@InCollection{PinedaRamoBettCamp:2019:UsCoNe,
author = "Pineda, Aruane Mello and Ramos, Fernando Manoel and Betting, Luiz
Eduardo and Campanharo, Andriana S. L. O.",
title = "Use of complex networks for the automatic detection and the
diagnosis of Alzheimer’s disease",
booktitle = "Advances in Computational Intelligence",
publisher = "Springer Verlag",
year = "2019",
editor = "Rojas, Ignacio and Joya, Gonzalo and Catala, Andreu",
pages = "115--126",
keywords = "complex networks, Alzheimer disease.",
abstract = "Alzheimers disease (AD) is classified as a chronic neurological
disorder of the brain and affects approximately 25 million elderly
individuals worldwide. This disorder leads to a reduction in
peoples productivity and imposes restrictions on their daily
lives. Studies of AD often rely on electroencephalogram (EEG)
signals to provide information on the behavior of the brain.
Recently, a map from a time series to a network has been proposed
and that is based on the concept of transition probabilities; the
series results in a so-called quantile graph (QG). Here, this map,
which is also called the QG method, is applied for the automatic
detection of healthy patients and patients with AD from recorded
EEG signals. Our main goal is to illustrate how the differences in
dynamics in the EEG signals are reflected in the topology of the
corresponding QGs. Based on various network metrics, namely, the
clustering coefficient, the mean jump length and the betweenness
centrality, our results show that the QG method can be used as an
effective tool for automated diagnosis of Alzheimers disease.",
affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista
(UNESP)} and {Universidade Estadual Paulista (UNESP)}",
doi = "10.1007/978-3-030-20521-8_10",
url = "http://dx.doi.org/10.1007/978-3-030-20521-8_10",
isbn = "978-303020520-1",
language = "en",
targetfile = "10.1007@978-3-030-20521-810.pdf",
urlaccessdate = "23 abr. 2024"
}