Comparing two time-scale and time-frequency based methods in newborns' EEG seizure detection

Zarjam, Pega, Mesbah, Mostefa and Boashash, Boualem (2007). Comparing two time-scale and time-frequency based methods in newborns' EEG seizure detection. In: Signal Processing and Communication 2007 (ICSPC 2007). IEEE International Conference on Signal Processing and Communication 2007 (ICSPC07), Dubai, United Arab Emirates, (1579-1582). 24-27 November 2007. doi:10.1109/ICSPC.2007.4728635


Author Zarjam, Pega
Mesbah, Mostefa
Boashash, Boualem
Title of paper Comparing two time-scale and time-frequency based methods in newborns' EEG seizure detection
Conference name IEEE International Conference on Signal Processing and Communication 2007 (ICSPC07)
Conference location Dubai, United Arab Emirates
Conference dates 24-27 November 2007
Convener IEEE
Proceedings title Signal Processing and Communication 2007 (ICSPC 2007)
Journal name ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Series ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE - Institute of Electrical Electronics Engineers Inc.
Publication Year 2007
Year available 2007
Sub-type Fully published paper
DOI 10.1109/ICSPC.2007.4728635
Open Access Status Not yet assessed
ISBN 1-4244-1236-6
978-1-4244-1235-8
Start page 1579
End page 1582
Total pages 4
Language eng
Abstract/Summary In this research, two different approaches for detecting seizure patterns in newborns' Electroencephalogram (EEG) signals are compared. The first proposed approach is a time-frequency (TF) based method, in which, the discrimination between seizure and non-seizure states is based on the TF distance between the consequent segments in the EEG signal. Three different TF measures and three different reduced time-frequency distributions (TFD) are used in this study. The second proposed approach is a discrete wavelet transform (DWT) based method, in which, the detection scheme is based on observing the changing behavior of few statistical quantities of the wavelet coefficients (WCs) of the EEGs at various scales. These statistics form a feature set which is fed into an artificial neural network (ANN) classifier to organize the EEG signals into seizure and non-seizure activities. The proposed methods are tested on the EEG data acquired from three neonates with ages under two weeks. The empirical results validate the suitability of the two proposed methods in automated newborns' seizure detection. The results present an average seizure detection rate (SDR) of 96% and false alarm rate (FAR) of 5% using Kullback-Leibler measure which outperforms the other two distance measures and the DWT based method.
Subjects 090609 Signal Processing
0903 Biomedical Engineering
Keyword Electroencephalogram (EEG)
Seizure
Reduced interference distributions
Discrete wavelet transform
Time-scale/frequency
Q-Index Code E1
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Excellence in Research Australia (ERA) - Collection
UQ Centre for Clinical Research Publications
 
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Created: Fri, 27 Mar 2009, 01:08:30 EST by Mary-Anne Marrington on behalf of UQ Centre for Clinical Research