Deep Learning-Based Diagnosis Of Schizophrenia

Abstract

People from different age groups suffer from a variety of mental health disorders but most of these come without a cause and are very difficult to cure. One such disorder is Schizophrenia. More than 20 million people around the world suffer from this disorder. In order to get a better understanding of such mental health disorders and to help in the study of the cure, here the EEG of the affected and normal patients are studied and classified. The intelligence of Neural Networks is deployed in the classification of the EEG signals as they provide a wide variety of algorithms to extract the features and to classify the data which in turn helps in the process of identification of abnormalities present in the EEG signal of the schizophrenic subjects when compared to normal subjects. Here, the LSTM algorithm which is a type of RNN architecture is deployed to classify the EEG of schizophrenic subjects and normal subjects. Further, the EEG signal is pre-processed using various techniques and the data is cleaned to study the major brain waves of the EEG signal and the absolute and relative power of each of the brain waves is calculated and graphically observed to understand the behaviour of the EEG of Schizophrenic subjects and to propose therapeutic guidance to relieve such patients from stress.