Fractal and smooth complexities in electroencephalographic processing
Resumen: The importance of the electroencephalogram (EEG) rests upon the fact that it provides useful information of the normal and pathological brain functions. However, the relations among abnormal EEG, brain functions and disorders are not well known yet. We have proposed numerical quantifiers of the EEG signal, coming from the methodology of fractal mathematics and the theory of approximation. In the first part we describe an alternative to the computation of nonlinear dimensions for this kind of signals. The approach used here is based on a fractal interpolation of the data. In the second part, we describe a method for the computation of smooth complexities based on the interpolation of EEG signals by means of polynomial splines. This kind of functions is used to find quadrature formulas for the spectral moments. Both procedures are applied to treat the electroencephalographic discrimination of a group of children suffering from an Attention Deficit with Hyperactivity Disorder (ADHD).
Idioma: Inglés
DOI: 10.4172/2168-9679.1000198
Año: 2015
Publicado en: Journal of Applied and Computational Mathematics 4, 1 (2015), 198 [6 pp]
ISSN: 2168-9679

Factor impacto SCIMAGO:

Tipo y forma: Article (Published version)
Área (Departamento): Matemática Aplicada (Departamento de Matemática Aplicada)

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Exportado de SIDERAL (2015-12-18-16:07:11)

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 Record created 2015-12-18, last modified 2017-03-27


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