Classification of people who suffer schizophrenia and healthy people by EEG signals using deep learning

Carlos Alberto Torres Naira, Cristian José Lopez Del Alamo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

More than 21 million people worldwide suffer from schizophrenia. This serious mental disorder exposes people to stigmatization, discrimination, and violation of their human rights. Different works on classification and diagnosis of mental illnesses use electroencephalogram signals (EEG) because it reflects brain functioning, and how these diseases affect it. Due to the information provided by the EEG signals and the performance demonstrated by Deep Learning algorithms, the present work proposes a model for the classification of schizophrenic and healthy people through EEG signals using Deep Learning methods. Considering the properties of an EEG, high-dimensional and multichannel, we applied the Pearson Correlation Coefficient (PCC) to represent the relations between the channels, this way instead of using the large amount of data that an EEG provides, we used a shorter matrix as an input of a Convolutional Neural Network (CNN). Finally, results demonstrated that the proposed EEG-based classification model achieved Accuracy, Specificity, and Sensitivity of 90%, 90%, and 90%, respectively.

Original languageEnglish
Pages (from-to)511-516
Number of pages6
JournalInternational Journal of Advanced Computer Science and Applications
Volume10
Issue number10
DOIs
StatePublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 International Journal of Advanced Computer Science and Applications.

Keywords

  • Classification
  • Convolutional Neural Network (CNN)
  • Deep learning
  • Electroencephalogram signals (EEG)
  • Electroencephalography
  • Pearson Correlation Coefficient (PCC)
  • Schizophrenia
  • Universidad Nacional de San Agustín (UNSA)

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