| Full text | |
| Author(s): |
Pineda, Aruane M.
[1]
;
Ramos, Fernando M.
[2]
;
Betting, Luiz Eduardo
[3]
;
Campanharo, Andriana S. L. O.
[1]
Total Authors: 4
|
| Affiliation: | [1] Sao Paulo State Univ UNESP, Inst Biosci, Dept Biostat, Botucatu, SP - Brazil
[2] Earth Syst Sci Ctr CCST, Natl Inst Space Res INPE, Sao Jose Dos Campos, SP - Brazil
[3] Sao Paulo State Univ UNESP, Botucatu Med Sch, Inst Biosci, Dept Neurol Psychol & Psychiat, Botucatu, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | PLoS One; v. 15, n. 6 JUN 5 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Known as a degenerative and progressive dementia, Alzheimer's disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here-clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index-showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients' scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs. (AU) | |
| FAPESP's process: | 16/17914-3 - Effect of antiepileptic drugs and of epileptiform discharges in the functional connectivity of patients with juvenile myoclonic epilepsy |
| Grantee: | Luiz Eduardo Gomes Garcia Betting |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 18/25358-9 - Use of complex networks for the automatic detection, diagnosis and classification of the Alzheimer's Disease |
| Grantee: | Andriana Susana Lopes de Oliveira Campanharo |
| Support Opportunities: | Regular Research Grants |