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Entropy 2012, 14(7), 1186-1202; doi:10.3390/e14071186
Article

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

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Received: 1 April 2012 / Revised: 21 June 2012 / Accepted: 26 June 2012 / Published: 4 July 2012
(This article belongs to the Special Issue Concepts of Entropy and Their Applications)
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Abstract

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.
Keywords: complexity; permutation entropy; multi-scale entropy; multivariate permutation entropy; Alzheimer’s Disease; biomedical signal analysis complexity; permutation entropy; multi-scale entropy; multivariate permutation entropy; Alzheimer’s Disease; biomedical signal analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Morabito, F.C.; Labate, D.; La Foresta, F.; Bramanti, A.; Morabito, G.; Palamara, I. Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG. Entropy 2012, 14, 1186-1202.

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