DocumentCode :
1561223
Title :
An optimal feature set for seizure detection systems for newborn EEG signals
Author :
Zarjam, Pega ; Mesbah, Mostefa ; Boashash, Boualem
Author_Institution :
Signal Process. Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia
Volume :
5
fYear :
2003
Abstract :
A novel automated method is applied to Electroencephalogram (EEG) data to detect seizure events in newborns. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimizing technique based on mutual information feature selection (MIFS) is employed. This technique evaluates a set of candidate features extracted from the WCs to select an informative subset. This subset is used as an input to an artificial neural network (ANN) classifier. The classifier organizes the EEG signal into seizure or non-seizure activities. The training and test sets are obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The optimized results show an average seizure detection rate of 94%.
Keywords :
electroencephalography; medical signal processing; neural nets; paediatrics; signal classification; wavelet transforms; artificial neural network classifier; automated method; average seizure detection rate; detection scheme; electroencephalogram data; informative; mutual information feature selection; newborn electroencephalogram signals; nonseizure activities; optimizing technique; seizure activities; seizure events; test sets; training sets; wavelet coefficients; Artificial neural networks; Australia; Databases; Discrete wavelet transforms; Electroencephalography; Event detection; Feature extraction; Pediatrics; Signal analysis; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on
Print_ISBN :
0-7803-7761-3
Type :
conf
DOI :
10.1109/ISCAS.2003.1206166
Filename :
1206166
Link To Document :
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