Detection of newborn EEG seizure using optimal features based on discrete wavelet transform

Pega Zarjam, Mostefa Mesbah, Boualem Boashash

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

A new automated method is proposed to detect seizure events in newborns from Electroencephalogram (EEG) data. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimal feature subset is obtained using the mutual information evaluation function (MIEF). The MIEF algorithm evaluates a set of candidate features extracted from WCs to select an informative feature subset. The subset is then fed to an artificial neural network (ANN) classifier that organizes the EEG signal into seizure or non-seizure activity. The performance of the proposed features is compared with that of the features obtained using mutual information feature selection (MIPS) algorithm. The training and test sets are obtained from EEG data acquired from 5 neonates with ages ranging from 2 days to 2 weeks.

Original languageEnglish
Pages (from-to)265-268
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
Publication statusPublished - 2003

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Discrete wavelet transforms
Electroencephalography
Function evaluation
Set theory
Feature extraction
Classifiers
Neural networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

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