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EEG seizure detection and prediction algorithms: a survey | EURASIP Journal on Advances in Signal Processing | Full Text

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Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

EEG seizure detection and prediction algorithms: a survey Review Open access Published: 23 December 2014 Volume 2014 , article number 183 ( 2014 ) Cite this article You have full access to this open access article Download PDF Save article View saved research EURASIP Journal on Advances in Signal Processing Aims and scope Submit manuscript EEG seizure detection and prediction algorithms: a survey Download PDF Abstract Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or

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