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

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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Bibliographic Details
Main Authors: Huaming Shen, Feng Ran, Meihua Xu, Allon Guez, Ang Li, Aiying Guo
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sensors
Subjects:
EEG
sleep stage
wavelet packet
state space model
Online Access:https://www.mdpi.com/1424-8220/20/17/4677

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