A SIGNAL REGULARITY-BASED AUTOMATED SEIZURE PREDICTION ALGORITHM USING LONG-TERM SCALP EEG RECORDINGS

被引：0

作者：

Chien, Jui-Hong ^{[1
,2
]}

Shiau, Deng -Shan ^{[3
]}

Halford, J. J. ^{[2
]}

Kelly, K. M. ^{[4
,5
,6
,7
]}

Kern, R. T. ^{[2
]}

Yang, M. C. K. ^{[8
]}

Zhang, Jicong ^{[9
]}

Sackellares, J. Ch. ^{[2
]}

Pardalos, P. M. ^{[1
,9
,10
]}

机构：

[1] Univ Florida, Pruitt Family Dept Biomed Engn, Gainesville, FL USA

[2] Optima Neurosci Inc, Gainesville, FL USA

[3] Sci Affairs, Gainesville, FL USA

[4] Med Univ South Carolina, Charleston, SC 29425 USA

[5] Drexel Univ, Coll Med, Philadelphia, PA 19104 USA

[6] Allegheny Gen Hosp, Pittsburgh, PA 15212 USA

[7] Allegheny Singer Res Inst, Pittsburgh, PA 15212 USA

[8] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

[9] Univ Florida, Dept Ind & Syst Engn, Gainesville, FL 32611 USA

[10] Univ Florida, Dept Comp & Informat Sci & Engn, Gainesville, FL USA

来源：

CYBERNETICS AND SYSTEMS ANALYSIS | 2011年 / 47卷 / 04期

关键词：

epileptic seizure; seizure warning; scalp electroencephalogram; brain dynamics;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：586 / 597

页数：12

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