Epileptic Seizure Prediction Using Data Mining Algorithms.

EasyChair Preprint 1975

Abstract

Epileptic seizures are caused by sudden electric discharges in neurons irregular transient operation; hence this generate cerebral neurological dysfunction. The seizures can occur in different regions of the brain and are reflected in various clinical expressions. As it happens, depending on the location and job of the affected neuron, seizures with different types occur: simple partial seizures (SPS), complex partial seizures (CPS), secondarily generalized seizures(SGS) with epilepsy locations classified in temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), posterior epilepsy (PE including the parietal, occipital, occipito-temporal, temporo-occipito-parietal junction regions), operculo-insular epilepsy (OIE) and multifocal epilepsy (MFE).
In this paper, we propose a new approach of epilepsy seizure prediction by using data mining algorithms to analyze seizures of a huge number of patients EEGs. This method is most powerful and most accurate than the others prediction methods.

Keyphrases: Algorithms, Data Mining, EEG, Epilepsy, Seizure, supervised learning

@booklet{EasyChair:1975,
  author    = {Ilyas Zidane and Jamal Mhamdi and Nordine Zidane},
  title     = {Epileptic Seizure Prediction Using Data Mining Algorithms.},
  howpublished = {EasyChair Preprint 1975},
  year      = {EasyChair, 2019}}