A Feature Set for EEG Seizure Detection in the Newborn based on Seizure and Background Charactersitics

Author

Stevenson, N. ; Mesbah, M. ; Boashash, B.

Author_Institution

Univ. of Queensland, Herston

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

7

Lastpage

10

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.

Keywords

covariance analysis; data acquisition; electroencephalography; feature extraction; medical signal processing; neurophysiology; obstetrics; EEG seizure detection; background characteristics; covariance function; data acquisition; electroencephalograms; feature extraction; newborn EEG; seizure characteristics; statistical testing; Australia; Band pass filters; Brain modeling; Cutoff frequency; Electroencephalography; Frequency modulation; Hospitals; Pediatrics; Spatial databases; Stochastic processes; Electroencephalography; Humans; Infant, Newborn; Linear Models; Models, Biological; Models, Statistical; Seizures; Signal Processing, Computer-Assisted;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4352209

Filename

4352209