%0 Journal Article %A Mohammad Zavid Parvez and Manoranjan Paul %D 2015 %J International Journal of Medical and Health Sciences %B World Academy of Science, Engineering and Technology %I Open Science Index 108, 2015 %T Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena %U https://publications.waset.org/pdf/10003072 %V 108 %X A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/ deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set. %P 844 - 848