Research Area: Machine Learning
Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.
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Author(s) Name: Syed Muhammad Usmanb, Shehzad Khalida, Rizwan Akhtara, Zuner Bortolottod , Zafar Bashird , Haiyang Qiu
Journal name: Seizure
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Publisher name: Elsevier
DOI: 10.1016/j.seizure.2019.08.006
Volume Information: Volume 71, October 2019, Pages 258-269
