Comparative performance of time-frequency based newborn EEG seizure detection using spike signatures

Hamid Hassanpour, Mostefa Mesbah, Boualem Boashash

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper investigates the performance of four non-parametric newborn EEG seizure detection methods. The authors recently proposed a time-frequency (TF) based technique suitable for nonstationarity of EEG signal. This method attempts to detect seizure activities through analysing the interspike intervals of the EEG in the TF domain. The performance of this method is compared to those of three nonparametric techniques for seizure detection. These methods are: Autocorrelation, Spectrum and Singular Spectrum Analysis (SSA). The Autocorrelation method performs analysis in the time domain and is based on the autocorrelation function of short epochs of EEG data. The Spectrum technique is based on spectral analysis and is used to detect periodic discharges. The SSA technique employs singular spectrum analysis and information theoretic-based selection of the signal subspace. These three methods are based on the assumption that newborn EEG signal is quasi-stationary. The obtained results show the superior performance of the TF-based technique for detecting newborn EEG seizures.

Original languageEnglish
Pages (from-to)389-392
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
Publication statusPublished - 2003

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Electroencephalography
Spectrum analysis
Autocorrelation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

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