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

Hamid Hassanpour*, Mostefa Mesbah, Boualem Boashash

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

105 Citations (Scopus)


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
Issue number16
Publication statusPublished - Nov 15 2004
Externally publishedYes


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

ASJC Scopus subject areas

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


Dive into the research topics of 'Time-frequency feature extraction of newborn EEC seizure using SVD-based techniques'. Together they form a unique fingerprint.

Cite this