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Abstract:

In the present work, we show that appropriate information-theory tools based on the wavelet transform (relative wavelet energy; normalized total wavelet entropy, H; generalized wavelet complexity, CW), when applied to tonic-clonic epileptic EEC data, provide one with valuable insights into the dynamics of neural activity. Twenty tonic-clonic secondary generalized epileptic records pertaining to eight patients have been analyzed. If the electromyographic activity is excluded the difference between the ictal and pre-ictal mean entropic values (ΔH = 〈H(ictal)〉 - 〈H(pre-ictal)〉) is negative in 95% of the cases (p< 0.0001), and the mean complexity variation (ΔCW = 〈C W (ictal)〉 - 〈CW (pre-ictal)〉) is positive in 85% of the cases (p = 0.0002). Thus during the seizure entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus in this kind of seizures triggers a self-organized brain state characterized by both order and maximal complexity. © 2004 Published by Elsevier B.V.

Registro:

Documento: Artículo
Título:Evidence of self-organization in brain electrical activity using wavelet-based informational tools
Autor:Rosso, O.A.; Martin, M.T.; Plastino, A.
Filiación:Fac. de Ciencias Exactas y Naturales, Instituto de Cálcula, Ciudad Universitaria, 1428 Ciudad de Buenos Aires, Argentina
Argentina's Natl. Research Council, Universidad Nacional de La Plata, Instituto de Física, C.C. 727, 1900 La Plata, Argentina
Palabras clave:Complexity; EEG; Epileptic seizures; Signal entropy; Time-frequency signal analysis; Wavelet analysis; Brain; Computational complexity; Data reduction; Electroencephalography; Entropy; Signal theory; Wavelet transforms; Epileptic seizures; Signal entropy; Time-frequency signal analysis; Wavelet analysis; Information theory
Año:2005
Volumen:347
Página de inicio:444
Página de fin:464
DOI: http://dx.doi.org/10.1016/j.physa.2004.08.085
Título revista:Physica A: Statistical Mechanics and its Applications
Título revista abreviado:Phys A Stat Mech Appl
ISSN:03784371
CODEN:PHYAD
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v347_n_p444_Rosso

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Citas:

---------- APA ----------
Rosso, O.A., Martin, M.T. & Plastino, A. (2005) . Evidence of self-organization in brain electrical activity using wavelet-based informational tools. Physica A: Statistical Mechanics and its Applications, 347, 444-464.
http://dx.doi.org/10.1016/j.physa.2004.08.085
---------- CHICAGO ----------
Rosso, O.A., Martin, M.T., Plastino, A. "Evidence of self-organization in brain electrical activity using wavelet-based informational tools" . Physica A: Statistical Mechanics and its Applications 347 (2005) : 444-464.
http://dx.doi.org/10.1016/j.physa.2004.08.085
---------- MLA ----------
Rosso, O.A., Martin, M.T., Plastino, A. "Evidence of self-organization in brain electrical activity using wavelet-based informational tools" . Physica A: Statistical Mechanics and its Applications, vol. 347, 2005, pp. 444-464.
http://dx.doi.org/10.1016/j.physa.2004.08.085
---------- VANCOUVER ----------
Rosso, O.A., Martin, M.T., Plastino, A. Evidence of self-organization in brain electrical activity using wavelet-based informational tools. Phys A Stat Mech Appl. 2005;347:444-464.
http://dx.doi.org/10.1016/j.physa.2004.08.085