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.
|Title of host publication||Biomedical image analysis and mining techniques for improved health outcomes|
|Editors||Wahiba Ben Abdessalem Karaa, Nilanjan Dey|
|Place of Publication||United States|
|Number of pages||18|
|ISBN (Print)||9781466688117, 1466688114|
|Publication status||Published - 2016|
|Name||Advances in Bioinformatics and Biomedical Engineering (ABBE)|
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Epilepsy Seizure prediction using Electroencephalogram (EEG) signal analysis
Manoranjan Paul (Creator) & Mohammad Zavid Parvez (Creator)
Impact: Quality of life Impact