title: Automatic detection of epileptic seizures in long-term EEG records.
abstract: Epilepsy is a neurological disorder which affects nearly 1.5% of the world׳s total population. Trained physicians and neurologists visually scan the long-term electroencephalographic (EEG) records to identify epileptic seizures. It generally requires many hours to interpret the data. Therefore, tools for quick detection of seizures in long-term EEG records are very useful. This study proposes an algorithm to help detect seizures in long-term iEEG based on low computational costs methods using Spectral Power and Wavelet analysis. The detector was tested on 21 invasive intracranial EEG (iEEG) records. A sensitivity of 85.39% was achieved. The results indicate that the proposed method detects epileptic seizures in long-term iEEG records successfully. Moreover, the algorithm does not require long processing time due to its simplicity. This feature will allow significant time reduction of the visual inspection of iEEG records performed by the specialists.
authors.affiliation: Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina. Electronic address: agarces@gateme.unsj.edu.ar.
authors.forename: Agustina
authors.initials: A
authors.lastname: Garcés Correa
authors.affiliation: Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina. Electronic address: lorosco@gateme.unsj.edu.ar.
authors.forename: Lorena
authors.initials: L
authors.lastname: Orosco
authors.affiliation: Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina. Electronic address: pdiez@gateme.unsj.edu.ar.
authors.forename: Pablo
authors.initials: P
authors.lastname: Diez
authors.affiliation: Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina. Electronic address: laciar@gateme.unsj.edu.ar.
authors.forename: Eric
authors.initials: E
authors.lastname: Laciar
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D058067:Wavelet Analysis; D055815:Young Adult
keywords: EEG frequency bands; Epilepsy; Intracranial EEG records (iEEG); Power spectrum; Wavelet decomposition