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Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation.

Corsini, J, Shoker, L, Sanei, S and Alarcón, G (2006) Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation. IEEE Trans Biomed Eng, 53 (5). pp. 790-799.

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Abstract

Most of the methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These recordings require intensive surgical operations to implant the electrodes within the brain which are hazardous to the patient. Here, we have developed a novel approach to quantify the dynamical changes of the brain using the scalp EEG. The scalp signals are preprocessed by means of an effective block-based blind source separation (BSS) technique to separate the underlying sources within the brain. The algorithm significantly removes the effect of eye blinking artifacts. An overlap window procedure has been incorporated in order to mitigate the inherent permutation problem of BSS and maintain the continuity of the estimated sources. Chaotic behavior of the underlying sources has then been evaluated by measuring the largest Lyapunov exponent. For our experiments, we provided twenty sets of simultaneous intracranial and scalp EEG recordings from twenty patients. The above recordings have been compared. Similar results were obtained when the intracranial electrodes recorded the electrical activity of the epileptic focus. Our preliminary results show a great improvement when the epileptic focus is not captured by the intracranial electrodes.

Item Type: Article
Authors :
AuthorsEmailORCID
Corsini, JUNSPECIFIEDUNSPECIFIED
Shoker, LUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Alarcón, GUNSPECIFIEDUNSPECIFIED
Date : May 2006
Identification Number : https://doi.org/10.1109/TBME.2005.862551
Uncontrolled Keywords : Algorithms, Animals, Artifacts, Brain, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Prognosis, Skin
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:14
Last Modified : 28 Mar 2017 14:14
URI: http://epubs.surrey.ac.uk/id/eprint/744116

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