Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG
Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG
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DOI：
10.3390/e14071186
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发表时间：
2012-07-01
期刊：
影响因子：
2.7
通讯作者：
Palamara, Isabella
中科院分区：
文献类型：
--
作者：
Morabito, Francesco Carlo;Labate, Domenico;Palamara, Isabella
提出了一种用于评估生理信号复杂性的原始多元多尺度方法。该技术能够将多通道数据的同步分析作为多尺度框架内的一个独特的块。基本的复杂性测量是通过使用排列熵来完成的，这是一种基于序数分析的时间序列处理方法。排列熵在概念上很简单，结构上对噪声和伪影具有鲁棒性，计算速度非常快，这与设计便携式诊断相关。由于源自生物系统的时间序列显示多个时空尺度的结构，因此所提出的技术可用于其他类型的生物医学信号分析。在这项工作中，在一个真实的、尽管相当有限的实验数据库上检查了区分与阿尔茨海默病患者和轻度认知障碍受试者与正常健康老年人相关的大脑状态的可能性。
An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer's disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.