A feature set for EEG seizure detection in the newborn based on seizure and background charactersitics

N. Stevenson; M. Mesbah; B. Boashash

doi:10.1109/IEMBS.2007.4352209

A feature set for EEG seizure detection in the newborn based on seizure and background charactersitics

N. Stevenson^*, M. Mesbah, B. Boashash

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper presents a set of four features to be used in the detection of seizure in the electroencephalograms (EEGs) of newborns. The features are designed with the aid of recent advances in modelling of the newborn EEG. The performance of the features is analysed with a database of 500 epochs of newborn EEG (250 background/250 seizure). The covariance of the features is also analysed to indicate the redundancy of the feature set. The results show significant differences in the features between seizure and background EEG. The covariance between the features suggests that there is little redundant information between the features.

Original language	English
Title of host publication	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Pages	7-10
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2007.4352209
Publication status	Published - 2007
Event	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France Duration: Aug 23 2007 → Aug 26 2007

Other

Other	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Country/Territory	France
City	Lyon
Period	8/23/07 → 8/26/07

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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10.1109/IEMBS.2007.4352209

Cite this

Stevenson, N, Mesbah, M & Boashash, B 2007, A feature set for EEG seizure detection in the newborn based on seizure and background charactersitics. in 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07., 4352209, pp. 7-10, 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, France, 8/23/07. https://doi.org/10.1109/IEMBS.2007.4352209

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abstract = "This paper presents a set of four features to be used in the detection of seizure in the electroencephalograms (EEGs) of newborns. The features are designed with the aid of recent advances in modelling of the newborn EEG. The performance of the features is analysed with a database of 500 epochs of newborn EEG (250 background/250 seizure). The covariance of the features is also analysed to indicate the redundancy of the feature set. The results show significant differences in the features between seizure and background EEG. The covariance between the features suggests that there is little redundant information between the features.",

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AB - This paper presents a set of four features to be used in the detection of seizure in the electroencephalograms (EEGs) of newborns. The features are designed with the aid of recent advances in modelling of the newborn EEG. The performance of the features is analysed with a database of 500 epochs of newborn EEG (250 background/250 seizure). The covariance of the features is also analysed to indicate the redundancy of the feature set. The results show significant differences in the features between seizure and background EEG. The covariance between the features suggests that there is little redundant information between the features.

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A feature set for EEG seizure detection in the newborn based on seizure and background charactersitics

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