Skip to main content

Advertisement

SpringerLink
Log in

Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A–E, B–E, C–E, D–E, and A–D clusters, 100, 96, 100, 99.00 and 100 % were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ropper A, Brown RH (2005) Principles of neurology, 8th edn. McGraw-Hill, Boston

    Google Scholar 

  2. Altunay S, Telatar Z, Erogul O (2010) Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl 37(8):5661–5665

    Article  Google Scholar 

  3. Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (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(6):061907 (1–8)

    Article  Google Scholar 

  4. Fisher R, van EmdeBoas W, Blume W, Elger C, Genton P, Lee P, Engel J (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46(4):470–472

    Article  PubMed  Google Scholar 

  5. Yuan Q, Zhou W, Li S, Cai D (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96(1–2):29–38

    Article  PubMed  Google Scholar 

  6. Kaya Y, Uyar M, Tekin R, Yıldırım S (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. App Math Comp 243:209–219

    Article  Google Scholar 

  7. Siuly Y, Lin PW (2011) Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed 104(3):358–372

    Article  CAS  PubMed  Google Scholar 

  8. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59

    Article  Google Scholar 

  9. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Trans. Patt Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  10. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  11. Dash M, Liu H (1997) Feature Selection for Classification. Intell Data Anal 1:131–156

    Article  Google Scholar 

  12. Ng KS, Yang HY, Kim S (2009) Hidden pattern discovery on event related potential EEG signals. BioSystems 97:15–27

    Article  PubMed  Google Scholar 

  13. Chang W (2000) A comprehensive study of grey relational generating. J Chin Grey Assoc 1:53–62

    Google Scholar 

  14. Kayacan E, Ulutas B, Kaynak O (2010) Grey system theory-based models in time series prediction. Expert Syst Appl 37:1784–1789

    Article  Google Scholar 

  15. Liu SF, Lin Y (1998) An introduction to grey systems. IIGSS Academic Publisher, PA

    Google Scholar 

  16. Hsiao SW, Tsai HC (2004) Use of gray system theory in product-color planning. Color Res Appl 29(3):222–231

    Article  Google Scholar 

  17. Lin YH, Lee PC, Chang TP (2009) Practical expert diagnosis model based on the grey relational analysis technique. Expert Syst Appl 36:1523–1528

    Article  Google Scholar 

  18. Lin Y, Liu S (2004) A historical introduction to grey systems theory. In Proceedings of IEEE international conference on systems, man and cybernetics, The Netherlands 1: 2403–2408

  19. Deng JL (1982) Control problems of grey system. Syst Control Lett 1:288–294

    Article  Google Scholar 

  20. Deng JL (1989) Introduction to grey system theory. J Grey Syst 1:1–24

    Google Scholar 

  21. Lin Y, Lee H, Chang PC (2009) Practical expert diagnosis model based on the grey relational analysis technique. Expert Syst Appl 36(2):1523–1528

    Article  Google Scholar 

  22. Menéndez LÁ, Cos Juez FJ, Lasheras FS, Riesgo JA (2010) Artificial neural networks applied to cancer detection in a breast screening programme. Math Comput Model 52(7):983–991

    Article  Google Scholar 

  23. Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093

    Article  Google Scholar 

  24. Übeyli ED, Güler İ (2004) Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks. Eng Appl Artif Intel 17(6):567–576

    Article  Google Scholar 

  25. Kumar SP, Sriraam N, Benakop PG, Jinaga BC (2010) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 37(4):3284–3291

    Article  Google Scholar 

  26. Fathima T, Bedeeuzzaman M, Faroog O, Khan YU (2011) Wavelet based features for epileptic seizure detection. MES J Technol Manag 2(1):108–112

    Google Scholar 

  27. Guo L, Rivero D, Pazos S (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193(1):156–163

    Article  PubMed  Google Scholar 

  28. Nigam VP, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26(1):55–60

    Article  PubMed  Google Scholar 

  29. 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):647–660

    Article  CAS  PubMed  Google Scholar 

  30. Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38(10):13475–13481

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Siirt University, 56100, Siirt, Turkey

    Yılmaz Kaya

Authors
  1. Yılmaz Kaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yılmaz Kaya.

Ethics declarations

Conflict of interest

There is no ‘Conflict of Interest’ in the publication of the manuscript “Hidden Pattern Discovery on Epileptic EEG with 1-D Local Binary Patterns and Epileptic Seizures Detection by Grey Relational Analysis”.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaya, Y. Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis. Australas Phys Eng Sci Med 38, 435–446 (2015). https://doi.org/10.1007/s13246-015-0362-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-015-0362-5

Keywords

Search

Navigation