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

  • 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.

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Author:Caroline Alves, Aruane M. Pineda, Kirstin Roster, Christiane Thielemann, Francisco A. Rodrigues
DOI:https://doi.org/DOI 10.1088/2632-072X/ac5f8d
Parent Title (English):Journal of Physics: complexity
Document Type:Article
Language:English
Year of Completion:2022
Release Date:2022/11/22
Tag:Machine learning; complex networks
GND Keyword:Hirnfunktionsstörung; Alzheimerkrankheit; Schizophrenie; Elektroencephalographie
Volume:2022
Issue:3
First Page:1
Last Page:13
Urheberrecht:0
Institutes:Fakultät Ingenieurwissenschaften / Labore / BIOMEMS Lab
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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