神经生理学
计算机科学
维数(图论)
分形维数
过程(计算)
斯科普斯
临床神经生理学
分形
人工智能
非线性系统
数据挖掘
脑电图
机器学习
模式识别(心理学)
梅德林
数学
神经科学
心理学
法学
纯数学
物理
数学分析
操作系统
量子力学
政治学
作者
Srdjan Kesić,Slavica Spasic
标识
DOI:10.1016/j.cmpb.2016.05.014
摘要
For more than 20 years, Higuchi's fractal dimension (HFD), as a nonlinear method, has occupied an important place in the analysis of biological signals. The use of HFD has evolved from EEG and single neuron activity analysis to the most recent application in automated assessments of different clinical conditions. Our objective is to provide an updated review of the HFD method applied in basic and clinical neurophysiological research.This article summarizes and critically reviews a broad literature and major findings concerning the applications of HFD for measuring the complexity of neuronal activity during different neurophysiological conditions. The source of information used in this review comes from the PubMed, Scopus, Google Scholar and IEEE Xplore Digital Library databases.The review process substantiated the significance, advantages and shortcomings of HFD application within all key areas of basic and clinical neurophysiology. Therefore, the paper discusses HFD application alone, combined with other linear or nonlinear measures, or as a part of automated methods for analyzing neurophysiological signals.The speed, accuracy and cost of applying the HFD method for research and medical diagnosis make it stand out from the widely used linear methods. However, only a combination of HFD with other nonlinear methods ensures reliable and accurate analysis of a wide range of neurophysiological signals.
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