title: Detection of rhythmic discharges in newborn EEG signals.
abstract: This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed.
authors.affiliation: Biomedical Engineering Laboratory of Sharif University of Technology, Tehran. Iran.
authors.forename: Hamid R
authors.initials: HR
authors.lastname: Mohseni
authors.forename: H
authors.initials: H
authors.lastname: Mirghasemi
authors.forename: Mohannad B
authors.initials: MB
authors.lastname: Shamsollahi
authors.forename: Mohammad R
authors.initials: MR
authors.lastname: Zamani
mesh_terms: D004569:Electroencephalography; D006801:Humans; D007231:Infant, Newborn; D008954:Models, Biological; D010507:Periodicity; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted