EEG Pathology Detection Based on Deep Learning
With the advancement of machine learning technologies, particularly deep learning, the automated systems to assist human life are flourishing. In this paper, we propose an automatic electroencephalogram (EEG) pathology detection system based on deep learning. Various types of pathologies can affect...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.27781-27788 |
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| creator | Alhussein, Musaed Muhammad, Ghulam Hossain, M. Shamim |
| description | With the advancement of machine learning technologies, particularly deep learning, the automated systems to assist human life are flourishing. In this paper, we propose an automatic electroencephalogram (EEG) pathology detection system based on deep learning. Various types of pathologies can affect brain signals. Thus, the brain signals captured in the form of EEG signals can indicate whether a person suffers from pathology or not. In the proposed system, the raw EEG signals are processed in the form of a spatio-temporal representation. The spatio-temporal form of the EEG signals is the input to a convolutional neural network (CNN). Two different CNN models, namely, a shallow model and a deep model, are investigated using transfer learning. A fusion strategy based on a multilayer perceptron is also investigated. The experimental results on the Temple University Hospital EEG Abnormal Corpus v2.0.0 show that the proposed system with the deep CNN model and fusion achieves 87.96% accuracy, which is better than some reported accuracy rates on the same corpus. |
| doi_str_mv | 10.1109/ACCESS.2019.2901672 |
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| subjects | Artificial neural networks Brain Brain modeling Deep learning EEG pathology EEG processing Electroencephalography Intelligent sensors Machine learning Medical services Model accuracy Multilayer perceptrons Pathology Real-time systems Signal processing Temple University Hospital EEG Abnormal Corpus |
| title | EEG Pathology Detection Based on Deep Learning |
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