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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Detaylı Bibliyografya
Asıl Yazarlar:	Md Junayed Hasan, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, Jong-Myon Kim
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2020-10-01
Seri Bilgileri:	Applied Sciences
Konular:	EEG signals deep learning sleep stage classification machine learning
Online Erişim:	https://www.mdpi.com/2076-3417/10/21/7639
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Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

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