@article{(Open Science Index):https://publications.waset.org/pdf/10003072,
	  title     = {Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena},
	  author    = {Mohammad Zavid Parvez and  Manoranjan Paul},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Medical and Health Sciences},
	  volume    = {9},
	  number    = {12},
	  year      = {2015},
	  pages     = {844 - 848},
	  ee        = {https://publications.waset.org/pdf/10003072},
	  url   	= {https://publications.waset.org/vol/108},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 108, 2015},