Automatic characterization of dynamics in Absence Epilepsy

Katrine N. H. Petersen, Trine N. Nielsen, Troels W. Kjær, Carsten E. Thomsen, Helge B. D. Sorensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Dynamics of the spike-wave paroxysms in Childhood Absence Epilepsy (CAE) are automatically characterized using novel approaches. Features are extracted from scalograms formed by Continuous Wavelet Transform (CWT). Detection algorithms are designed to identify an estimate of the temporal development of frequencies in the paroxysms. A database of 106 paroxysms from 26 patients was analyzed. The database is large compared to other known studies in the field of dynamics in CAE. CWT is more efficient than the widely used Fourier transform due to CWTs ability to recognize smaller discontinuities and variations. The use of scalograms and the detection algorithms result in a potentially usable clinical tool for dividing CAE patients into subsets. Differences between the grouped paroxysms may turn out to be useful from a clinical perspective as a prognostic indicator or when adjusting drug treatment.
Original languageEnglish
Title of host publicationIEEE Engineering in medicine and biology society conference proceedings
PublisherIEEE
Publication date2013
Pages4283 - 4286
ISBN (Print)9781457702167
DOIs
Publication statusPublished - 2013
Event35th Annual International Conference of the IEEE EMBS - Osaka, Japan
Duration: 3 Jul 20137 Jul 2013

Conference

Conference35th Annual International Conference of the IEEE EMBS
CountryJapan
CityOsaka
Period03/07/201307/07/2013

Keywords

  • drugs
  • electroencephalography
  • feature extraction
  • Fourier transforms
  • medical disorders
  • medical signal detection
  • paediatrics
  • patient treatment
  • wavelet transforms
  • Engineered Materials, Dielectrics and Plasmas

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