Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
- Resource Type
- article
- Authors
- Shu Lih Oh; Jahmunah Vicnesh; Edward J Ciaccio; Rajamanickam Yuvaraj; U Rajendra Acharya
- Source
- Applied Sciences, Vol 9, Iss 14, p 2870 (2019)
- Subject
- automated detection system
schizophrenia
deep learning
deep learning algorithm
Technology
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
- Language
- English
- ISSN
- 2076-3417
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.