Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Abstract

A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network (CNN) model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ.

Automated detection of calcified plaque using higher‐order spectra cumulant technique in computer tomography angiography images

“…Deep learning is a part of the artificial intelligence approach that eliminates feature extraction, feature reduction, and categorization, incorporating the entire methodology into one typically convolutional model. Deep learning algorithms have been lucrative in the detection of arrhythmias, Parkinson's disease, depression, and schizophrenia . In the future, we plan to design a convolutional neural network model with a larger database to not only detect calcified plaque, but also to automatically calculate the calcium score.…”

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“…In fact, in the article from Lih Oh et al [2], a CNN was applied for the diagnosis of schizophrenia. In particular, electroencephalogram signals were collected from healthy patients and from patients presenting with schizophrenia.…”

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“…The dropout rate of 0.5 means that a neuron has a 0.5 probability to be dropped out during training [82]. Table 2 shows the layers used.…”

“…During training and testing, the model with the best validation accuracy will be considered. The fully connected layer will be used to perform the classification [82,83].…”

“…For better detection or classification of data by the kernels, deeper layers are used. The backpropagation algorithm withweights constantly adjusted is used to train CNN models, this helps to reduce errors and optimize training performance[82].…”