Paper Info
Title
Characterization Of The Spontaneous Electroencephalographic Activity In Alzheimer'S Disease Using Disequilibria And Graph Theory
Abstract
The aim of this research was to study the changes that Alzheimer's disease (AD) elicits in the organization of brain networks. For this task, the electroencephalographic (EEG) activity from 32 AD patients and 25 healthy controls was analyzed. In a first step, a disequilibrium measure, the Euclidean distance (ED), was used to estimate the similarity between the spectral content of each pair of electrodes. In a second step, the similarity matrices were used to generate the corresponding graphs, from which two parameters were computed to characterize the network structure: the mean clustering coefficient and the mean path length. Results revealed significant changes (p<0.05) in ED values, as well as in the mean clustering coefficient and the mean path length, though they depend on the specific frequency band. Our findings suggest that AD is accompanied by a significant frequency-dependent alteration of brain network organization.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6610917
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
statistical analysis,electroencephalography,synchronization,sensors,graph theory,euclidean distance,alzheimer disease,organizations
Path length,Artificial intelligence,Clustering coefficient,Electroencephalography,Network structure,Graph theory,Graph,Computer vision,Pattern recognition,Frequency band,Euclidean distance,Statistics,Mathematics
Conference
Volume
ISSN
Citations 
2013
1557-170X
1
PageRank 
References 
Authors
0.48
2
6
Name
Order
Citations
PageRank
Jesús Poza171.38
Maria Garcia2102.13
Carlos Gómez38615.72
Alejandro Bachiller462.95
Alicia Carreres510.48
Hornero, R.6627.33