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Journal article
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings
Cybernetics and systems analysis, v 47(4), pp 586-597
01 Jul 2011
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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Details
- Title
- A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings
- Creators
- Jui-hong Chien - University of FloridaDeng-shan Shiau - Optima Neuroscience (United States)J Halford - Medical University of South CarolinaK M Kelly - Drexel University, College of MedicineR Kern - Optima Neuroscience (United States)M Yang - University of FloridaJicong Zhang - University of FloridaJ Sackellares - Optima Neuroscience (United States)P Pardalos - University of Florida
- Publication Details
- Cybernetics and systems analysis, v 47(4), pp 586-597
- Publisher
- Springer Nature B.V
- Number of pages
- 12
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Neurology
- Web of Science ID
- WOS:000217727300008
- Scopus ID
- 2-s2.0-80052645937
- Other Identifier
- 991019173449304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Mathematics, Interdisciplinary Applications