossRefGoogle Scholar
  4. 4.
    Ince NF, Goksu F, Tewfik AH, Arica S (2009) Adapting subject specific motor imagery EEG patterns in space–time–frequency for a brain computer interface. Biosignal Process Control 4(3):236–246CrossRefGoogle Scholar
  5. 5.
    Guler I, Ubeyli ED (2009) Multiclass support vector machines for EEG-signals classification. IEEE Trans Inf Technol Biomed 11(2):117–126CrossRefGoogle Scholar
  6. 6.
    Muthanantha Murugavel AS, Ramakrishnan S (2014) An optimized extreme learning machine for epileptic seizure detection. IAENG Int J Comput Sci 41(4):212–221Google Scholar
  7. 7.
    Liu A, Hahn JS, Heldt GP, Coen RW (1992) Detection of neonatal seizures through computerized EEG analysis. Electroencephalogr Clin Neurophysiol 82:30–37CrossRefGoogle Scholar
  8. 8.
    Srinivasan V, Eswaran C, Sriraam N (2005) Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst 29(6):647–660CrossRefGoogle Scholar
  9. 9.
    Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123:69–87CrossRefGoogle Scholar
  10. 10.
    Khan YU, Gotman J (2003) Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin Neurophysiol 114:898–908CrossRefGoogle Scholar
  11. 11.
    Zarjam P, Mesbah M, Boashash B (2003) Detection of newborns EEG seizure using optimal features based on discrete wavelet transform. Proc IEEE Int Conf Acoust Speech Signal Process 2:265–268Google Scholar
  12. 12.
    Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036CrossRefGoogle Scholar
  13. 13.
    Niknazar M, Mousavi SR, Vosoughi Vahdat B, Sayyah M (2013) A new framework based on recurrence quantification analysis for epileptic seizure detection. IEEE J Biomed Health Inform 17(3):572–578CrossRefGoogle Scholar
  14. 14.
    Kannathal N, Choo M, Acharya U, Sadasivan P (2005) Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed 80(3):187–194CrossRefGoogle Scholar
  15. 15.
    Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Muthanantha Murugavel AS, Ramakrishnan S (2016) Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification. Med Biol Eng Comput Springer 54(1):149–161.  https://doi.org/10.1007/s11517-015-1351-2 CrossRefGoogle Scholar
  17. 17.
    Vairavan S, Chikkannan E, Natarajan S (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288–295CrossRefGoogle Scholar
  18. 18.
    Hojjat A, Samanwoy G, Nahid D (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng 54(9):1545–1551CrossRefGoogle Scholar
  19. 19.
    Lan-Lan C, Jian Z, Jun-Zhong Z, Chen-Jie Z, Gui-Song W (2014) A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection. Biomed Signal Process Control 10:1–10CrossRefGoogle Scholar
  20. 20.
    Hsu K, Yu S (2010) Detection of seizures in EEG using sub band nonlinear parameters and genetic algorithm. Comput Biol Med 40(10):823–830CrossRefGoogle Scholar
  21. 21.
    Guo L, Rivero D, Dorado J, Munteanu C, Pazos A (2011) Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst Appl 38(8):10425–10436CrossRefGoogle Scholar
  22. 22.
    Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machine. Expert Syst Appl 37:8659–8666CrossRefGoogle Scholar
  23. 23.
    Hoquea N, Bhattacharyyaa DK, Kalitab JK (2014) MIFS-ND: a mutual information-based feature selection method. Expert Syst Appl, Elsevier 41(14):6371–6385CrossRefGoogle Scholar
  24. 24.
    Swingle B (2012) Entropy, mutual information, and fluctuation properties of Fermi liquids. Phys Rev B 86(4):045109MathSciNetCrossRefGoogle Scholar
  25. 25.
    Ma Z, Tan ZH, Guo J (2016) Feature selection for neutral vector in EEG signal classification. Neurocomput, Elsevier B 174(24):937–945CrossRefGoogle Scholar
  26. 26.
    Xiang J, Li C, Li H, Cao R, Wang B, Han X, Chen J (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18–25CrossRefGoogle Scholar
  27. 27.
    Chandaka S, Chatterjee A, Munshi S (2009) Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst Appl 36(2):1329–1336CrossRefGoogle Scholar
  28. 28.
    Ubeyli E (2006) Analysis of EEG signals using Lyapunov exponents. Neural Netw World 16(3):257–273Google Scholar
  29. 29.
    Moustakidis S, Mallinis G, Koutsias N, Theocharis JB (2012) SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans Geosci Remote Sens 50(1):149–168CrossRefGoogle Scholar
  30. 30.
    Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE, Ralph KL (2001) 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 64:061907-1–061907-8CrossRefGoogle Scholar
  31. 31.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  32. 32.
    Poorna BR, Subith K (2015) Medical diagnostic system using fuzzy logic. Int J Latest Trends Eng Technol 5(1):307–310Google Scholar
  33. 33.
    Tzallas A, Tsipouras M, Fotiadis D (2007) Automatic seizure detection based on time–frequency analysis and artificial neural networks. Comput Intell Neurosci 13: Article ID 80510Google Scholar
  34. 34.
    Ubeyli ED (2010) Least square support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Syst Appl 37:233–239CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyDr. Mahalingam College of Engineering and TechnologyPollachiIndia

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