Automated sleep scoring: A review of the latest approaches
| Autor: | Paolo Favaro, Michela Papandrea, Luigi Fiorillo, Francesca Dalia Faraci, Claudio L. Bassetti, Panagiotis Bargiotas, Alessandro Puiatti, Corinne Roth, Pietro Luca Ratti |
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| Rok vydání: | 2019 |
| Předmět: |
Data Analysis
Sleep Wake Disorders Pulmonary and Respiratory Medicine Computer science Polysomnography Machine learning computer.software_genre Task (project management) Machine Learning 03 medical and health sciences 510 Mathematics 0302 clinical medicine Software Physiology (medical) Humans Diagnosis Computer-Assisted 610 Medicine & health 000 Computer science knowledge & systems Daily routine business.industry Deep learning Sleep scoring Clinical Practice 030228 respiratory system Neurology Sleep Stages Neurology (clinical) Sleep (system call) Artificial intelligence business computer Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Sleep Medicine Reviews. 48:101204 |
| ISSN: | 1087-0792 |
| DOI: | 10.1016/j.smrv.2019.07.007 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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