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

@article{Alves2021EEGFC,
  title={EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia},
  author={Caroline Lourenco Alves and Aruane Mello Pineda and Kirstin Roster and Christiane Thielemann and Francisco A Rodrigues},
  journal={Journal of Physics: Complexity},
  year={2021},
  volume={3}
}
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… 
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Towards better intelligent implementation of Schizophrenia prediction using federated deep learning framework
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A hybrid CNN–Bi- LSTM automated system that analyses EEG statistical data and performs the prediction of susceptibility to develop SZ, which has a high level of classification accuracy when compared to most existing machine learning models.

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