Use of the fractal dimension for the analysis of electroencephalographic time series |
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Authors: | A Accardo M Affinito M Carrozzi F Bouquet |
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Institution: | (1) Dip. di Elettrotecnica, Elettronica e Informatica (DEEI), Università di Trieste, Via Valerio, 10, I-34100 Trieste, Italy, IT;(2) Divisione di Neuropsichiatria, IRCSS Burlo Garofolo, Via dell’Istria 65/1, I-34100 Trieste, Italy, IT |
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Abstract: | Electroencephalogram (EEG) traces corresponding to different physiopathological conditions can be characterized by their
fractal dimension, which is a measure of the signal complexity. Generally this dimension is evaluated in the phase space by
means of the attractor dimension or other correlated parameters. Nevertheless, to obtain reliable values, long duration intervals
are needed and consequently only long-term events can be analysed; also much calculation time is required. To analyse events
of brief duration in real-time mode and to apply the results obtained directly in the time domain, thus providing an easier
interpretation of fractal dimension behaviour, in this work we optimize and propose a new method for evaluating the fractal
dimension. Moreover, we study the robustness of this evaluation in the presence of white or line noises and compare the results
with those obtained with conventional spectral methods. The non-linear analysis carried out allows us to investigate relevant
EEG events shorter than those detectable by means of other linear and non-linear techniques, thus achieving a better temporal
resolution. An interesting link between the spectral distribution and the fractal dimension value is also pointed out.
Received: 21 November 1996 / Accepted in revised form: 1 July 1997 |
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