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Power spectral densities of nocturnal pulse oximetry signals differ in OSA patients with and without daytime sleepiness

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Item embargoed until 2021-07-20. Restrictions imposed by the publisher
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Date
2020
Author(s)
Kainulainen, Samu
Töyräs, Juha
Oksenberg, Arie
Korkalainen, Henri
Afara, Isaac O
Leino, Akseli
Kalevo, Laura
Nikkonen, Sami
Gadoth, Natan
Kulkas, Antti
Myllymaa, Sami
Leppänen, Timo
Unique identifier
10.1016/j.sleep.2020.07.015
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Self-archived article

Citation
Kainulainen, Samu. Töyräs, Juha. Oksenberg, Arie. Korkalainen, Henri. Afara, Isaac O. Leino, Akseli. Kalevo, Laura. Nikkonen, Sami. Gadoth, Natan. Kulkas, Antti. Myllymaa, Sami. Leppänen, Timo. (2020). Power spectral densities of nocturnal pulse oximetry signals differ in OSA patients with and without daytime sleepiness.  Sleep medicine, 73, 231-237. 10.1016/j.sleep.2020.07.015.
Rights
© 2020 Elsevier B.V.
Licensed under
CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract

Background
As nocturnal hypoxemia and heart rate variability are associated with excessive daytime sleepiness (EDS) related to OSA, we hypothesize that the power spectral densities (PSD) of nocturnal pulse oximetry signals could be utilized in the assessment of EDS. Thus, we aimed to investigate if PSDs contain features that are related to EDS and whether a convolutional neural network (CNN) could detect patients with EDS using self-learned PSD features.

Methods
A total of 915 OSA patients who had undergone polysomnography with multiple sleep latency test on the following day were investigated. PSDs for nocturnal blood oxygen saturation (SpO2), heart rate (HR), and photoplethysmogram (PPG), as well as power in the 15–35 mHz band in SpO2 (PSPO2) and HR (PHR), were computed. Differences in PSD features were investigated between EDS groups. Additionally, a CNN classifier was developed for identifying severe EDS patients based on spectral data.

Results
SpO2 power content increased significantly (p < 0.002) with increasing severity of EDS. Furthermore, a significant (p < 0.001) increase in HR-PSD was found in severe EDS (mean sleep latency < 5 min). Elevated odds of having severe EDS was found in PSPO2 (OR = 1.19–1.29) and PHR (OR = 1.81–1.83). Despite these significant spectral differences, the CNN classifier reached only moderate sensitivity (49.5%) alongside high specificity (80.4%) in identifying patients with severe EDS.

Conclusions
We conclude that PSDs of nocturnal pulse oximetry signals contain features significantly associated with OSA-related EDS. However, CNN-based identification of patients with EDS is challenging via pulse oximetry.

Subjects
obstructive sleep apnea   pulse analysis   spectrum analysis   machine learning   oximetry   
URI
https://erepo.uef.fi/handle/123456789/8335
Link to the original item
http://dx.doi.org/10.1016/j.sleep.2020.07.015
Publisher
Elsevier BV
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  • Luonnontieteiden ja metsätieteiden tiedekunta [1139]
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