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 perfor-mance 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.

Carlos Alberto Torres Naira and Cristian Jos´e L´opez Del Alamo, “Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 10(10), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101067

@article{Naira2019,
title = {Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101067},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101067},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {10},
author = {Carlos Alberto Torres Naira and Cristian Jos´e L´opez Del Alamo}
}