EEG-Based Identification of Schizophrenia Using Deep Learning Techniques

A detection system for the diagnosis of Schizophrenia using machine learning and deep learning techniques are used in this study. Schizophrenia is a brain disorder which can be identified by various symptoms. Most common symptoms of Schizophrenia are speech disorder, laughing without any reason, crying without any reason, poor memory, lack of motivation etc. EEG signals are collected from human brains by placing electrodes (metal discs) on the scalp using a device named Electroencephalogram. It measures electrical activity of the brain, and the data is represented in the form of EEG signals. EEG signals are mainly used to study various diseases of the human brain. EEG signals of 14 healthy persons and 14 Schizophrenia patients are used. One machine learning classification algorithm, i.e. logistic regression and two deep learning models, i.e. convolutional neural network (CNN), and combination of multiple layers of convolutional neural networks and gated recurrent unit (GRU) are used to analyze the signals. Manual features are extracted from EEG signals and then feed into logistic regression to classify the signals. Extraction of Mel Frequency Cepstral Coefficient (MFCC) feature is done. Deep learning models are used to classify the EEG signals.