Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

Entropy

Francesco Carlo Morabito²  Domenico Labate²  Fabio La Foresta²  Alessia Bramanti¹  Giuseppe Morabito³ 

[1] DFMTFA Department, University of Messina, Messina, 98166, Italy; E-Mail:;MecMat Department, University “Mediterranea” of Reggio Calabria, Reggio Calabria, 89100, Italy; E-Mails:;Faculty of Engineering, University of Pavia, Pavia, 27100, Italy; E-Mail:

关键词: complexity;    permutation entropy;    multi-scale entropy;    multivariate permutation entropy;    Alzheimer’s Disease;    biomedical signal analysis;   

DOI  :  10.3390/e14071186

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.