Literature DB >> 33017770

A review on current trends in automatic sleep staging through bio-signal recordings and future challenges.

Panteleimon Chriskos1, Christos A Frantzidis2, Christiane M Nday1, Polyxeni T Gkivogkli2, Panagiotis D Bamidis3, Chrysoula Kourtidou-Papadeli4.   

Abstract

Sleep staging is a vital process conducted in order to analyze polysomnographic data. To facilitate prompt interpretation of these recordings, many automatic sleep staging methods have been proposed. These methods rely on bio-signal recordings, which include electroencephalography, electrocardiography, electromyography, electrooculography, respiratory, pulse oximetry and others. However, advanced, uncomplicated and swift sleep-staging-evaluation is still needed in order to improve the existing polysomnographic data interpretation. The present review focuses on automatic sleep staging methods through bio-signal recording including current and future challenges.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Keywords:  Automatic staging techniques; Bio-signals; Feature extraction methods; Pre-processing; Sleep; Sleep datasets; Sleep staging rules

Year:  2020        PMID: 33017770     DOI: 10.1016/j.smrv.2020.101377

Source DB:  PubMed          Journal:  Sleep Med Rev        ISSN: 1087-0792            Impact factor:   11.609


  4 in total

1.  Rhythmicity in heart rate and its surges usher a special period of sleep, a likely home for PGO waves.

Authors:  Andreas A Ioannides; Gregoris A Orphanides; Lichan Liu
Journal:  Curr Res Physiol       Date:  2022-02-15

2.  Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages.

Authors:  Claus Metzner; Achim Schilling; Maximilian Traxdorf; Holger Schulze; Patrick Krauss
Journal:  Commun Biol       Date:  2021-12-10

Review 3.  Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

Authors:  Anuja Bandyopadhyay; Cathy Goldstein
Journal:  Sleep Breath       Date:  2022-03-09       Impact factor: 2.816

4.  MMASleepNet: A multimodal attention network based on electrophysiological signals for automatic sleep staging.

Authors:  Zheng Yubo; Luo Yingying; Zou Bing; Zhang Lin; Li Lei
Journal:  Front Neurosci       Date:  2022-08-16       Impact factor: 5.152

  4 in total

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