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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Autores principales:	Md Junayed Hasan, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, Jong-Myon Kim
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2020-10-01
Colección:	Applied Sciences
Materias:	EEG signals deep learning sleep stage classification machine learning
Acceso en línea:	https://www.mdpi.com/2076-3417/10/21/7639
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