Detection of newborns' EEG seizure using time-frequency divergence measures

Pega Zarjam, Ghasem Azemi, Mostefa Mesbah, Boualem Boashash

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)

Abstract

In this paper, a time-frequency approach for detecting seizure activities in newborns Electroencephalogram (EEG) data is proposed. In this approach, the discrimination between seizure and non-seizure states is based on the time-frequency distance between the consequent segments in the EEG signal. Three different time-frequency measures and three different reduced time-frequency distributions are used in this study. The proposed method is tested on the EEG data acquired from three neonates with ages ranging from two days to two weeks. The experimental results validate the suitability of the proposed method in automated newborns' seizure detection. The results show an average seizure detection rate of 96% and false alarm rate of 5%.
Original languageEnglish
Title of host publication2004 IEEE International Conference on Acoustics, Speech and Signal Processing
Subtitle of host publicationProceedings
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
VolumeV
ISBN (Print)0780384849
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Montreal, Canada
Duration: 17 May 200421 May 2004

Conference

Conference2004 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryCanada
CityMontreal
Period17/05/0421/05/04

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