EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia
This addresses the challenge of accurate diagnosis for mental disorders, which lack established clinical tests, potentially aiding clinicians and patients, but appears incremental as it builds on existing deep learning and EEG analysis techniques.
The authors tackled the problem of automatic diagnosis of brain disorders like Alzheimer's disease and schizophrenia by using EEG functional connectivity and deep learning, achieving high accuracy and outperforming methods using raw EEG time series.
Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis, but the absence of established clinical tests makes this task challenging. 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 to the diagnosis of neurological disorders.