EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia

Autor(es)

Alves, Caroline L. ; Pineda, Aruane M. ; Roster, Kirstin ; Thielemann, Christiane ; Rodrigues, Francisco A.

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF PHYSICS-COMPLEXITY; v. 3, n. 2, p. 13-pg., 2022-06-01.

Resumo

Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer's disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases, that use raw EEG time series, shows that our method provides the highest precision. Therefore, the application of deep neural networks on data from brain connections is a very promising method for the diagnosis of neurological disorders. (AU)

• Processo FAPESP: 19/23293-0 - Predição e inferência em sistemas complexos
• Beneficiário: Francisco Aparecido Rodrigues
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 19/26595-7 - Modelagem de doenças transmitidas por vetores
• Beneficiário: Kirstin Roster
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 19/22277-0 - Modelagem de dinâmicas sociais interagentes em redes complexas
• Beneficiário: Aruane Mello Pineda
• Modalidade de apoio: Bolsas no Brasil - Doutorado