The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
Open accessAbstract
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.
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Self-citationArtificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
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Self-citationData leakage in deep learning studies of translational EEG
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Deep learning applied to electroencephalogram data in mental disorders: a systematic review
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Metadata
- Authors
- Oh, S. L., Vicnesh, J., Ciaccio, E. J., Yuvaraj, R., & Acharya, U. R.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2019-07-18
- Volume
- 9
- Issue
- 14
- Pages
- 2870
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2963568316
- Cited by
- 304
- References
- 35
- FWCI
- 18.79
- Type
- journal article
- Language
- en
- Open access
- gold
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- Oh, S. L., Vicnesh, J., Ciaccio, E. J., Yuvaraj, R., & Acharya, U. R. (2019). Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals. Applied Sciences. https://doi.org/10.3390/app9142870
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