Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection
Author:
Affiliation:
1. Research Center for Augmented Intelligence, Research Institute of Artificial Intelligence, Zhejiang Laboratory, Hangzhou, China
2. School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW, Australia
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation
Link
http://xplorestaging.ieee.org/ielx7/7333/10031624/10064189.pdf?arnumber=10064189
Reference47 articles.
1. Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
2. EEG signal classification using PCA, ICA, LDA and support vector machines
3. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy
4. Analysis of EEG records in an epileptic patient using wavelet transform
5. EEG signal classification using wavelet feature extraction and a mixture of expert model
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4. Improving Seizure Detection by Integrating Feature Selection and Classification Techniques;2023 7th International Conference on Computer Applications in Electrical Engineering-Recent Advances (CERA);2023-10-27
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