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

Authors:
Hamid Hassanpour;Mostefa Mesbah;Boualem Boashash
Affiliations:
Lab of Signal Processing Research, Queensland University of Technology, Brisbane, QLD, Australia;Lab of Signal Processing Research, Queensland University of Technology, Brisbane, QLD, Australia;Lab of Signal Processing Research, Queensland University of Technology, Brisbane, QLD, Australia
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

Citing 5
Cited 7

Methods of signal classification using the images produced by the Wigner-Ville distribution

Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature sets for nonstationary signals derived from moments of thesingular value decomposition of Cohen-Posch (positive time-frequency)distributions

IEEE Transactions on Signal Processing
A high-resolution quadratic time-frequency distribution formulticomponent signals analysis

IEEE Transactions on Signal Processing
Neighborhood based Levenberg-Marquardt algorithm for neural network training

IEEE Transactions on Neural Networks

A time--frequency approach for noise reduction

Digital Signal Processing
Epileptic seizure prediction by a system of particle filter associated with a neural network

EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Time-frequency data reduction for event related potentials: combining principal component analysis and matching pursuit

EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Singular Spectrum Analysis of Sleep EEG in Insomnia

Journal of Medical Systems
Time-frequency distributions in the classification of epilepsy from EEG signals

Expert Systems with Applications: An International Journal
Time domain signal enhancement based on an optimized singular vector denoising algorithm

Digital Signal Processing
Early prediction of the highest workload in incremental cardiopulmonary tests

ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers

Quantified Score

Hi-index	0.00

Visualization

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.