eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

Journal: Epilepsy & behavior : E&B
Published Date:

Abstract

OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary human scrutiny. Significant information may not be evident and is missed, and misinterpretation remains a serious problem. Can we develop an artificial intelligence system to accurately and efficiently classify EEG and even reveal novel information? In this study, deep learning techniques and, in particular, Convolutional Neural Networks, have been used to develop a model (which we have named eDeeplepsy) for distinguishing different brain states in children with epilepsy.

Authors

  • Alberto Nogales
    CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain. Electronic address: alberto.nogales@ceiec.es.
  • Álvaro J García-Tejedor
    CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain. Electronic address: a.gtejedor@ceiec.es.
  • Juan Serrano Vara
    CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain. Electronic address: juanjose.serrano@ceiec.es.
  • Arturo Ugalde-Canitrot
    School of Medicine. Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800. Pozuelo de Alarcón 28223, Spain; Epilepsy Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz, Paseo de la Castellana, 261, Madrid 28046, Spain.