A review on current trends in automatic sleep staging through bio-signal recordings and future challenges - 19/01/21

Doi : 10.1016/j.smrv.2020.101377

Panteleimon Chriskos ^a, Christos A. Frantzidis ^a,^b, Christiane M. Nday ^a, Polyxeni T. Gkivogkli ^a,^b, Panagiotis D. Bamidis ^a,^b,^⁎,^{^{1
Shared senior authorship.}} , Chrysoula Kourtidou-Papadeli ^b,^{^{1
Shared senior authorship.}}
^a Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
^b Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece

^∗Corresponding author. Laboratory of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, PO Box 376, 54124, Thessaloniki, Greece. Fax: +30 2310999702.Laboratory of Medical PhysicsSchool of MedicineAristotle University of ThessalonikiPO Box 376Thessaloniki54124Greece

Summary

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.

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Highlights

•	No consensus regarding the pre-processing steps applied to the bio-signals.
•	Majority of available methodologies rely on electroencephalographic signals.
•	Several approaches are based on wearable and minimally invasive devices.
•	Low availability of large scale datasets for sleep staging applications.
•	Future research may be focused on both clinical and commercial applications.

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

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A review on current trends in automatic sleep staging through bio-signal recordings and future challenges - 19/01/21

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