Papini, Gabriele B.;
Fonseca, Pedro;
van Gilst, Merel M.;
van Dijk, Johannes P.;
Pevernagie, Dirk A. A.;
Bergmans, Jan W. M.;
Vullings, Rik;
Overeem, Sebastiaan
Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features
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Media type:
E-Article
Title:
Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features
Contributor:
Papini, Gabriele B.;
Fonseca, Pedro;
van Gilst, Merel M.;
van Dijk, Johannes P.;
Pevernagie, Dirk A. A.;
Bergmans, Jan W. M.;
Vullings, Rik;
Overeem, Sebastiaan
Published:
Springer Science and Business Media LLC, 2019
Published in:
Scientific Reports, 9 (2019) 1
Language:
English
DOI:
10.1038/s41598-019-53403-y
ISSN:
2045-2322
Origination:
Footnote:
Description:
AbstractObstructive sleep apnea (OSA) is a highly prevalent sleep disorder, which results in daytime symptoms, a reduced quality of life as well as long-term negative health consequences. OSA diagnosis and severity rating is typically based on the apnea-hypopnea index (AHI) retrieved from overnight poly(somno)graphy. However, polysomnography is costly, obtrusive and not suitable for long-term recordings. Here, we present a method for unobtrusive estimation of the AHI using ECG-based features to detect OSA-related events. Moreover, adding ECG-based sleep/wake scoring yields a fully automatic method for AHI-estimation. Importantly, our algorithm was developed and validated on a combination of clinical datasets, including datasets selectively including OSA-pathology but also a heterogeneous, “real-world” clinical sleep disordered population (262 participants in the validation set). The algorithm provides a good representation of the current gold standard AHI (0.72 correlation, estimation error of 0.56 ± 14.74 events/h), and can also be employed as a screening tool for a large range of OSA severities (ROC AUC ≥ 0.86, Cohen’s kappa ≥ 0.53 and precision ≥70%). The method compares favourably to other OSA monitoring strategies, showing the feasibility of cardiovascular-based surrogates for sleep monitoring to evolve into clinically usable tools.