Skip to content

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)258-269
Number of pages12
JournalSeizure
Volume71
Early online date19 Aug 2019
DOIs
DateAccepted/In press - 14 Aug 2019
DateE-pub ahead of print - 19 Aug 2019
DatePublished (current) - 1 Oct 2019

Abstract

Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.

    Research areas

  • Epilepsy prediction, Intracranial EEG, Preictal state, Scalp EEG, Seizures prediction methods

Documents

Documents

  • Full-text PDF (author’s accepted manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S1059131119302213#! . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 816 KB, PDF document

    Embargo ends: 19/08/20

    Request copy

    Licence: CC BY-NC-ND

DOI

View research connections

Related faculties, schools or groups