Multivariate multi-scale permutation entropy for complexity analysis of Alzheimer's disease EEG
type: Item
instance of: scholarly article
title: Multivariate multi-scale permutation entropy for complexity analysis of Alzheimer's disease EEG
zbMATH Open document ID: 1314.92042
author: Francesco Carlo Morabito
author: Domenico Labate
author: Fabio La Foresta
author: Alessia Bramanti
author: Giuseppe Morabito
author: Isabella Palamara
published in: Entropy
publication date: 2014-09-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 92C30
Mathematics Subject Classification ID: 92C50
zbMATH DE Number: 6341012
zbMATH Keywords: complexity
zbMATH Keywords: permutation entropy
zbMATH Keywords: multi-scale entropy
zbMATH Keywords: multivariate permutation entropy
zbMATH Keywords: Alzheimer's disease
zbMATH Keywords: biomedical signal analysis
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e14071186
OpenAlex ID: W2078015570
cites work: Symbolic Analysis of High-Dimensional Time Series
DOI: 10.3390/E14071186
description: scientific article; zbMATH DE number 6341012