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

TL;DR

This paper introduces a deep learning approach using EEG functional connectivity matrices to automatically diagnose Alzheimer's disease and schizophrenia, achieving higher accuracy than traditional raw EEG analysis.

Contribution

The study demonstrates that deep neural networks applied to EEG connectivity data significantly improve diagnostic accuracy for brain disorders compared to conventional methods.

Findings

High classification accuracy for Alzheimer's and schizophrenia

Deep connectivity-based analysis outperforms raw EEG methods

Deep learning shows promise for neurological disorder diagnosis

Abstract

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.