An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 17; S. 4677
Hauptverfasser:	Shen, Huaming, Ran, Feng, Xu, Meihua, Guez, Allon, Li, Ang, Guo, Aiying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 19.08.2020 MDPI
Schlagworte:	Accuracy Algorithms Classification Datasets Decomposition EEG Methods Neural networks Sleep sleep stage state space model wavelet packet Wavelet transforms Belgium
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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