An Entropy-based Feature in Epileptic Seizure Prediction Algorithm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 6
Abstract
Abstract : Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure predictionalgorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods. The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizureperiods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific time. The prediction time in order of 1min- 49min is achieved in 60 seizure periods under study in this task. SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performanceis evaluated and analysed.
Authors and Affiliations
Ashraf Anwar
Keywords
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