%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