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.
Original language | English (US) |
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Pages (from-to) | 586-597 |
Number of pages | 12 |
Journal | Cybernetics and Systems Analysis |
Volume | 47 |
Issue number | 4 |
DOIs | |
State | Published - Jul 2011 |
Externally published | Yes |
Keywords
- brain dynamics
- epileptic seizure
- scalp electroencephalogram
- seizure warning
ASJC Scopus subject areas
- Computer Science(all)