Discrete wavelet transform based seizure detection in newborns EEG signals

Zarjam, P. and Mesbah, M. (2003). Discrete wavelet transform based seizure detection in newborns EEG signals. In: ISSPA 2003. Seventh International Symposium on Signal Processing and its Applications. Proceedings. Seventh International Symposium on Signal Processing and Its Applications, 2003., Paris, France, (459-462). 1-4 July 2003. doi:10.1109/ISSPA.2003.1224913


Author Zarjam, P.
Mesbah, M.
Title of paper Discrete wavelet transform based seizure detection in newborns EEG signals
Conference name Seventh International Symposium on Signal Processing and Its Applications, 2003.
Conference location Paris, France
Conference dates 1-4 July 2003
Proceedings title ISSPA 2003. Seventh International Symposium on Signal Processing and its Applications. Proceedings
Journal name Seventh International Symposium On Signal Processing and its Applications, Vol 2, Proceedings
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE
Publication Year 2003
Sub-type Fully published paper
DOI 10.1109/ISSPA.2003.1224913
ISBN 0-7803-7946-2
Volume 2
Start page 459
End page 462
Total pages 4
Language eng
Formatted Abstract/Summary
This paper proposes a novel method for detecting newborns seizure events from electroencephalogram (EEG) data. The detection scheme is based on the discrete wavelet transform (DWT) of the EEG signals. The number of zero-crossings, the average distance between adjacent zero-crossings, the number of extrema, and the average distance between adjacent extrema of the wavelet coefficients (WCs) of certain scales are extracted to form a feature set. The extracted feature set is then fed to an artificial neural network (ANN) classifier to organize the EEG signals into seizure and non- seizure activities. In this study, the training and test sets were obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The obtained results show that on the average 95% of the EEG seizures were detected by the proposed scheme.
©2003 IEEE.

Subjects 090609 Signal Processing
0903 Biomedical Engineering
Keyword Discrete wavelet transforms
Electroencephalography
Feature extraction
Medical signal detection
Neural nets
Artificial neural network classifier
Discrete wavelet transform
Newborns EEG signals
Seizure detection
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

 
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Created: Thu, 26 Mar 2009, 09:51:52 EST by Maryanne Watson on behalf of Faculty Of Health Sciences