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EEG-Based Pathology Detection for Home Health Monitoring.
- Source :
- IEEE Journal on Selected Areas in Communications; Feb2021, Vol. 39 Issue 2, p603-610, 8p
- Publication Year :
- 2021
-
Abstract
- An electroencephalogram (EEG)-based remote pathology detection system is proposed in this study. The system uses a deep convolutional network consisting of 1D and 2D convolutions. Features from different convolutional layers are fused using a fusion network. Various types of networks are investigated; the types include a multilayer perceptron (MLP) with a varying number of hidden layers, and an autoencoder. Experiments are done using a publicly available EEG signal database that contains two classes: normal and abnormal. The experimental results demonstrate that the proposed system achieves greater than 89% accuracy using the convolutional network followed by the MLP with two hidden layers. The proposed system is also evaluated in a cloud-based framework, and its performance is found to be comparable with the performance obtained using only a local server. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 07338716
- Volume :
- 39
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Journal on Selected Areas in Communications
- Publication Type :
- Academic Journal
- Accession number :
- 148207701
- Full Text :
- https://doi.org/10.1109/JSAC.2020.3020654