EURASIP Journal on Applied Signal Processing 
Volume 2004 (2004), Issue 16, Pages 2544-2554
doi:10.1155/S1110865704406167

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

Hamid Hassanpour, Mostefa Mesbah, and Boualem Boashash

 Lab of Signal Processing Research, Queensland University of Technology, GPO Box 2434, Brisbane 4001, QLD, Australia
Received 27 August 2003; Revised 17 May 2004

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.