NDLI: Automatic detection of preterm neonatal EEG background states

Content Provider	IEEE Xplore Digital Library
Author	Wong, L. Abdulla, W.H.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Auckland, Auckland (Wong, L.; Abdulla, W.H.)
Abstract	Background states of an EEG signal describe the distinctive variations in the amplitude of the signal with respect to time. Background state detection in EEG is used to help estimate the brain growth progress in infants. Currently, background detection is mostly done manually, which is highly subjective. This paper proposes a way to automatically detect background states for preterm infants. The distribution of the amplitude vector in a 10-minute window of 2-channel preterm neonatal EEG signal is analysed, and the mean and standard deviations of the amplitudes in log-space are used as features in a linear discriminant analysis based classifier. The results are compared with existing methods of background detection. The algorithm performs well compared with the visual classification. It also shows less sensitivity to local variations the existing algorithm are suffering from.
Starting Page	421
Ending Page	424
File Size	298745
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4517636
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Electroencephalography State estimation Signal analysis Guidelines Patient monitoring Instruments Vectors Linear discriminant analysis Pattern classification Medical expert systems Biomedical signal analysis
Content Type	Text
Resource Type	Article

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