Duration of respiratory events in obstructive sleep apnea: In search of paradoxical results

Oksenberg, Arie; Leppänen, Timo

doi:10.1016/j.smrv.2022.101728

Cited by 4 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sleep Apnea-Hypopnea Syndrome (SAHS) falls within the Sleep-related Breathing Disorder (SBD) spectrum. Patients affected by SAHS experience numerous involuntary respiratory pauses during the night referred to as "apneic events", which must last between 10 seconds to 2 minutes (typically around 20 to 40 seconds [1]) to be considered clinically significant. The duration of apneic events is influenced by several factors such as gender, obesity, age, sleep position, pharyngeal collapsibility, loop gain, with many of these factors interacting with each other [2] [3].…”

Section: A Sleep Apnea-hypopnea Syndromementioning

confidence: 99%

Sleep apnea events recognition based on polysomnographic recordings: a large-scale multi-channel machine learning approach

Porta,

Scafa,

Papandrea

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: A Sleep Apnea-hypopnea Syndromementioning

confidence: 99%

Sleep apnea events recognition based on polysomnographic recordings: a large-scale multi-channel machine learning approach

Porta,

Scafa,

Papandrea

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach

La Porta,

Scafa,

Papandrea

et al. 2025

IEEE Open J. Eng. Med. Biol.

View full text Add to dashboard Cite

Goal: The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality. Methods: The present work proposes a machine learning-based approach for automatically recognizing apneic events in subjects affected by sleep apnea-hypopnea syndrome. It embraces a vast and diverse pool of subjects, the Wisconsin Sleep Cohort (WSC) database. Results: An overall accuracy of 87.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 1.8% is reached for the event detection task, significantly higher than other works in literature performed over the same dataset. The distinction between different types of apnea was also studied, obtaining an overall accuracy of 62.9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 4.1%. Conclusions: The proposed approach for sleep apnea events recognition, validated over a wide pool of subjects, enlarges the landscape of possibilities for sleep apnea events recognition, identifying a subset of signals that improves State-of-the-art performance and guarantees simple interpretation.

show abstract

Assessing the impact of apnea duration on the relationship between obstructive sleep apnea and hearing loss

Jo,

Lee

2024

PLoS ONE

View full text Add to dashboard Cite

Objectives The relationship between obstructive sleep apnea (OSA) and hearing loss (HL) remains uncertain. This study aimed to investigate the relationship between OSA and HL, and to identify which factors play a key role. Methods A retrospective review was conducted of 90 subjects diagnosed with OSA. These subjects underwent overnight polysomnography (PSG) and pure-tone audiometry at a single institution from February 2014 to November 2023. Hearing evaluations involved the comparison of OSA subjects with a non-OSA group, identified through national data utilizing the STOP-BANG questionnaire (SBQ) and age-sex 1:1 matching. Subsequently, individuals with OSA were categorized into HL and non-HL groups. Comparisons were made to ascertain differences in PSG parameters, followed by regression analysis to assess their actual impact. Results The OSA group exhibited elevated hearing thresholds across all frequencies compared to the non-OSA group. Furthermore, classification of OSA subjects into the HL and non-HL groups revealed a statistically significant increase in apnea duration in the HL group for all-frequency and high-frequency cases (p = 0.038, 0.006). Multiple linear regression analysis, adjusting for age and sex, revealed a significant influence of apnea duration on HL in both all-frequency and high-frequency cases (ß = 0.404, p = 0.002; ß = 0.425, p = 0.001). Conclusion These findings underscore the significant association between OSA and reduced auditory function, with apnea duration standing out as a crucial factor contributing to hearing loss. Our results suggest that prolonged apnea duration may be a marker of chronic hypoxic damage in patients with OSA, further clarifying its potential role in the development of hearing loss.

show abstract

Duration of respiratory events in obstructive sleep apnea: In search of paradoxical results

Cited by 4 publications

References 44 publications

Sleep apnea events recognition based on polysomnographic recordings: a large-scale multi-channel machine learning approach

Sleep apnea events recognition based on polysomnographic recordings: a large-scale multi-channel machine learning approach

Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach

Assessing the impact of apnea duration on the relationship between obstructive sleep apnea and hearing loss

Contact Info

Product

Resources

About