Time-frequency feature extraction of newborn EEC seizure using SVD-based techniques

Hamid Hassanpour, Mostefa Mesbah, Boualem Boashash

Research output: Contribution to journalArticle

90 Citations (Scopus)

Abstract

The nonstationary and multicomponent nature of newborn EEC 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 EEC seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEC 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.

Original languageEnglish
Pages (from-to)2544-2554
Number of pages11
JournalEurasip Journal on Applied Signal Processing
Volume2004
Issue number16
DOIs
Publication statusPublished - Nov 15 2004

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Singular value decomposition
Feature extraction
Distribution functions
Neural networks
European Union

Keywords

  • Detection
  • Probability distribution function
  • Singular value decomposition
  • Time-frequency distribution

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Signal Processing

Cite this

Time-frequency feature extraction of newborn EEC seizure using SVD-based techniques. / Hassanpour, Hamid; Mesbah, Mostefa; Boashash, Boualem.

In: Eurasip Journal on Applied Signal Processing, Vol. 2004, No. 16, 15.11.2004, p. 2544-2554.

Research output: Contribution to journalArticle

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