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Use of complex networks for the automatic detection, diagnosis and classification of the Alzheimer's Disease

Abstract

Alzheimer's disease (AD) can be understood as a degenerative and progressive dementia of the Central Nervous System. It is irreversible and with unknown cause. This disease is mainly characterized by a progressive intellectual deterioration, loss of memory and disorientation in time and space. AD is the leading dementia among older people over 65 and affects approximately 25 million individuals worldwide. Currently, accurate diagnosis of AD can be made only through examination of brain tissue obtained by biopsy or necropsy. Since only after the patient's death it is possible to be sure that he or she had AD, the approximate diagnosis is made by excluding other causes of dementia. In parallel, studies have been developed for the study of AD with Electroencephalogram (EEG) databases, and in this sense, several methods of EEG data analysis have been proposed. However, the study of AD using EEG data is still a challenge, and consequently, it is necessary the propose of new methods in order to capture additional information about the disease. In this sense, in this research project we want to use the mapping of a time series into a complex network, recently proposed by Campanharo et al., in an unique application, that is, in the study of time series dynamics of patients with AD. More specifically, in the distinction between ageing and AD, in the detection of the most affected regions of the brain by AD and in classifying the stages of AD in diseased patients. (AU)

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VEICULO: TITULO (DATA)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PINEDA, ARUANE M.; RAMOS, FERNANDO M.; BETTING, LUIZ EDUARDO; CAMPANHARO, ANDRIANA S. L. O. Quantile graphs for EEG-based diagnosis of Alzheimer's disease. PLoS One, v. 15, n. 6 JUN 5 2020. Web of Science Citations: 0.
E. R. PINTO; E. G. NEPOMUCENO; A. S. L. O. CAMPANHARO. Impact of network topology on the spread of infectious diseases. TEMA (São Carlos), v. 21, n. 1, p. 95-115, Abr. 2020.

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