DocumentCode :
3489390
Title :
Detection of newborn EEG seizure using optimal features based on discrete wavelet transform
Author :
Zarjam, Pega ; Mesbah, Mostefa ; Boashash, Boualem
Author_Institution :
Signal Process. Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia
Volume :
2
fYear :
2003
fDate :
6-10 April 2003
Abstract :
A new automated method is proposed to detect seizure events in newborns from electroencephalogram (EEG) data. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimal feature subset is obtained using the mutual information evaluation function (MIEF). The MIEF algorithm evaluates a set of candidate features extracted from WCs to select an informative feature subset. The subset is then fed to an artificial neural network (ANN) classifier that organizes the EEG signal into seizure or non-seizure activity. The performance of the proposed features is compared with that of the features obtained using a mutual information feature selection (MIFS) algorithm. The training and test sets are obtained from EEG data acquired from 5 neonates with ages ranging from 2 days to 2 weeks.
Keywords :
bioelectric potentials; discrete wavelet transforms; electroencephalography; feature extraction; learning (artificial intelligence); medical diagnostic computing; medical signal processing; neural nets; paediatrics; patient diagnosis; signal classification; ANN classifier; artificial neural network classifier; discrete wavelet transform; electroencephalogram; feature extraction; mutual information evaluation function; mutual information feature selection; newborn EEG seizure detection; seizure events; wavelet coefficients; Artificial neural networks; Data mining; Discrete wavelet transforms; Electroencephalography; Event detection; Feature extraction; Mutual information; Pediatrics; Testing; Wavelet coefficients;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-7663-3
Type :
conf
DOI :
10.1109/ICASSP.2003.1202345
Filename :
1202345
Link To Document :
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