Advances in Automated Neonatal Seizure Detection

  • Eoin M. Thomas
  • Andrey Temko
  • Gordon Lightbody
  • William P. Marnane
  • Geraldine B. Boylan
Part of the Studies in Computational Intelligence book series (SCI, volume 372)


This chapter highlights the current approaches in automated neonatal seizure detection and in particular focuses on classifier based methods. Automated detection of neonatal seizures has the potential to greatly improve the outcome of patients in the neonatal intensive care unit. The electroencephalogram (EEG) is the only signal on which 100% of electrographic seizures are visible and thus is considered the gold standard for neonatal seizure detection. Although a number of methods and algorithms have been proposed previously to automatically detect neonatal seizures, to date their transition to clinical use has been limited due to poor performances mainly attributed to large inter and intra-patient variability of seizure patterns and the presence of artifacts. Here, a novel detector is proposed based on time-domain, frequency-domain and information theory analysis of the signal combined with pattern recognition using machine learning principles. The proposed methodology is based on a classifier with a large and diverse feature set and includes a post-processing stage to incorporate contextual information of the signal. It is shown that this methodology achieves high classification accuracy for both classifiers and allows for the use of soft decisions, such as the probability of seizure over time, to be displayed.


Neonatal EEG analysis biomedical signal classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Clancy, R.: Pediatrics 117, 23 (2006)Google Scholar
  2. 2.
    Evans, D., Levene, M.: Arch. Dis. Child. Fetal Neonatal Ed. 78, 70 (1998)CrossRefGoogle Scholar
  3. 3.
    Saliba, R.M., Annegers, J.F., Waller, D.K., Tyson, J.E., Mizrahi, E.: American Journal of Epidemiology 154(1), 14 (2001)CrossRefGoogle Scholar
  4. 4.
    Bye, A.M.E., Flanagan, D.: Epilepsia 36(10), 1009 (1995)CrossRefGoogle Scholar
  5. 5.
    Rennie, J.M., Chorley, G., Boylan, G.B., Pressler, R., Nguyen, Y., Hooper, R.: Arch. Dis. Child Fetal Neonatal Ed 89, 37 (2004)CrossRefGoogle Scholar
  6. 6.
    De Weerd, A.W.: Atlas of EEG in the first months of life. Elsevier Science Ltd., Amsterdam (1995)Google Scholar
  7. 7.
    Shellhaas, R., Clancy, R.: Clin. Neurophysiol. 118(10), 2156 (2007)CrossRefGoogle Scholar
  8. 8.
    Kitayama, M., Otsubo, H., Parvez, S., Lodha, A., Ying, E., Parvez, B., Ishii, R., Mizuno-Matsumoto, Y., Zoroofi, R.A., Snead, O.C.: Pediatric Neurology 29(4), 326 (2003)CrossRefGoogle Scholar
  9. 9.
    Navakatikyan, M.A., Colditz, P.B., Bruke, C.J., Inder, T.E., Richmond, J., Williams, C.E.: Clin. Neurophysiol. 117(6), 1190 (2006)CrossRefGoogle Scholar
  10. 10.
    Deburchgraeve, W., Cherian, P., Vos, M.D., Swarte, R., Blok, J., Visser, G., Govaert, P., Huffel, S.V.: Clin. Neurophysiol. 119(11), 2447 (2008)CrossRefGoogle Scholar
  11. 11.
    Aarabi, A., Wallois, F., Grebe, R.: Clin. Neurophysiol. 117(2), 328 (2006)CrossRefGoogle Scholar
  12. 12.
    Aarabi, A., Grebe, R., Wallois, F.: Clin. Neurophysiol. 118(12), 2781 (2007)CrossRefGoogle Scholar
  13. 13.
    Greene, B.R., Marnane, W.P., Lightbody, G., Reilly, R.B., Boylan, G.B.: Physiol. Meas. 29, 1157 (2008)CrossRefGoogle Scholar
  14. 14.
    Mitra, J., Glover, J.R., Ktonas, P.Y., Kumar, A.T., Mukherjee, A., Karayiannis, N.B., Frost, J.D., Hrachovy, R.A., Mizrahi, E.M.: J. Clin. Neurophysiol. 26(4), 218 (2009)CrossRefGoogle Scholar
  15. 15.
    Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  16. 16.
    Shoeb, A., Edwards, H., Connolly, J., Bourgeois, B., Treves, S.T., Guttag, J.: Epilepsy and Behaviour 5, 483 (2004)CrossRefGoogle Scholar
  17. 17.
    AcIr, N., Güzelis, C.: Computers in Biology and Medicine 34(7), 561 (2004)CrossRefGoogle Scholar
  18. 18.
    Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: Journal of Machine Learning Research 7, 1025 (2006)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Runarsson, T., Sigurdsson, S.: Computational Intelligence for Modelling. Control and Automation 2, 673 (2005)Google Scholar
  20. 20.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Digital Signal Processing 10(1-3), 19 (2000)CrossRefGoogle Scholar
  21. 21.
    Zhu, X., Wu, J., Cheng, Y., Wang, Y.: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), pp. 1171–1174 (2006)Google Scholar
  22. 22.
    Marcel, S., Millan, J.: IEEE Trans. on Pattern Analysis and Machine Intelligence 29(4), 743 (2007)CrossRefGoogle Scholar
  23. 23.
    Meng, L., Frei, M., Osorio, I., Strang, G., Nguyen, T.: Med. Eng. Phys. 26(5), 379 (2004)CrossRefGoogle Scholar
  24. 24.
    Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer, New York (1982)zbMATHGoogle Scholar
  25. 25.
    Greene, B.R., Faul, S., Marnane, W.P., Lightbody, G., Korotchikova, I., Boylan, G.B.: Clin. Neurophysiol. 119(6), 1248 (2008)CrossRefGoogle Scholar
  26. 26.
    Shoeb, A., Bourgeois, B., Treves, S.T., Schachter, S.C., Guttag, J.: In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 4110–4114 (2007)Google Scholar
  27. 27.
    Gotman, J., Flanagan, D., Zhang, J., Rosenblatt, B.: Electroenceph. clin. Neurophysiol. 103, 356 (1997)CrossRefGoogle Scholar
  28. 28.
    Hjorth, B.: Electroencephalogr. Clin. Neurophysiol. 29(3), 306 (1970)CrossRefGoogle Scholar
  29. 29.
    D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., Litt, B.: IEEE Trans. Biomed. Eng. 50, 603 (2003)CrossRefGoogle Scholar
  30. 30.
    Esteller, R., Echauz, J., Tcheng, T., Litt, B., Pless, B.: Proceedings of the 23rd Annual EMBS International Conference, pp. 1707–1710 (2001)Google Scholar
  31. 31.
    Faul, S., Boylan, G.B., Connolly, S., Marnane, W.P., Lightbody, G.: Proceedings of the IEEE International Symposium on Intelligent Signal Processing, pp. 381–386 (2005)Google Scholar
  32. 32.
    Platt, J.: Advances in large margin classifiers, 61–74 (1999)Google Scholar
  33. 33.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1 (1977)MathSciNetzbMATHGoogle Scholar
  34. 34.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2001)zbMATHGoogle Scholar
  35. 35.
    Duchene, J., Leclercq, S.: IEEE Trans. on Pattern Analysis and Machine Intelligence 10(6), 978 (1988)CrossRefzbMATHGoogle Scholar
  36. 36.
    Thomas, E., Temko, A., Lightbody, G., Marnane, W., Boylan, G.: IEEE MLSP (2009)Google Scholar
  37. 37.
    Temko, A., Thomas, E., Boylan, G., Marnane, W., Lightbody, G.: IEEE EMBC (2009)Google Scholar
  38. 38.
    Wilson, S.B., Scheuer, M., Plummer, C., Young, B., Pacia, S.: Clin. Neurophysiol. 2156(114) (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eoin M. Thomas
    • 1
  • Andrey Temko
    • 1
  • Gordon Lightbody
    • 1
  • William P. Marnane
    • 1
  • Geraldine B. Boylan
    • 2
  1. 1.Department of Electrical and Electronic EngineeringUniversity College CorkIreland
  2. 2.School of MedicineUniversity College CorkIreland

Personalised recommendations