Trial reportPloS one2016
Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.
Trial report in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01161381 (Evaluation Of Patients With Suspected Obstructive Sleep Apnea - Hypopnea Syndrome Using Two Models Based on Nonlinear Analysis Of Respiratory Signals), which is not on this map. Cited by 6 papers.
What it found
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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.
The trial behind it
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.
Evaluation Of Patients With Suspected Obstructive Sleep Apnea - Hypopnea Syndrome Using Two Models Based on Nonlinear Analysis Of Respiratory Signals
Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Leveraging intrinsic non-sinusoidal patterns to infer search behavior to predict exposure to respiratory stressors.American journal of physiology. Lung cellular and molecular physiology · 2025Article
- Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea.International journal of molecular sciences · 2022Article
- Application of automatic detection based on overnight airflow and blood oxygen in patients with sleep disordered breathing.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2021Article
- Article
- Dynamic structure of lower limb joint angles during walking post-stroke.Journal of biomechanics · 2018Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionObstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND
methodsPatients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI.
resultsA linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION: We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome.
conclusionsReliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology.
trial registrationClinicalTrials.gov NCT01161381.
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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.