A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings

• Datum: 2011
• Hauptverfasser: 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.
• Format: Artikel
• Sprache: English
• Veröffentlicht: Інститут кібернетики ім. В.М. Глушкова НАН України 2011
• Schriftenreihe: Кибернетика и системный анализ
• Schlagworte: Системный анализ
• Online Zugang: https://nasplib.isofts.kiev.ua/handle/123456789/84219
• Назва журналу: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Zitieren: 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.

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...