Author： Chien Jui-Hong
Publisher： Springer Publishing Company
ISSN： 1060-0396
Source： Cybernetics and Systems Analysis, Vol.47, Iss.4, 2011-07, pp. : 586-597
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Abstract
The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure.
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