Open Access

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

EURASIP Journal on Advances in Signal Processing20042004:898124

Received: 27 August 2003

Published: 2 December 2004


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.

Keywords and phrases

detectiontime-frequency distributionsingular value decompositionprobability distribution function

Authors’ Affiliations

Lab of Signal Processing Research, Queensland University of Technology


© Hassanpour et al. 2004