Paper Info
Title
Multiscale Dispersion Entropy For The Regional Analysis Of Resting-State Magnetoencephalogram Complexity In Alzheimer'S Disease
Abstract
Alzheimer's disease (AD) is a progressive and irreversible brain disorder of the nervous system affecting memory, thinking, and emotion. It is the most important cause of dementia and an influential social problem in all the world. The complexity of brain recordings has been successfully used to help to characterize AD. We have recently introduced multiscale dispersion entropy (MDE) as a very fast and powerful tool to quantify the complexity of signals. The aim of this study is to assess the ability of MDE, in comparison with multiscale permutation entropy (MPE) and multiscale entropy (MSE), to discriminate 36 AD patients from 26 elderly age-matched control subjects using resting-state magnetoencephalogram (MEG) recordings. The results showed that MDE, unlike MSE, does not lead to undefined values. Moreover, the differences between the MDE values for AD palatines versus controls were more significant than their corresponding MSE-and MPE-based values. In addition, the computation time for our recently developed MDE was considerably less than that for MSE and even MPE.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037533
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Multiscale entropy,Disease,Pattern recognition,Computer science,Resting state fMRI,Permutation entropy,Artificial intelligence,Dementia
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
1
0.36
References 
Authors
4
5
Name
Order
Citations
PageRank
Hamed Azami114012.94
Eli Kinney-Lang212.05
Ahmed Ebied311.71
Alberto Fernández45311.82
Escudero Javier517427.45