The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Automated sleep scoring: A review of the latest approaches
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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“Cognitive biases and the lack of diversity in the training data contribute to inter-and intra-scoring disagreement, posing limitations [63].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [63]
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Addressing bias in big data and AI for health care: A call for open science
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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
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Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
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Metadata
- 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
- Venue
- Sleep Medicine Reviews
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 48
- Pages
- 101204
- ISSN
- 1087-0792 (ISSN-L)
- 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
- 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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