Cited by 284EEG and Brain-Computer InterfacesSleep and related disordersObstructive Sleep Apnea Research

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Automated sleep scoring: A review of the latest approaches

Fiorillo, Luigi, Puiatti, Alessandro, Papandrea, Michela, Ratti, Pietro-Luca, Favaro, Paolo, Roth, Corinne, Bargiotas, Panagiotis, Bassetti, Claudio L., Faraci, Francesca D.·2019·Sleep Medicine Reviews

DOI 10.1016/j.smrv.2019.07.007

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Abstract

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algorithms have been applied to sleep scoring for many years. As a result, several software products offer nowadays automated or semi-automated scoring services. However, the vast majority of the sleep physicians do not use them. Very recently, thanks to the increased computational power, deep learning has also been employed with promising results. Machine learning algorithms can undoubtedly reach a high accuracy in specific situations, but there are many difficulties in their introduction in the daily routine. In this review, the latest approaches that are applying deep learning for facilitating and accelerating sleep scoring are thoroughly analyzed and compared with the state of the art methods. Then the obstacles in introducing automated sleep scoring in the clinical practice are examined. Deep learning algorithm capabilities of learning from a highly heterogeneous dataset, in terms both of human data and of scorers, are very promising and should be further investigated.

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Authors: Fiorillo, L., Puiatti, A., Papandrea, M., Ratti, P.-L., Favaro, P., Roth, C., Bargiotas, P., Bassetti, C. L., & Faraci, F. D.
Affiliations: University of Bern (CH)
University of Applied Sciences and Arts of Southern Switzerland (CH)
Centre Hospitalier Universitaire de Martinique (MQ)
Corresponding author: Faraci, Francesca D.
ORCID: Fiorillo, Luigi
Puiatti, Alessandro
Papandrea, Michela
Ratti, Pietro-Luca
Favaro, Paolo
Roth, Corinne
Bargiotas, Panagiotis
Bassetti, Claudio L.
Faraci, Francesca D.
Venue: Sleep Medicine Reviews
Publisher: Elsevier BV
Published: 2019-12
Volume: 48
Pages: 101204
ISSN: 1087-0792 (ISSN-L)
DOI: 10.1016/j.smrv.2019.07.007
OpenAlex: W2969159609
Cited by: 284
References: 83
Citations by year: 2026: 29 · 2025: 43 · 2024: 43 · 2023: 64 · 2022: 46 · 2021: 38 · 2020: 20 · 2019: 1
FWCI: 3.33
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Sleep Medicine Reviews (publishedVersion)
PubMed (publishedVersion)
BORIS (University Library Bern) (submittedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
Health Sciences › Medicine › Physiology (medical) › Obstructive Sleep Apnea Research
Keywords: automated sleep scoring, polysomnography, deep learning, machine learning, clinical implementation barriers, sleep medicine, clinical decision support
MeSH terms: Machine Learning, Data Analysis, Algorithms, Diagnosis, Computer-Assisted, Humans, Sleep Wake Disorders, Sleep Stages, Polysomnography

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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What counts as a claim ›How we measure confidence ›How we check this ›

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Automated sleep scoring: A review of the latest approaches

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