Advanced search
Start date

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

Full text
Alves, Caroline L. ; Pineda, Aruane M. ; Roster, Kirstin ; Thielemann, Christiane ; Rodrigues, Francisco A.
Total Authors: 5
Document type: Journal article
Source: JOURNAL OF PHYSICS-COMPLEXITY; v. 3, n. 2, p. 13-pg., 2022-06-01.

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)

FAPESP's process: 19/23293-0 - Prediction and inference in complex systems
Grantee:Francisco Aparecido Rodrigues
Support Opportunities: Regular Research Grants
FAPESP's process: 19/26595-7 - Modeling vector borne diseases
Grantee:Kirstin Roster
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 19/22277-0 - Modeling interacting social dynamics in complex networks
Grantee:Aruane Mello Pineda
Support Opportunities: Scholarships in Brazil - Doctorate