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...
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| 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| id |
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irk-123456789-842192015-07-04T03:02:01Z A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. Системный анализ 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. Запропоновано алгоритм автоматизованого прогнозування епілептичного нападу на основі аналізу регулярності сигналу ЕЕГ головного мозку. Регулярність сигналу розраховується на основі введеної величини регулярної статистики збігу фрагментів (Pattern Match Regularity Statistics — PMRS). Відмінною рисою алгоритму є ступінь збіжності в значеннях PMRS, розрахованих на основі показань із різних груп електродів, визначених у процесі навчання алгоритму. В основі алгоритму лежить гіпотеза про те, що збіжність у значеннях величини PMRS збільшується під час переходу в стан нападу і в такий спосіб може слугувати індикатором для прогнозування нападу. 2011 Article 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. 0023-1274 http://dspace.nbuv.gov.ua/handle/123456789/84219 519.6 en Кибернетика и системный анализ Інститут кібернетики ім. В.М. Глушкова НАН України |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| language |
English |
| topic |
Системный анализ Системный анализ |
| spellingShingle |
Системный анализ Системный анализ Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings Кибернетика и системный анализ |
| description |
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. |
| format |
Article |
| author |
Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. |
| author_facet |
Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. |
| author_sort |
Jui-Hong, Ch. |
| title |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_short |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_full |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_fullStr |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_full_unstemmed |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_sort |
signal regularity-based automated seizure prediction algorithm using long-term scalp eeg recordings |
| publisher |
Інститут кібернетики ім. В.М. Глушкова НАН України |
| publishDate |
2011 |
| topic_facet |
Системный анализ |
| url |
http://dspace.nbuv.gov.ua/handle/123456789/84219 |
| citation_txt |
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. |
| series |
Кибернетика и системный анализ |
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