Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia


Infantile spasms (ISS) is a devastating epileptic syndrome that affects children under the age of 1 year. The diagnosis of ISS is based on the semiology of the seizure and the electroencephalogram (EEG) background characterized by hypsarrhythmia (HYPS). However, even skilled electrophysiologists may interpret the EEG of children with ISS differently, and commercial software or existing epilepsy detection algorithms are not helpful. Since EEG is a key factor in the diagnosis of ISS, misinterpretation could result in serious consequences including inappropriate treatment. In this paper, we developed a novel algorithm to localize the relevant electrical abnormality known as epileptic discharges (or spikes) to provide a quantitative assessment of ISS in HYPS. The proposed algorithm extracts novel time–frequency features from the EEG signals and localizes the epileptic discharges associated with ISS in HYPS using a support vector machine classifier. We evaluated the proposed method on an EEG dataset with ISS subjects and obtained an average true positive and false negative of 98 and 7%, respectively, which was a significant improvement compared to the results obtained using the clinically available software. The proposed automated method provides a quantitative assessment of ISS in HYPS, which could significantly enhance our knowledge in therapy management of ISS.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Baram TZ (2007) Models for infantile spasms: an arduous journey to the holy grail. Ann Neurol 61(2):89–91

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Battenberg E, Wessel D (2009) Accelerating nonnegative matrix factorization for audio source separation on multi-core and many-core architectures. In: ISMIR, pp 501–506

  3. 3.

    Becker JM, Sohn C, Rohlfing C (2014) NMF with spectral and temporal continuity criteria for monaural sound source separation. In: 2014 Proceedings of the 22nd European signal processing conference (EUSIPCO). IEEE, pp 316–320

  4. 4.

    Boashash B (2003) Time frequency analysis. Gulf Professional Publishing, Houston

    Google Scholar 

  5. 5.

    Cai S, Yang S, Zheng F, Lu M, Wu Y, Krishnan S (2013) Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion. Comput Math Methods Med 2013:904267

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Cohen L (1995) Time–frequency analysis, vol 1. Prentice Hall, Upper Saddle River

    Google Scholar 

  7. 7.

    Ghoraani B, Krishnan S (2011) Time–frequency matrix feature extraction and classification of environmental audio signals. IEEE Trans Audio Speech Lang Process 19(7):2197–2209

    Article  Google Scholar 

  8. 8.

    Ghoraani B, Krishnan S (2009) A joint time-frequency and matrix decomposition feature extraction methodology for pathological voice classification. EURASIP J Adv Signal Process (ID 928974). doi:10.1155/2009/928974

  9. 9.

    Hunter DR, Lange K (2004) A tutorial on mm algorithms. Am Stat 58:30–37

    Article  Google Scholar 

  10. 10.

    Hussain SA, Kwong G, Millichap JJ, Mytinger JR, Ryan N, Matsumoto JH, Wu JY, Lerner JT, Sankar R (2015) Hypsarrhythmia assessment exhibits poor interrater reliability: a threat to clinical trial validity. Epilepsia 56(1):77–81

    Article  PubMed  Google Scholar 

  11. 11.

    Hwang WJ, Wang SH, Hsu YT (2014) Spike detection based on normalized correlation with automatic template generation. Sensors 14(6):11049–11069

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Jeavons PM, Bower BD (1961) The natural history of infantile spasms. Arch Dis Child 36(185):17

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Jones RD, Dingle A, Carroll GJ, Green RD, Black M, Donaldson IM, Parkin PJ, Bones PJ, Burgess KL et al (1996) A system for detecting epileptiform discharges in the EEG: real-time operation and clinical trial. In: Engineering in medicine and biology society, 1996. Bridging disciplines for biomedicine. Proceedings of the 18th annual international conference of the IEEE, vol 3. IEEE, pp 948–949

  14. 14.

    Kameoka H, Ono N, Kashino K, Sagayama S (2009) Complex NMF: A new sparse representation for acoustic signals. In: IEEE international conference on Acoustics, speech and signal processing, 2009. ICASSP 2009. IEEE, pp 3437–3440

  15. 15.

    Kim S, McNames J (2007) Automatic spike detection based on adaptive template matching for extracellular neural recordings. J Neurosci Methods 165(2):165–174

    Article  PubMed  Google Scholar 

  16. 16.

    Liu YC, Lin CCK, Tsai JJ, Sun YN (2013) Model-based spike detection of epileptic EEG data. Sensors 13(9):12536–12547

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Lodder SS, Askamp J, van Putten MJ (2013) Inter-ictal spike detection using a database of smart templates. Clin Neurophysiol 124(12):2328–2335

    Article  PubMed  Google Scholar 

  18. 18.

    Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE TSP 41(12):3397–3415

    Google Scholar 

  19. 19.

    Nenadic Z, Burdick JW (2005) Spike detection using the continuous wavelet transform. IEEE Trans Biomed Eng 52(1):74–87

    Article  PubMed  Google Scholar 

  20. 20.

    Pellock JM, Hrachovy R, Shinnar S, Baram TZ, Bettis D, Dlugos DJ, Gaillard WD, Gibson PA, Holmes GL, Nordli DR et al (2010) Infantile spasms: a US consensus report. Epilepsia 51(10):2175–2189

    Article  PubMed  Google Scholar 

  21. 21.

    Riikonen R (2001) Epidemiological data of West syndrome in Finland. Brain Dev 23(7):539–541

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Tan VYF, Févotte C (2013) Automatic relevance determination in nonnegative matrix factorization with the/spl beta/-divergence. IEEE Trans Pattern Anal Mach Intell 35(7):1592–1605

    Article  PubMed  Google Scholar 

  23. 23.

    Tjoa SK, Liu KR (2010) Multiplicative update rules for nonnegative matrix factorization with co-occurrence constraints. In: 2010 IEEE international conference on acoustics speech and signal processing (ICASSP). IEEE, pp 449–452

  24. 24.

    Traitruengsakul S, Seltzer L, Paciorkowski AR, Ghoraani B (2015) Automatic localization of epileptic spikes in eegs of children with infantile spasms. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 6194–6197

  25. 25.

    Zaveri HP, Duckrow RB, Spencer SS (2006) On the use of bipolar montages for time-series analysis of intracranial electroencephalograms. Clin Neurophysiol 117(9):2102–2108

    Article  PubMed  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Behnaz Ghoraani.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Traitruengsakul, S., Seltzer, L.E., Paciorkowski, A.R. et al. Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia. Med Biol Eng Comput 55, 1659–1668 (2017).

Download citation


  • Hypsarrythmia
  • Time–frequency representations
  • Nonnegative matrix factorization
  • Feature extraction
  • Classification