Computational EEG analysis for early Alzheimer's Disease diagnosis

• Grant number: 15/09510-7
• Support type: Regular Research Grants
• Duration: October 01, 2015 - September 30, 2017
• Field of knowledge: Engineering - Biomedical Engineering
• Principal researcher: 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)

Scientific publications

(6)

(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)

SAN-MARTIN, RODRIGO; CASTRO, LEONARDO ANDRADE; MENEZES, PAULO ROSSI; FRAGA, FRANCISCO JOSE; SIMOES, PRISCYLA WALESKA; SALUM, CRISTIANE. Meta-Analysis of Sensorimotor Gating Deficits in Patients With Schizophrenia Evaluated by Prepulse Inhibition Test. SCHIZOPHRENIA BULLETIN, v. 46, n. 6, p. 1482-1497, NOV 2020. Web of Science Citations: 0.

FRAGA, FRANCISCO J.; MAMANI, GODOFREDO QUISPE; JOHNS, ERIN; TAVARES, GUILHERME; FALK, TIAGO H.; PHILLIPS, NATALIE A. Early diagnosis of mild cognitive impairment and Alzheimer's with event-related potentials and event-related desynchronization in N-back working memory tasks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 164, p. 1-13, OCT 2018. Web of Science Citations: 4.

PEDROSO, RENATA VALLE; CANCELA, JOSE MARIA; AYAN, CARLOS; STEIN, ANGELICA MIKI; FUZARO, GILSON; COSTA, JOSE LUIZ RIANI; FRAGA, FRANCISCO J.; SANTOS-GALDUROZ, RUTH FERREIRA. Effects of Physical Exercise on the P300 of Elderly With Alzheimer's Disease. JOURNAL OF PHYSICAL ACTIVITY & HEALTH, v. 15, n. 6, p. 403-410, JUN 2018. Web of Science Citations: 2.

JÉSSICA NATULINE IANOF; FRANCISCO JOSÉ FRAGA; LEONARDO ALVES FERREIRA; RENATO TEODORO RAMOS; JOSÉ LUIZ CARLOS DEMARIO; REGINA BARATHO; LUÍS FERNANDO HINDI BASILE; RICARDO NITRINI; RENATO ANGHINAH. Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis. Dement. Neuropsychol., v. 11, n. 2, p. 176-185, Jun. 2017.

CASSANI, RAYMUNDO; FALK, TIAGO H.; FRAGA, FRANCISCO J.; CECCHI, MARCO; MOORE, DENNIS K.; ANGHINAH, RENATO. Towards automated electroencephalography-based Alzheimer's disease diagnosis using portable low-density devices. Biomedical Signal Processing and Control, v. 33, p. 261-271, MAR 2017. Web of Science Citations: 4.

KANDA, PAULO A. M.; OLIVEIRA, ELIEZYER F.; FRAGA, FRANCISCO J. EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer's. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 138, p. 13-22, JAN 2017. Web of Science Citations: 4.