1. Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning
- Author
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Si-you Li, Shuqun Ye, Mei-zhen Zhang, Xiu-mei Huang, Wen-Sheng Chen, Yun Xue, and Qin Lin
- Subjects
medicine.diagnostic_test ,business.industry ,Computer science ,Deep learning ,Speech recognition ,Feature extraction ,Feature selection ,Pattern recognition ,02 engineering and technology ,Electroencephalography ,Autoencoder ,03 medical and health sciences ,ComputingMethodologies_PATTERNRECOGNITION ,0302 clinical medicine ,Softmax function ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,020201 artificial intelligence & image processing ,Artificial intelligence ,Epileptic seizure ,medicine.symptom ,business ,Classifier (UML) ,030217 neurology & neurosurgery - Abstract
Automatic detection of epileptic seizure plays an important role in the diagnosis of epilepsy for it can obtain invisible information of epileptic electroencephalogram (EEG) signals exactly and reduce the heavy burdens of doctors efficiently. Current automatic detection technologies are almost shallow learning models that are insufficient to learn the complex and non-stationary epileptic EEG signals. Moreover, most of their feature extraction or feature selection methods are supervised and depend on domain-specific expertise. To solve these problems, we proposed a novel framework for the automatic detection of epileptic EEG by using stacked sparse autoencoder (SSAE) with a softmax classifier. The proposed framework firstly learns the sparse and high level representations from the preprocessed data via SSAE, and then send these representations into softmax classifier for training and classification. To verify the performance of this framework, we adopted the epileptic EEG datasets to conduct experiments. The simulation results with an average accuracy of 96 % illustrated the effectiveness of the proposed framework.
- Published
- 2016