| Full text | |
| Author(s): |
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. |
| Abstract | |
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 |