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Content Provider | IET Digital Library |
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Author | Zhao, Wei Wang, Wenfeng |
Abstract | Epilepsy is a neurological disorder and generally detected by electroencephalogram (EEG) signals. The manual inspection of epileptic seizures is a time-consuming and laborious process. Extensive automatic detection algorithms were proposed by using traditional approaches, which show good accuracy for several specific EEG classification problems but perform poorly in others. To address this issue, the authors present a novel model, named SeizureNet, for robust detection of epileptic seizures using EEG signals based on convolutional neural network. Firstly, they utilise two convolutional neural networks to extract time-invariant features from single-channel EEG signals. Then, a fully connected layer is employed to learn high-level features. Finally, these features are supplied to a softmax layer to classify. They evaluated the model on a benchmark database provided by the University of Bonn and adopted a ten-fold cross-validation approach. The proposed model has achieved the accuracy of 98.50–100.00% in classifying non-seizure and seizure, 97.00–99.00% in classifying healthy, inter-ictal and ictal, and 95.84% in classifying among five-class EEG states. |
Starting Page | 119 |
Ending Page | 124 |
Page Count | 6 |
Volume Number | 2 |
e-ISSN | 25177567 |
Issue Number | Issue 3, Sep (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/ccs/2/3 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/ccs.2020.0011 |
Journal | Cognitive Computation and Systems |
Publisher | Shenzhen University The Institution of Engineering and Technology |
Publisher Date | 2020-06-01 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Benchmark Database Bioelectric Signal Biology And Medical Computing Convolutional Neural Nets Convolutional Neural Network Digital Signal Processing EEG Classification Problem Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Signal Electroencephalography Epileptic Seizures Extensive Automatic Detection Algorithm Feature Extraction High-level Feature Medical Disorders Medical Signal Detection Medical Signal Processing Neural Computing Technique Neurological Disorder Neurophysiology Nonseizure Classification Robust Detection SeizureNet Signal Classification Signal Detection Single-channel EEG Signal Softmax Layer Ten-fold Cross-validation Approach Time-invariant Feature Extraction |
Content Type | Text |
Resource Type | Article |
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