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Characterization of Focal Seizures in Scalp Electroencephalograms Based on Energy of Signal and Time-Frequency Analysis

  • Alexander Cerquera
  • Laura V. Guío
  • Elías Buitrago
  • Rafael M. Gutiérrez
  • Carlos Medina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

This work presents a method for characterization of focal seizures from scalp digital electroencephalograms (EEG) obtained in the Central League Against Epilepsy in Bogotá, which were acquired from patients between 13 and 53 years old. This characterization was performed in segments of 500 ms with presence of focal seizures that had been initially identified and labeled by a specialist during their visual examination. After selection of the segments and channels for analysis, the energy of the signals were calculated with the idea that the energy of focal seizures could be larger than the one of their side waves in the segments. This procedure produced peaks of energy corresponding to the seizures and, some times, to noise and artifacts. In order to identify the peaks of energy of the seizures an analysis with continuous wavelet transform was performed. It was found that the mother wavelet ‘bior2.2’ allowed more easily the identification of such seizures from the seventh scale of the analysis. The method allowed the identification of the 65% of the seizures labeled by the specialist.

Keywords

Continuous Wavelet Transform Epileptiform Activity Focal Seizure Focal Epilepsy Biorthogonal Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexander Cerquera
    • 1
  • Laura V. Guío
    • 2
  • Elías Buitrago
    • 3
  • Rafael M. Gutiérrez
    • 3
  • Carlos Medina
    • 2
  1. 1.Faculty for Electronic and Biomedical Engineering, Complex Systems GroupAntonio Nariño UniversityBogotáColombia
  2. 2.Central League Against EpilepsyBogotáColombia
  3. 3.Complex Systems GroupAntonio Nariño UniversityBogotáColombia

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