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Article

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

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Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 535 Clementi Road 599489, Singapore
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Department of Medicine, Columbia University, 180 Fort Washington Avenue, New York, NY 10032, USA
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School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore
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School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road 599494, Singapore
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School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya 47500, Malaysia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2870; https://doi.org/10.3390/app9142870
Received: 5 May 2019 / Revised: 28 June 2019 / Accepted: 9 July 2019 / Published: 18 July 2019
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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. View Full-Text
Keywords: automated detection system; schizophrenia; deep learning; deep learning algorithm automated detection system; schizophrenia; deep learning; deep learning algorithm
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MDPI and ACS Style

Oh, S.L.; Vicnesh, J.; Ciaccio, E.J.; Yuvaraj, R.; Acharya, U.R. Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals. Appl. Sci. 2019, 9, 2870. https://doi.org/10.3390/app9142870

AMA Style

Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR. Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals. Applied Sciences. 2019; 9(14):2870. https://doi.org/10.3390/app9142870

Chicago/Turabian Style

Oh, Shu L., Jahmunah Vicnesh, Edward J. Ciaccio, Rajamanickam Yuvaraj, and U R. Acharya 2019. "Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals" Applied Sciences 9, no. 14: 2870. https://doi.org/10.3390/app9142870

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