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  • Research Article
  • Open Access

Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

EURASIP Journal on Advances in Signal Processing20042004:898124

https://doi.org/10.1155/S1110865704406167

  • Received: 27 August 2003
  • Published:

Abstract

The nonstationary and multicomponent nature of newborn EEG seizures tends to increase the complexity of the seizure detection problem. In dealing with this type of problems, time-frequency-based techniques were shown to outperform classical techniques. This paper presents a new time-frequency-based EEG seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEG epoch to characterise the patterns embedded in the signal. The estimated distribution functions related to seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.

Keywords and phrases

  • detection
  • time-frequency distribution
  • singular value decomposition
  • probability distribution function

Authors’ Affiliations

(1)
Lab of Signal Processing Research, Queensland University of Technology, GPO Box 2434, Brisbane, QLD, 4001, Australia

Copyright

© Hassanpour et al. 2004

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