Detection of newborns' EEG seizure using time-frequency divergence measures

Pega Zarjam, Ghasem Azemi, Mostefa Mesbah, Boualem Boashash

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this paper, a time-frequency approach for detecting seizure activities in newborns Electroencephalogram (EEG) data is proposed. In this approach, the discrimination between seizure and non-seizure states is based on the time-frequency distance between the consequent segments in the EEG signal. Three different time-frequency measures and three different reduced time-frequency distributions are used in this study. The proposed method is tested on the EEG data acquired from three neonates with ages ranging from two days to two weeks. The experimental results validate the suitability of the proposed method in automated newborns' seizure detection. The results show an average seizure detection rate of 96% and false alarm rate of 5%.

Original languageEnglish
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
Publication statusPublished - 2004

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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