EEG analysis using wavelet-based information tools

O. A. Rosso; M. T. Martin; A. Figliola; K. Keller; A. Plastino

doi:10.1016/j.jneumeth.2005.10.009

EEG analysis using wavelet-based information tools

O. A. Rosso^*, M. T. Martin, A. Figliola, K. Keller, A. Plastino

^*Corresponding author for this work

Institute of Mathematics

200 Citations (Scopus)

Abstract

Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity.

Original language	English
Journal	Journal of Neuroscience Methods
Volume	153
Issue number	2
Pages (from-to)	163-182
Number of pages	20
ISSN	0165-0270
DOIs	https://doi.org/10.1016/j.jneumeth.2005.10.009
Publication status	Published - 15.06.2006

Funding

This work was partially supported by CONICET (PIP 0029/98, 5687/05 and 6036/05), Argentina and the International Office of BMBF (ARG-4-G0A-6A and ARG01-005), Germany. The authors wish to thank A. Rabinowicz of the Instituto de Investigaciones Neurológicas Raúl Carra (FLENI), Argentina, for useful comments and the providing of the EEG recordings used in this work. OAR and AF are very grateful to Prof. Dr. B. Fischer and Prof. Dr. J. Prestin for their kind hospitality at Institut für Mathematik, Universität zu Lübeck, Germany, where parts of this work was done. The comments of two anonymous reviewers were responsible for a significant improvement in the manuscript, we are thankful to them.

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10.1016/j.jneumeth.2005.10.009

Cite this

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AU - Rosso, O. A.

AU - Martin, M. T.

AU - Figliola, A.

AU - Keller, K.

AU - Plastino, A.

PY - 2006/6/15

Y1 - 2006/6/15

N2 - Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity.

AB - Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity.

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ER -

EEG analysis using wavelet-based information tools

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Graduate School GSC 235: Graduate School for Computing in Medicine and Life Sciences

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EEG analysis using wavelet-based information tools

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Graduate School GSC 235: Graduate School for Computing in Medicine and Life Sciences

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