Studying sleep: towards the identification of hypnogram features that drive expert interpretation.

Autor:	van der Woerd C; Department Mathematics and Computer Science, Eindhoven University of Technology.; Remote Patient Management and Chronic Care, Philips Research, Eindhoven, The Netherlands., van Gorp H; Remote Patient Management and Chronic Care, Philips Research, Eindhoven, The Netherlands.; Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands., Dujardin S; Sleep Medicine Center, Kempenhaeghe, Heeze, The Netherlands., Sastry M; Academic Sleep Clinic, CIRO, Horn, The Netherlands., Garcia Caballero H; Department Mathematics and Computer Science, Eindhoven University of Technology., van Meulen F; Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.; Sleep Medicine Center, Kempenhaeghe, Heeze, The Netherlands., van den Elzen S; Department Mathematics and Computer Science, Eindhoven University of Technology., Overeem S; Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.; Sleep Medicine Center, Kempenhaeghe, Heeze, The Netherlands., Fonseca P; Remote Patient Management and Chronic Care, Philips Research, Eindhoven, The Netherlands.; Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Jazyk:	angličtina
Zdroj:	Sleep [Sleep] 2024 Mar 11; Vol. 47 (3).
DOI:	10.1093/sleep/zsad306
Abstrakt:	Study Objectives: Hypnograms contain a wealth of information and play an important role in sleep medicine. However, interpretation of the hypnogram is a difficult task and requires domain knowledge and "clinical intuition." This study aimed to uncover which features of the hypnogram drive interpretation by physicians. In other words, make explicit which features physicians implicitly look for in hypnograms. Methods: Three sleep experts evaluated up to 612 hypnograms, indicating normal or abnormal sleep structure and suspicion of disorders. ElasticNet and convolutional neural network classification models were trained to predict the collected expert evaluations using hypnogram features and stages as input. The models were evaluated using several measures, including accuracy, Cohen's kappa, Matthew's correlation coefficient, and confusion matrices. Finally, model coefficients and visual analytics techniques were used to interpret the models to associate hypnogram features with expert evaluation. Results: Agreement between models and experts (Kappa between 0.47 and 0.52) is similar to agreement between experts (Kappa between 0.38 and 0.50). Sleep fragmentation, measured by transitions between sleep stages per hour, and sleep stage distribution were identified as important predictors for expert interpretation. Conclusions: By comparing hypnograms not solely on an epoch-by-epoch basis, but also on these more specific features that are relevant for the evaluation of experts, performance assessment of (automatic) sleep-staging and surrogate sleep trackers may be improved. In particular, sleep fragmentation is a feature that deserves more attention as it is often not included in the PSG report, and existing (wearable) sleep trackers have shown relatively poor performance in this aspect. (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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