Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. With...

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Bibliographic Details
Main Authors: Md Junayed Hasan, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Applied Sciences
Subjects:
EEG signals
deep learning
sleep stage
classification
machine learning
Online Access:https://www.mdpi.com/2076-3417/10/21/7639

Internet

https://www.mdpi.com/2076-3417/10/21/7639

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