Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance
Author:
Affiliation:
1. Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Japan
2. Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Japan
Funder
University of Tokyo
JST CREST
JSPS KAKENHI
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10242175/10143185.pdf?arnumber=10143185
Reference68 articles.
1. A Review on Machine Learning for EEG Signal Processing in Bioengineering
2. Epileptic seizure detection in EEG signals using discriminative Stein kernel-based sparse representation
3. Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review
4. SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals
5. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
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