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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Vydané v:	Sensors (Basel, Switzerland) Ročník 20; číslo 17; s. 4677
Hlavní autori:	Shen, Huaming, Ran, Feng, Xu, Meihua, Guez, Allon, Li, Ang, Guo, Aiying
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 19.08.2020 MDPI
Predmet:	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
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