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

• Grant number: 19/05787-5
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: June 01, 2019
• End date: December 31, 2020
• Field of knowledge: Engineering - Biomedical Engineering
• Principal Investigator: Andriana Susana Lopes de Oliveira Campanharo
• Grantee: Ana Luiza Milani
• Host Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

Alzheimer's disease (AD), first characterized by the German neuropathologist Alois Alzheimer in 1907, can be understood as a degenerative and progressive dementia of the Central Nervous System, irreversible and that causes loss of memory and several cognitive disorders. 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, however its 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 the AD using EEG data. More specifically, in the differentiation of ageing and AD and in the detection of the most affected regions of the brain by AD.