A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings
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 conv...
Збережено в:
| Дата: | 2011 |
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| Автори: | , , , , , , , , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут кібернетики ім. В.М. Глушкова НАН України
2011
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| Назва видання: | Кибернетика и системный анализ |
| Теми: | |
| Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/84219 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings / Ch. Jui-Hong, Sh. Deng-Shan, J.J. Halford, K.M. Kelly, R.T. Kern, M.C.K. Yang, Zh. Jicong, J.Ch. Sackellares, P.M. Pardalos // Кибернетика и системный анализ. — 2011. — Т. 47, № 4. — С. 95-107. — Бібліогр.: 41 назв. — рос. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| Резюме: | 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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