Application of wavelet-based similarity analysis to epileptic seizures prediction.
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 (10-30 Hz) 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.
Ouyang
Gaoxiang
G
Centre for Networking Control and Bioinformatics, Yanshan University, Qinhuangdao, Hebei, 066004, China.
Li
Xiaoli
X
Li
Yan
Y
Guan
Xinping
X
D000465:Algorithms; D000818:Animals; D001640:Bicuculline; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000175:diagnosis* / Q000503:physiopathology; D004827:Epilepsy / Q000139:chemically induced / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D019579:Neocortex / Q000503:physiopathology; D009415:Nerve Net / Q000503:physiopathology; D017711:Nonlinear Dynamics; D051381:Rats; D017207:Rats, Sprague-Dawley; D012815:Signal Processing, Computer-Assisted; D013702:Temporal Lobe / Q000503:physiopathology
D001640:Bicuculline