title: Morphological descriptors for automatic detection of epileptiform events.
abstract: The purpose of this study was to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. There are several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy. Considering that during the automatic detection process the morphological characteristics pertaining to these events may get mixed up if only conventional descriptors are used, it was necessary to create a new set of parameters that reveal more differences between them. The parameters are fed to artificial neural networks and the individual and collective contribution of each parameter was evaluated by statistical process. The proposed method achieved a success rate of 80-90%, sensitivity and specificity between 85% and 96%.
authors.affiliation: Instituto de Engenharia Biomédica, DEEL, Universidade Federal de Santa Catarina, Campus Universitário - Trindade, 88040-900, Florianópolis, Brazil. cris_boos@ieb.ufsc.br
authors.forename: Christine Fredel
authors.initials: CF
authors.lastname: Boos
authors.forename: Maria
authors.initials: M
authors.lastname: do Carmo Vitarelli Pereira
authors.forename: Fernanda Isabel
authors.initials: FI
authors.lastname: Marques Argoud
authors.forename: Fernando
authors.initials: F
authors.lastname: Mendes de Azevedo
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors