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). Automated sleep scoring: A review of the latest approaches. Sleep medicine reviews, 48, p. 101204. Elsevier 10.1016/j.smrv.2019.07.007

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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.

Item Type:

Journal Article (Review Article)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Favaro, Paolo; Roth Wälti, Corinne; Bargiotas, Panagiotis and Bassetti, Claudio

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics
600 Technology > 610 Medicine & health

ISSN:

1087-0792

Publisher:

Elsevier

Language:

English

Submitter:

Chantal Kottler

Date Deposited:

22 Nov 2019 13:56

Last Modified:

03 Dec 2019 01:32

Publisher DOI:

10.1016/j.smrv.2019.07.007

PubMed ID:

31491655

Uncontrolled Keywords:

Artificial intelligence Automated and semi-automated systems Deep learning Shallow learning Sleep scoring

BORIS DOI:

10.7892/boris.134847

URI:

https://boris.unibe.ch/id/eprint/134847

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