Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEGReportar como inadecuado


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 - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

MecMat Department, University -Mediterranea- of Reggio Calabria, Reggio Calabria, 89100, Italy

2

DFMTFA Department, University of Messina, Messina, 98166, Italy

3

Faculty of Engineering, University of Pavia, Pavia, 27100, Italy





*

Author to whom correspondence should be addressed.



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. View Full-Text

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





Autor: Francesco Carlo Morabito 1,* , Domenico Labate 1, Fabio La Foresta 1, Alessia Bramanti 2, Giuseppe Morabito 3 and Isabella Palamara 1

Fuente: http://mdpi.com/



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