Prediction and Detection of Epileptic Seizure

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Author(s): Mohammad Zavid Parvez (Charles Sturt University, Australia)and Manoranjan Paul (Charles Sturt University, Australia)
Copyright: 2016
Pages: 23
Source title: Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes
Source Author(s)/Editor(s): Wahiba Ben Abdessalem Karâa (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia)and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
DOI: 10.4018/978-1-4666-8811-7.ch015

Keywords: Bioinformatics / Medical Information Science Reference / Medicine & Healthcare

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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.

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