Paperdoi:10.1093/sleep/zsab142
Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography
SLEEP
(opens in a new tab)Riku Huttunen et al., "Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography", doi:10.1093/sleep/zsab142, CC BY 4.0
TL;DR
CNN+RNN PPG sleep staging used to quantify OSA-related sleep fragmentation without EEG setup, building on the Korkalainen PPG staging line. Focus is staging-derived fragmentation metrics for OSA severity/characterization rather than apnea event detection alone.
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Abstract
Study Objectives
To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an automated deep learning-based solution to assess OSA-related sleep fragmentation based on photoplethysmography (PPG) signal.
Methods
A combination of convolutional and recurrent neural networks was used for PPG-based sleep staging. The models were trained using two large clinical datasets from Israel (n = 2149) and Australia (n = 877) and tested separately on three-class (wake/NREM/REM), four-class (wake/N1 + N2/N3/REM), and five-class (wake/N1/N2/N3/REM) classification. The relationship between OSA severity categories and sleep fragmentation was assessed using survival analysis of mean continuous sleep. Overlapping PPG epochs were applied to artificially obtain denser hypnograms for better identification of fragmented sleep.
Results
Automatic PPG-based sleep staging achieved an accuracy of 83.3% on three-class, 74.1% on four-class, and 68.7% on five-class models. The hazard ratios for decreased mean continuous sleep compared to the non-OSA group obtained with Cox proportional hazards models with 5-s epoch-to-epoch intervals were 1.70, 3.30, and 8.11 for mild, moderate, and severe OSA, respectively. With EEG-based hypnograms scored manually with conventional 30-s epoch-to-epoch intervals, the corresponding hazard ratios were 1.18, 1.78, and 2.90.
Conclusions
PPG-based automatic sleep staging can be used to differentiate between OSA severity categories based on sleep continuity. The differences between the OSA severity categories become more apparent when a shorter epoch-to-epoch interval is used.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/
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APA
Huttunen, R., Leppänen, T., Duce, B., Oksenberg, A., Myllymaa, S., Töyräs, J., & Korkalainen, H. (2021). Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography. SLEEP. https://doi.org/10.1093/sleep/zsab142
BibTeX
@article{huttunen2021assessment,
title = {{Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography}},
author = {Huttunen, Riku and Leppänen, Timo and Duce, Brett and Oksenberg, Arie and Myllymaa, Sami and Töyräs, Juha and Korkalainen, Henri},
year = {2021},
journal = {SLEEP},
doi = {10.1093/sleep/zsab142}
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