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
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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
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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
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Volumen: | 347
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Página de inicio: | 444
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Página de fin: | 464
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DOI: |
http://dx.doi.org/10.1016/j.physa.2004.08.085 |
Título revista: | Physica A: Statistical Mechanics and its Applications
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Título revista abreviado: | Phys A Stat Mech Appl
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ISSN: | 03784371
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CODEN: | PHYAD
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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