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Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG
Authors:Arthur [Reference to Petrosian]   Danil [Reference to Prokhorov]   Richard [Reference to Homan]   Richard [Reference to Dasheiff]  Donald Wunsch [Reference to II]
Affiliation:

a Health Sciences Center, Department of Neurology, Texas Tech University, 3601 4th Street, Lubbock, TX 79430, USA

b Ford Research Laboratory, Dearborn, MI, USA

c Applied Computational Intelligence Laboratory, Department of Electrical Engineering, Texas Tech University, Lubbock, TX 79430, USA

Abstract:Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN training/testing, as opposed to specific signal features extracted from EEG. To the best of our knowledge this approach has never been attempted before. The data used included both scalp and intracranial EEG recordings obtained from two epileptic patients. We demonstrate that the existence of a “preictal” stage (immediately preceding seizure) of some minutes duration is quite feasible.
Keywords:EEG   Epileptic seizure   Recurrent neural network   Wavelet transform
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