Analysis and Definition of Morphological Descriptors for Automatic Detection of Epileptiform Events in EEG Signals with Artificial Neural Networks
This study proposes 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. Despite the existence of several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy, it was necessary to create a new set of parameters that reveal specific morphological characteristics pertaining to these events, since during the automatic detection process they may get mixed up if only conventional descriptors are used. The proposed 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 automatic detection with a 90％ success rate and sensitivity and specificity between 90％ and 95％.
Epileptiform Events Morphological descriptors EEG signals Artificial Neural Networks
Christine Fredel Boos Fernando Mendes de Azevedo Maria do Carmo Vitarelli Pereira Fernanda Isabel Marques Argoud
Instituto de Engenharia Biomedica EEL - CTC - UFSC Florianopolis,Brazil Faculdade de Medicina do Vale do Aco FAMEVACO Ipatinga,Brazil Departamento de Eletronica Instituto Federal de Educacao,Ciencia e Tecnologia de Santa Catarina - IF
国际会议
成都
英文
349-353
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）