Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies. (October 2019)

• Record Type: Journal Article
• Authors: Usman, Syed Muhammad; Khalid, Shehzad; Akhtar, Rizwan; Bortolotto, Zuner; Bashir, Zafar; Qiu, Haiyang
• Is Part Of: Seizure. Volume 71(2019)
• Page Start: 258
• Page End: 269
• Publication Date: 2019-10
• Subjects: Epilepsy prediction -- Preictal state -- Scalp EEG -- Intracranial EEG -- Seizures prediction methods; Epilepsy -- Periodicals; Epilepsy -- Periodicals; Seizures -- Periodicals; Épilepsie -- Périodiques; Electronic journals; Electronic journals; 616.853
• DOI: 10.1016/j.seizure.2019.08.006
• Languages: English
• ISSNs: 1059-1311

Abstract

Highlights: Epileptic seizures cannot be treated completely with medicines or surgery. Seizures can be prevented with medicines if predicted before onsets. In this study, multiple seizure prediction methods have been compared. Effective pre-processing, feature extraction and classification improves prediction. Methods have been evaluated on the basis of sensitivity and false positive alarms. Abstract: 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.