Skip to main content
SpringerLink
Log in

A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG

  • Original Article
  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.

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

Similar content being viewed by others

References

  1. Chatrian E, Bergamini L, Dondey M, Klass DW, Lennox-Buchthal M, Petersen I (1974) A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr Clin Neurophysiol 37:538–548

    Article  PubMed  Google Scholar 

  2. Kalaycı T, Özdamar Ö (1995) Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Eng Med Biol 14(2):160–166

    Article  Google Scholar 

  3. Ktonas PY (1987) Methods of analysis of brain electrical and magnetic signals (EEG Handbook). Elsevier, Amsterdam, pp 211–241

    Google Scholar 

  4. Frost D (1985) Automatic recognition and characterization of epileptiform discharges in human EEG. J Clin Neurophysiol 2(3):231–249

    Article  PubMed  Google Scholar 

  5. Glover R, Raghavan N, Ktonas PY, Frost JD (1989) Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives. IEEE Trans Biomed Eng 36(5):519–527

    Article  PubMed  Google Scholar 

  6. Ko CW, Lin YD, Chang HW, Jan GJ (1998) An EEG spike detection algorithm using ANN with multi-channel correlation. In: Proceedings of the 20th annual international conference of the IEEE engineering in medicene and biological society, pp 2070–2073

  7. Dingle AA, Jones RD, Carroll GJ, Fright WR (1993) A multistage system to detect epileptiform activity in the EEG. IEEE Trans Biomed Eng 40:1260–1268

    Article  PubMed  CAS  Google Scholar 

  8. Gabor AJ, Seyal M (1992) Automated interictal EEG spike detection using artificial neural networks. Electroencephalogr Clin Neurophysiol 83:271–280

    Article  PubMed  CAS  Google Scholar 

  9. Shimada T, Shiina T, Saito Y (2000) Detection of characteristic waves of sleep EEG by neural network analysis. IEEE Trans Biomed Eng 47(3):369–379

    Article  PubMed  CAS  Google Scholar 

  10. Ko CW, Chung HW (2000) Automatic spike detection via an artificial neural network using raw EEG data: effect of data preparation and implications in the limitations of online recognition. Clin Neurophysiol 111:477–481

    Article  PubMed  CAS  Google Scholar 

  11. Eberhart R, Dobbins R, Webber WRS (1989) Neural network design considerations for EEG spike detection. In: Proceedings 15th northeast bioengineering conference. Boston, MA, pp 97–98

  12. Webber WRS, Litt B, Wilson K, Lesser R (1994) Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized data. Electroencephalogr Clin Neurophysiol 91:194–204

    Article  PubMed  CAS  Google Scholar 

  13. Liu HS, Zhang T, Yang FS (2002) A multistage, multimethod approach for automatic detection and classification of epileptiform EEG. IEEE Trans Biomed Eng 9(12):1557–1566

    Google Scholar 

  14. Acır N, Öztura İ, Kuntalp M, Baklan B, Güzeliş C (2005) Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks. IEEE Trans Biomed Eng 52(1):30–40

    Article  PubMed  Google Scholar 

  15. Özdamar Ö, Kalayci T (1998) Detection of spikes with artificial neural networks using raw EEG. Comput Biomed Res 31:122–142

    Article  PubMed  Google Scholar 

  16. Acır N, Güzeliş C (2004) Automatic spike detection in EEG by a two-stage procedure based on support vector machines. Comput Biol Med 34:561–575

    Article  PubMed  Google Scholar 

  17. Ham FM, Hwang JN (2001) Principles of neurocomputing for science and engineering. McGraw Hill, NY

    Google Scholar 

  18. Hu YH, Hwang JN (2001) Handbook of neural network signal processing. CRC Press, NY

    Google Scholar 

  19. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, NJ

    MATH  Google Scholar 

  20. Bertsekas DP (1995) Nonlinear programming. Ayhena Scientific, Belmont, MA

    MATH  Google Scholar 

  21. Zurada JM (1992) Introduction to artificial neural systems. PWS Publishing Company, Boston, MA

    Google Scholar 

  22. Haykin S (1996) Adaptive filter theory. Prentice Hall, NJ

    Google Scholar 

  23. Özdamar Ö, Lopez C, Yaylali I (1991) Multilevel neural network system for EEG spike detection. In: Computer based medical systems. IEEE Computer Society Press, NY, pp272–279

  24. Hostetler W, Doller H, Homan W (1992) Assessment of a computer program to detect epileptiform spikes. Electroencephalogr Clin Neurophysiol 83:1–11

    Article  PubMed  CAS  Google Scholar 

  25. James CJ, Jones RD, Bones PJ, Carroll GJ (1999) Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages. Clin Neurophysiol 110:2049–2063

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The author gratefully acknowledges the help of Neurology Department staff of the Dokuz Eylül University in the preparation and the evaluation of EEG database.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Nigde University, 51245, Nigde, Turkey

    Nurettin Acır

Authors
  1. Nurettin Acır
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nurettin Acır.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Acır, N. A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG. Neural Comput & Applic 15, 49–54 (2006). https://doi.org/10.1007/s00521-005-0007-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-005-0007-9

Keywords

Search

Navigation