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Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features

Gabriele B Papini, Pedro Fonseca, Merel M van Gilst, Johannes P van Dijk, Dirk Pevernagie (UGent) , Jan WM Bergmans, Rik Vullings and Sebastiaan Overeem
(2019) SCIENTIFIC REPORTS. 9.
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Organization
Abstract
Obstructive 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.
Keywords
HEART-RATE-VARIABILITY, CARDIORESPIRATORY COORDINATION, APPROXIMATE ENTROPY, RESPIRATORY EVENTS, SPECTRAL-ANALYSIS, TIME-SERIES, ECG, SEVERITY, PERIOD, CLASSIFICATION

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MLA
Papini, Gabriele B., et al. “Estimation of the Apnea-Hypopnea Index in a Heterogeneous Sleep-Disordered Population Using Optimised Cardiovascular Features.” SCIENTIFIC REPORTS, vol. 9, 2019.
APA
Papini, G. B., Fonseca, P., van Gilst, M. M., van Dijk, J. P., Pevernagie, D., Bergmans, J. W., … Overeem, S. (2019). Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features. SCIENTIFIC REPORTS, 9.
Chicago author-date
Papini, Gabriele B, Pedro Fonseca, Merel M van Gilst, Johannes P van Dijk, Dirk Pevernagie, Jan WM Bergmans, Rik Vullings, and Sebastiaan Overeem. 2019. “Estimation of the Apnea-Hypopnea Index in a Heterogeneous Sleep-Disordered Population Using Optimised Cardiovascular Features.” SCIENTIFIC REPORTS 9.
Chicago author-date (all authors)
Papini, Gabriele B, Pedro Fonseca, Merel M van Gilst, Johannes P van Dijk, Dirk Pevernagie, Jan WM Bergmans, Rik Vullings, and Sebastiaan Overeem. 2019. “Estimation of the Apnea-Hypopnea Index in a Heterogeneous Sleep-Disordered Population Using Optimised Cardiovascular Features.” SCIENTIFIC REPORTS 9.
Vancouver
1.
Papini GB, Fonseca P, van Gilst MM, van Dijk JP, Pevernagie D, Bergmans JW, et al. Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features. SCIENTIFIC REPORTS. 2019;9.
IEEE
[1]
G. B. Papini et al., “Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features,” SCIENTIFIC REPORTS, vol. 9, 2019.
@article{8646355,
  abstract     = {Obstructive 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.},
  articleno    = {17448},
  author       = {Papini, Gabriele B and Fonseca, Pedro and van Gilst, Merel M and van Dijk, Johannes P and Pevernagie, Dirk and Bergmans, Jan WM and Vullings, Rik and Overeem, Sebastiaan},
  issn         = {2045-2322},
  journal      = {SCIENTIFIC REPORTS},
  keywords     = {HEART-RATE-VARIABILITY,CARDIORESPIRATORY COORDINATION,APPROXIMATE ENTROPY,RESPIRATORY EVENTS,SPECTRAL-ANALYSIS,TIME-SERIES,ECG,SEVERITY,PERIOD,CLASSIFICATION},
  language     = {eng},
  pages        = {16},
  title        = {Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features},
  url          = {http://dx.doi.org/10.1038/s41598-019-53403-y},
  volume       = {9},
  year         = {2019},
}
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