ReviewPhysiological measurement2018
Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity.
Review in Physiological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Hypoxic Load and Average Oxygen-Saturation During Sleep and Wake Are Associated With Cognitive Function in Obstructive Sleep Apnea.Journal of sleep research · 2026Article
- Article
- Obstructive Sleep Apnea Endophenotypes.Sleep science (Sao Paulo, Brazil) · 2025Review
- Predicting All-Cause Mortality in Patients With Obstructive Sleep Apnea Using Sleep-Related Features: A Machine-Learning Approach.Journal of clinical neurology (Seoul, Korea) · 2025Article
- Tackling obstructive sleep apnea with pharmacotherapeutics: expert guidance.Expert opinion on pharmacotherapy · 2024Review
- Sleep Apnea Physiological Burdens and Cardiovascular Morbidity and Mortality.American journal of respiratory and critical care medicine · 2023Article
- Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea.International journal of molecular sciences · 2022Article
- Article
- Immediate Physiological Responses to Inspiratory Flow Limited Events in Mild Obstructive Sleep Apnea.Annals of the American Thoracic Society · 2022Article
- Pulse arrival time, a novel sleep cardiovascular marker: the multi-ethnic study of atherosclerosis.Thorax · 2021Article
- The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea.Frontiers in psychiatry · 2021Review
- Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network.Nature and science of sleep · 2021Article
- Deep learning applied to polysomnography to predict blood pressure in obstructive sleep apnea and obesity hypoventilation: a proof-of-concept study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020Article
- Reinventing polysomnography in the age of precision medicine.Sleep medicine reviews · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundObstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.
objectiveIn this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA. SIGNIFICANCE: Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.