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

Jui Hong Chien; Deng Shan Shiau; J. J. Halford; K. M. Kelly; R. T. Kern; M. C.K. Yang; Jicong Zhang; J. Ch Sackellares; P. M. Pardalos

doi:10.1007/s10559-011-9339-x

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

Jui Hong Chien, Deng Shan Shiau, J. J. Halford, K. M. Kelly, R. T. Kern, M. C.K. Yang, Jicong Zhang, J. Ch Sackellares, P. M. Pardalos

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English (US)
Pages (from-to)	586-597
Number of pages	12
Journal	Cybernetics and Systems Analysis
Volume	47
Issue number	4
DOIs	https://doi.org/10.1007/s10559-011-9339-x
State	Published - Jul 2011
Externally published	Yes

Keywords

brain dynamics
epileptic seizure
scalp electroencephalogram
seizure warning

ASJC Scopus subject areas

Computer Science(all)

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10.1007/s10559-011-9339-x

Cite this

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title = "A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings",

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.",

keywords = "brain dynamics, epileptic seizure, scalp electroencephalogram, seizure warning",

author = "Chien, {Jui Hong} and Shiau, {Deng Shan} and Halford, {J. J.} and Kelly, {K. M.} and Kern, {R. T.} and Yang, {M. C.K.} and Jicong Zhang and Sackellares, {J. Ch} and Pardalos, {P. M.}",

note = "Funding Information: 1This work was supported by the grants 5R01NS050582 (JCS) and 1R43NS064647 (DSS) from NIH-NINDS.",

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doi = "10.1007/s10559-011-9339-x",

language = "English (US)",

volume = "47",

pages = "586--597",

journal = "Cybernetics and Systems Analysis",

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AU - Chien, Jui Hong

AU - Shiau, Deng Shan

AU - Halford, J. J.

AU - Kelly, K. M.

AU - Kern, R. T.

AU - Yang, M. C.K.

AU - Zhang, Jicong

AU - Sackellares, J. Ch

AU - Pardalos, P. M.

N1 - Funding Information: 1This work was supported by the grants 5R01NS050582 (JCS) and 1R43NS064647 (DSS) from NIH-NINDS.

PY - 2011/7

Y1 - 2011/7

N2 - 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.

AB - 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.

KW - brain dynamics

KW - epileptic seizure

KW - scalp electroencephalogram

KW - seizure warning

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

Abstract

Keywords

ASJC Scopus subject areas

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