Study of evoked and endogenous potentials obtained from electroencephalographic re...
| Grant number: | 15/09510-7 |
| Support type: | Regular Research Grants |
| Duration: | October 01, 2015 - September 30, 2017 |
| Field of knowledge: | Engineering - Biomedical Engineering |
| Principal Investigator: | Francisco José Fraga da Silva |
| Grantee: | Francisco José Fraga da Silva |
| Home Institution: | Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil |
| Assoc. researchers: | Paulo Afonso Medeiros Kanda |
Abstract
While the overall growth in life expectancy is extremely positive, the aging population has been followed around the world, for it also leads to some disturbing consequences, such as the increasing prevalence of neurodegenerative diseases. Among those, by far the one with highest incidence is Alzheimer's Disease (AD), which early detection is a pressing need, since it allows to start a treatment that can significantly retard disease progression. Therefore, it is extremely important the development of methods for supporting early AD diagnosis. Furthermore, when it comes to early diagnosis, mild cognitive impairment (MCI) has been shown as an important risk factor in the development of AD. Recent statistics show that about 50% of all people who reported MCI symptoms to a doctor will develop AD within four years, with 12% annual conversion rate in average. Over the past decade, quantitative electroencephalography (qEEG) has emerged as a reliable and cost-effective tool for the diagnosis of cortical disorders such as AD and MCI, due its wide availability, low-cost and use of non-invasive procedures, thus allowing health professionals perform serial examinations and monitor changes in neurological status. In this research project we intend to develop, improve and validate biomarkers based on computer analysis of the EEG signals for early diagnosis of both MCI and AD. Consequently, it is expected that from our effort on the search for effective EEG-based biomarkers may arise a computer application that integrates them all and thus significantly increases the accuracy, sensitivity and specificity on the important and urgent task of AD early detection. (AU)