Please use this identifier to cite or link to this item: http://dspace.sctimst.ac.in/jspui/handle/123456789/10662
Title: Use of Electrographic Seizures and Interictal Epileptiform Discharges for improving performance in Seizure Prediction
Authors: Sudalaimani, C
Asha, SA
Parvathy, K
Thomas, ET
Devanand, P
Sasi, PM
Menon, RN
Ashalatha, R
Thomas, SV
Keywords: Computer Science; Engineering
Issue Date: 2015
Publisher: PROCEEDINGS OF THE 2015 IEEE RECENT ADVANCES IN INTELLIGENT COMPUTATIONAL SYSTEMS (RAICS)
Citation: ,;229-234
Abstract: Electroencephalography (EEG) is an important tool in analyzing brain activity. EEG recording is effectively used for detection and prediction of electrophysiological abnormalities due to epilepsy. Epileptic seizure is a brain disorder which affects the patients acutely. Seizures are controllable with medication in 70% of the cases, however the rest may continue to have recurring epileptic seizures despite medications. Since seizures are unpredictable clinically, these patients will also live with perpetual anxiety about the onset of seizure, apart from being affected by the seizure consequences such as drowsiness, headache, vomiting, etc. The seizures can cause injury to the patients, and in some cases may even result in death. Seizure prediction can aid patients with disabling seizure by detecting the seizure precursors in advance and alerting the patients or their caregivers. If the seizure is predicted in advance it can be aborted by fast acting Anti-epileptic drugs (AEDs) or other treatment procedures. This will also aid pre-surgical video EEG monitoring wherein prediction of the ictal onset zone is paramount and machine alarms can be devised. In this paper, we are comparing the results of our research work related to the seizure prediction models. First model, as in usual practice, differentiates between preictal and interictal data segments only. The other seizure prediction model uses Interictal Epileptiform Discharges (IEDs), Electrographic Seizures (ES) and ictal data segments in addition to the first model. We found that the latter one provided better results and improved the seizure prediction performance.
URI: http://dspace.sctimst.ac.in/jspui/handle/123456789/10662
Appears in Collections:Journal Articles

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