Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis

Xiaoli LI; Gaoxiang OUYANG; Xin YAO; Xinping GUAN

doi:10.1016/j.physleta.2004.10.028

Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis

Xiaoli LI, Gaoxiang OUYANG, Xin YAO, Xinping GUAN

Research output: Journal Publications › Journal Article (refereed) › peer-review

71 Citations (Scopus)

Abstract

Understanding the transition of brain activity towards an epileptic seizure, called pre-epileptic seizure, is a challenge in epilepsy. In this Letter, a recurrence quantification analysis (RQA) is proposed to describe dynamical characteristics of EEG (electroencephalograph) recordings on rat experiments, which is helpful to predict seizures. One of the advantages of this method does not require any assumptions to EEG data, such as linear, stationary, noiseless and so on. A series of experimental tests in this study show that the dynamical characteristics of EEG data with RQA can identify the differences among inter-ictal, pre-ictal and ictal phases; and support the hypothesis that complexity of brain electrical activity has a significant decrease prior to an epileptic seizure. This change could be useful in predicting epileptic seizures. © 2004 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	164-171
Number of pages	8
Journal	Physics Letters, Section A: General, Atomic and Solid State Physics
Volume	333
Issue number	1-2
Early online date	24 Oct 2004
DOIs	https://doi.org/10.1016/j.physleta.2004.10.028
Publication status	Published - Nov 2004
Externally published	Yes

Bibliographical note

The partial support of Welcome Trust and Advantage West Midland (AWM) are gratefully acknowledged.

Keywords

EEG
Epileptic seizure
Prediction
Recurrence quantification analysis

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10.1016/j.physleta.2004.10.028

Cite this

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title = "Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis",

abstract = "Understanding the transition of brain activity towards an epileptic seizure, called pre-epileptic seizure, is a challenge in epilepsy. In this Letter, a recurrence quantification analysis (RQA) is proposed to describe dynamical characteristics of EEG (electroencephalograph) recordings on rat experiments, which is helpful to predict seizures. One of the advantages of this method does not require any assumptions to EEG data, such as linear, stationary, noiseless and so on. A series of experimental tests in this study show that the dynamical characteristics of EEG data with RQA can identify the differences among inter-ictal, pre-ictal and ictal phases; and support the hypothesis that complexity of brain electrical activity has a significant decrease prior to an epileptic seizure. This change could be useful in predicting epileptic seizures. {\textcopyright} 2004 Elsevier B.V. All rights reserved.",

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T1 - Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis

AU - LI, Xiaoli

AU - OUYANG, Gaoxiang

AU - YAO, Xin

AU - GUAN, Xinping

N1 - The partial support of Welcome Trust and Advantage West Midland (AWM) are gratefully acknowledged.

PY - 2004/11

Y1 - 2004/11

N2 - Understanding the transition of brain activity towards an epileptic seizure, called pre-epileptic seizure, is a challenge in epilepsy. In this Letter, a recurrence quantification analysis (RQA) is proposed to describe dynamical characteristics of EEG (electroencephalograph) recordings on rat experiments, which is helpful to predict seizures. One of the advantages of this method does not require any assumptions to EEG data, such as linear, stationary, noiseless and so on. A series of experimental tests in this study show that the dynamical characteristics of EEG data with RQA can identify the differences among inter-ictal, pre-ictal and ictal phases; and support the hypothesis that complexity of brain electrical activity has a significant decrease prior to an epileptic seizure. This change could be useful in predicting epileptic seizures. © 2004 Elsevier B.V. All rights reserved.

AB - Understanding the transition of brain activity towards an epileptic seizure, called pre-epileptic seizure, is a challenge in epilepsy. In this Letter, a recurrence quantification analysis (RQA) is proposed to describe dynamical characteristics of EEG (electroencephalograph) recordings on rat experiments, which is helpful to predict seizures. One of the advantages of this method does not require any assumptions to EEG data, such as linear, stationary, noiseless and so on. A series of experimental tests in this study show that the dynamical characteristics of EEG data with RQA can identify the differences among inter-ictal, pre-ictal and ictal phases; and support the hypothesis that complexity of brain electrical activity has a significant decrease prior to an epileptic seizure. This change could be useful in predicting epileptic seizures. © 2004 Elsevier B.V. All rights reserved.

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KW - Epileptic seizure

KW - Prediction

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DO - 10.1016/j.physleta.2004.10.028

M3 - Journal Article (refereed)

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SP - 164

EP - 171

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

IS - 1-2

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Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis

Abstract

Bibliographical note

Keywords

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