Wavelet analysis of generalized tonic-clonic epileptic seizures
Signal Processing
Application of fuzzy similarity to prediction of epileptic seizures using EEG signals
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Towards Personalized Neural Networks for Epileptic Seizure Prediction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Seizure characterisation using frequency-dependent multivariate dynamics
Computers in Biology and Medicine
Auto mutual information analysis with order patterns for epileptic EEG
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
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Epileptic seizures prediction is an interesting issue in epileptology, since it can promise a novel approach to control seizures and understand the mechanism of epileptic seizures. In this paper, we describe a new method, called wavelet-based nonlinear similarity index (WNSI), to predict epileptic seizures using EEG recordings in real time. This method combines wavelet techniques and nonlinear dynamics. The test results of EEG recordings of rats and humans show that WNSI can track the hidden dynamical changes of brain electrical activity. Particularly, we found that it can obtain the best performance of seizure prediction at the beta (10-30Hz) frequency band of EEG signals. A possible reason is suggested from the functional connectivity of the brain. In terms of this study, it is recommended that wavelet technique is very useful to improve the performance of epileptic seizures prediction.