Nonlinear dynamical analysis of periodic lateralized epileptiform discharges

C J Stam, J Nicolai, R W Keunen

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. In the present study we used this approach to study the pathophysiology of PLEDs. We calculated the correlation dimension D2 of an EEG with typical PLEDs, and compared the results with those obtained for surrogate data. These surrogate data have the same power spectrum and amplitude distribution as the original EEG data, but are otherwise random. By construction, such surrogate data can be described by a linear model. Our results showed that D2 estimations for PLEDs were low, on the order of one, and that the results for EEG and the surrogate data were clearly different, indicating that the EEG with PLEDs reflects nonlinear dynamics of the underlying neural networks.

Original languageEnglish
Pages (from-to)101-5
Number of pages5
JournalClinical EEG (electroencephalography)
Volume29
Issue number2
Publication statusPublished - Apr 1998

Cite this

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title = "Nonlinear dynamical analysis of periodic lateralized epileptiform discharges",
abstract = "Nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. In the present study we used this approach to study the pathophysiology of PLEDs. We calculated the correlation dimension D2 of an EEG with typical PLEDs, and compared the results with those obtained for surrogate data. These surrogate data have the same power spectrum and amplitude distribution as the original EEG data, but are otherwise random. By construction, such surrogate data can be described by a linear model. Our results showed that D2 estimations for PLEDs were low, on the order of one, and that the results for EEG and the surrogate data were clearly different, indicating that the EEG with PLEDs reflects nonlinear dynamics of the underlying neural networks.",
keywords = "Electroencephalography, Epilepsy/physiopathology, Female, Humans, Middle Aged, Nonlinear Dynamics, Signal Processing, Computer-Assisted",
author = "Stam, {C J} and J Nicolai and Keunen, {R W}",
year = "1998",
month = "4",
language = "English",
volume = "29",
pages = "101--5",
journal = "Clinical EEG (electroencephalography)",
issn = "0009-9155",
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}

Nonlinear dynamical analysis of periodic lateralized epileptiform discharges. / Stam, C J; Nicolai, J; Keunen, R W.

In: Clinical EEG (electroencephalography), Vol. 29, No. 2, 04.1998, p. 101-5.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Nonlinear dynamical analysis of periodic lateralized epileptiform discharges

AU - Stam, C J

AU - Nicolai, J

AU - Keunen, R W

PY - 1998/4

Y1 - 1998/4

N2 - Nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. In the present study we used this approach to study the pathophysiology of PLEDs. We calculated the correlation dimension D2 of an EEG with typical PLEDs, and compared the results with those obtained for surrogate data. These surrogate data have the same power spectrum and amplitude distribution as the original EEG data, but are otherwise random. By construction, such surrogate data can be described by a linear model. Our results showed that D2 estimations for PLEDs were low, on the order of one, and that the results for EEG and the surrogate data were clearly different, indicating that the EEG with PLEDs reflects nonlinear dynamics of the underlying neural networks.

AB - Nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. In the present study we used this approach to study the pathophysiology of PLEDs. We calculated the correlation dimension D2 of an EEG with typical PLEDs, and compared the results with those obtained for surrogate data. These surrogate data have the same power spectrum and amplitude distribution as the original EEG data, but are otherwise random. By construction, such surrogate data can be described by a linear model. Our results showed that D2 estimations for PLEDs were low, on the order of one, and that the results for EEG and the surrogate data were clearly different, indicating that the EEG with PLEDs reflects nonlinear dynamics of the underlying neural networks.

KW - Electroencephalography

KW - Epilepsy/physiopathology

KW - Female

KW - Humans

KW - Middle Aged

KW - Nonlinear Dynamics

KW - Signal Processing, Computer-Assisted

M3 - Article

VL - 29

SP - 101

EP - 105

JO - Clinical EEG (electroencephalography)

JF - Clinical EEG (electroencephalography)

SN - 0009-9155

IS - 2

ER -