An eleven-layered convolutional neural network model using EEG signals achieved classification accuracies of 98.07% and 81.26% for non-subject based and subject based testing for schizophrenia.
Does an eleven-layered CNN model accurately diagnose Schizophrenia using EEG signals in healthy subjects and SZ patients?
An eleven-layered CNN model can accurately classify EEG signals to detect Schizophrenia, potentially serving as a diagnostic tool for early detection.
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.
Oh et al. (Thu,) conducted a other in Schizophrenia (n=28). Eleven-layered convolutional neural network (CNN) model was evaluated on Classification accuracy. An eleven-layered convolutional neural network model using EEG signals achieved classification accuracies of 98.07% and 81.26% for non-subject based and subject based testing for schizophrenia.