Prediction and detection of epileptic seizure

Mohammad Parvez, Manoranjan Paul

Research output: Book chapter/Published conference paperChapter (peer-reviewed)

Abstract

Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed ''seizure", affecting around 65 million individuals worldwide. Epileptic seizure may lead to many injuries such as fractures, submersion, burns, motor vehicle accidents and even death. It is highly possible to prevent these unwanted situations if we can predict/detect electrical changes in brain that occur prior to onset of actual seizure. When building a prediction model, the goal should be to make a model that accurately classifies preictal period (prior to a seizure onset) from interictal (period between seizures when non-seizure syndrome is observed) period. On the hand, for the detection we need to make a model that can classify ictal (actual seizure period) from non-ictal/interictal period. This chapter describes the seizure detection and prediction techniques with its background, features, recent developments, and future trends.
Original languageEnglish
Title of host publicationBiomedical image analysis and mining techniques for improved health outcomes
EditorsWahiba Ben Abdessalem Karaa, Nilanjan Dey
Place of PublicationUnited States
PublisherIGI Global
Chapter15
Pages314-336
Number of pages18
ISBN (Electronic)9781466688124
ISBN (Print)9781466688117, 1466688114
DOIs
Publication statusPublished - 2016

Publication series

NameAdvances in Bioinformatics and Biomedical Engineering (ABBE)

    Fingerprint

Cite this

Parvez, M., & Paul, M. (2016). Prediction and detection of epileptic seizure. In W. B. Abdessalem Karaa, & N. Dey (Eds.), Biomedical image analysis and mining techniques for improved health outcomes (pp. 314-336). (Advances in Bioinformatics and Biomedical Engineering (ABBE)). IGI Global. https://doi.org/10.4018/978-1-4666-8811-7.ch015