EEG seizure detection and prediction algorithms: a survey

By Turky Nayef Alotaiby, Saleh A. Alshebeili, Tariq A. Alshawi, Ishtiaq Ahmad, Fathi E. Abd El-Samie. 2014

283 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Signal Processing

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, background): “tic patients. Researchers from nonlinear dynamic systems analysis and control have proposed promising prediction and detection algorithms to suppress the frequency, duration and amplitude of seizures [156]. However, due to lack of full understanding of seizure and its associated neural dynamics, des”
The Temple University Hospital EEG Data Corpus (2016, background): “These methods have yielded limited results, owing mostly to the fact that brain signals (and EEG in particular) are characterized by great variability, which can only be properly interpreted by building statistical models using massive amounts of data (Alotaiby et al., 2014; Ramgopal et al., 2014).”