EEG seizure detection and prediction algorithms: a survey

@article{Alotaiby2014EEGSD,
  title={EEG seizure detection and prediction algorithms: a survey},
  author={Turky N. Alotaiby and Saleh A. Alshebeili and Tariq Alshawi and Ishtiaq Ahmad and Fathi E. Abd El-Samie},
  journal={EURASIP Journal on Advances in Signal Processing},
  year={2014},
  volume={2014},
  pages={1-21}
}
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… CONTINUE READING

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Key Quantitative Results

  • Simulation results on the Freiburg database have shown 100% sensitivity with low false-alarm rate.
  • Results reported a sensitivity of 91% and false-alarm rate of 0.02 false positives per hour.
  • Experimental results have shown that this method achieved a 94.46% sensitivity, a 95.26% specificity, and a 0.58/h false detection rate on long-term iEEG.
  • Simulation results have shown a detection accuracy of 98% and a false detection rate of 6%.

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