Use of the fractal dimension for the analysis of electroencephalographic time series

被引：286
作者：
Accardo A. [1 ]
Affinito M. [2 ]
Carrozzi M. [2 ]
Bouquet F. [2 ]
机构：
[1] Dip. di Elettrotecnica, Elettronica e Informatica (DEEI), Università di Trieste, I-34100 Trieste, Via Valerio
[2] Divisione di Neuropsichiatria, IRCSS Burlo Garofolo, I-34100 Trieste
来源：
Biological Cybernetics | 1997年 / 77卷 / 5期
关键词：
Attractor Dimension; Phase Space; Fractal Dimension; Temporal Resolution; Spectral Method;
D O I：
10.1007/s004220050394
中图分类号：
学科分类号：
摘要：
Electroencephalogram (EEC) 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.
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页码：339 / 350
页数：11
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