Analysis of Electroencephalogram on Children with Epilepsy Using Global Wavelet Spectrum

  • Salko Zahirović
  • Nedis Dautbašić
  • Maja Muftić DedovićEmail author
  • Smail Zubčević
  • Samir Avdaković
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 3)


The electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain and is used as the method of choice for the diagnosis of epilepsy. Nowadays, we can find dozens of EEG signal analysis papers using mathematical approach and with a focus on identification of epilepsy. This paper presents some results relating to the analysis of EEG on children using the Global Wavelet Spectrum (GWS). The signals are analyzed and collected on the UKCS during 2015 and 2016 using GWS. To be able to make comparison, EEG signals are gathered from both patients with and without epilepsy. Using this approach it is possible to clearly differentiate patients with a diagnosis of epilepsy from healthy ones.


  1. 1.
    Zahirovic S, Zubcevic S, Avdakovic S, Dautbasic N, Muftic Dedovic M (2015) Analysis of electroencephalogram report using the wavelet transform. J Neurol Surg A Cent Eur Neurosurg 76:A094Google Scholar
  2. 2.
    Omerhodžić I, Avdaković S, Nuhanović A, Dizdarević K (2010) Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. Int J Biol Life Sci 6:210–216Google Scholar
  3. 3.
    Barkmeier DT, Senador D, Leclercq K, Pai D, Hua J, Boutros NN, Kaminski RM, Loeb JA (2012) Electrical, molecular and behavioral effects of interictal spiking in the rat. Neurobiol Dis 92–101Google Scholar
  4. 4.
    Patnaika LM, Manyamb OK (2008) Epileptic EEG detection using neural networks and post-classification. Elsevier 91:100–109Google Scholar
  5. 5.
    Daubechies I (1992) Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, PhiladelphiaCrossRefzbMATHGoogle Scholar
  6. 6.
    He H, Starzyk JA (2006) A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans Power Delivery 21(1):286–295CrossRefGoogle Scholar
  7. 7.
    Mallat S (1998) A wavelet tour of signal processing. Academic, San Diego, CAzbMATHGoogle Scholar
  8. 8.
    Morales C, Ronquillo–Jarillo G, Campos–Enríquez JO (2009) Multi-scale analysis of well–logging data in petrophysical and stratigraphic correlation. GeofisicaGoogle Scholar
  9. 9.
    Avdakovic S, Omerhodzic I, Badnjevic A, Boskovic D (2015) Diagnosis of epilepsy from EEG signals using global wavelet power spectrum. In: 6th European conference of the international federation for medical and biological engineering. Springer International Publishing, pp 481–484Google Scholar
  10. 10.
    Ibrić S, Avdaković S, Omerhodžić I, Suljanović N, Mujčić A (2015) Diagnosis of epilepsy from EEG signals using Hilbert Huang transform. Folia Med Fac Univ Saraeviensis 50(1):68–73Google Scholar
  11. 11.
    Subasi A, Alkan A, Koklukaya E, Kiymik MK (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093CrossRefGoogle Scholar
  12. 12.
    Subasi A, Ercelebi E (2005) Classification of EEG signals using neural network and logistic regression, Comput Methods Programs Biomed 87–99Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Salko Zahirović
    • 1
  • Nedis Dautbašić
    • 1
  • Maja Muftić Dedović
    • 1
    Email author
  • Smail Zubčević
    • 1
  • Samir Avdaković
    • 1
  1. 1.University of SarajevoSarajevoBosnia and Herzegovina

Personalised recommendations