Research Area:  Machine Learning
Epileptic seizures are known for their unpredictable nature. However, recent research provides that the transition to seizure event is not random but the result of evidence accumulations. Therefore, a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes, and the amplitude. In this study, spike rate is used as the indicator to anticipate seizures in electroencephalogram (EEG) signal. Spikes detection step is used in EEG signal during interictal, preictal, and ictal periods followed by a mean filter to smooth the spike number. The maximum spike rate in interictal periods is used as an indicator to predict seizures. When the spike number in the preictal period exceeds the threshold, an alarm is triggered. Using the CHB-MIT database, the proposed approach has ensured 92% accuracy in seizure prediction for all patients.
Author(s) Name:  Itaf Ben Slimen, Larbi Boubchir,and Hassene Seddik
Journal name:  The Journal of Biomedical Research
Publisher name:  PubMed
Volume Information:  Volume 34, Issue (3), Pages : 162–169
Paper Link:   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324272/