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Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning
- Source :
- Intelligent Computing Methodologies ISBN: 9783319422961, ICIC (3)
- Publication Year :
- 2016
- Publisher :
- Springer International Publishing, 2016.
-
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.
- 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
Subjects
Details
- ISBN :
- 978-3-319-42296-1
- ISBNs :
- 9783319422961
- Database :
- OpenAIRE
- Journal :
- Intelligent Computing Methodologies ISBN: 9783319422961, ICIC (3)
- Accession number :
- edsair.doi...........a90e2e65e7e55aecf9e1f2e4f8237a02