Understanding the transition of brain activity towards an epileptic seizure, called pre-epileptic seizure, is a challenge in epilepsy. In this Letter, a recurrence quantification analysis (RQA) is proposed to describe dynamical characteristics of EEG (electroencephalograph) recordings on rat experiments, which is helpful to predict seizures. One of the advantages of this method does not require any assumptions to EEG data, such as linear, stationary, noiseless and so on. A series of experimental tests in this study show that the dynamical characteristics of EEG data with RQA can identify the differences among inter-ictal, pre-ictal and ictal phases; and support the hypothesis that complexity of brain electrical activity has a significant decrease prior to an epileptic seizure. This change could be useful in predicting epileptic seizures. © 2004 Elsevier B.V. All rights reserved.
|Number of pages
|Physics Letters, Section A: General, Atomic and Solid State Physics
|Early online date
|24 Oct 2004
|Published - Nov 2004
Bibliographical noteThe partial support of Welcome Trust and Advantage West Midland (AWM) are gratefully acknowledged.
- Epileptic seizure
- Recurrence quantification analysis