Application of fractal theory in analysis of human electroencephalographic signals
Application of fractal theory in analysis of human electroencephalographic signals
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DOI：
10.1016/j.compbiomed.2007.12.004
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发表时间：
2008-03-01
影响因子：
7.7
通讯作者：
Uthayakumar, R.
中科院分区：
文献类型：
--
作者：
Paramanathan, P.;Uthayakumar, R.
在医学领域，复杂性度量主要集中在分析波形信号处理过程中的非线性模式。分形维数是衡量物体复杂性的一个指标，它可以很好地对这些波形信号的复杂性进行度量。它的应用在不同的领域，如医疗，图像和信号处理。已经提出了几种算法来计算波形的分形维数。我们已经评估了两个著名的算法，即Higuchi和Katz的性能。它们包含一些问题，确定初始和最终长度的缩放因子和他们的表现与脑电图（EEG）信号没有得到更好的结果。本文提出用分形维数作为分析和度量非线性脑电信号复杂性的有效工具。我们开发了一种基于尺寸度量关系（SMR）方法的算法。SMR算法可以用于检测大脑疾病，它通过分析信号的行为来定位受影响的大脑部分。该算法的效率，以定位关键的大脑部位（复发性癫痫发作部分）相比，其他分形维数算法。K-means聚类算法用于电极位置的分组。(C)2007爱思唯尔有限公司保留所有权利。
In medical discipline, complexity measure is focused on the analysis of nonlinear patterns in processing waveform signals. The complexity measure of such waveform signals is well performed by fractal dimension technique, which is an index for measuring the complexity of an object. Its applications are found in diverse fields like medical, image and signal processing. Several algorithms have been suggested to compute the fractal dimension of waveforms. We have evaluated the performance of the two famous algorithms namely Higuchi and Katz. They contain some problems of determining the initial and final length of scaling factors and their performance with electroencephalogram (EEG) signals did not give better results. In this paper, fractal dimension is proposed as an effective toot for analyzing and measuring the complexity of nonlinear human EEG signals. We have developed an algorithm based on size measure relationship (SMR) method. The SMR algorithm can be used to detect the brain disorders and it locates the affected brain portions by analyzing the behavior of signals. The efficiency of the algorithm to locate the critical brain sites (recurrent seizure portion) is compared to other fractal dimension algorithms. The K-means clustering algorithm is used for grouping of electrode positions. (C) 2007 Elsevier Ltd. All rights reserved.