Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

Gasa, Mercè; Salord, Neus; Fontanilles, Eva; Ramos, Sandra Pérez; Prado, E B A; Pallarès, Natàlia; Santos, Salud; Monasterio, Carmen

doi:10.1016/j.arbres.2023.07.007

Archivos de Bronconeumología

2023

DOI: 10.1016/j.arbres.2023.07.007

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Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

Mercè Gasa

Neus Salord

Eva Fontanilles

et al.

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2024

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Cited by 2 publications

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A comparative analysis of unsupervised machine‐learning methods in PSG‐related phenotyping

Ghorvei,

Karhu,

Hietakoste

et al. 2024

Journal of Sleep Research

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SummaryObstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well‐known clustering methods: Agglomerative Hierarchical Clustering; K‐means; Fuzzy C‐means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C‐means and K‐means had the strongest (κ = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (κ = 0.51) between each other. The K‐means showed the best clustering performance, followed by the Fuzzy C‐means in most evaluation criteria. Moreover, Fuzzy C‐means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C‐means, in the application of obstructive sleep apnea phenotyping.

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A comparative analysis of unsupervised machine‐learning methods in PSG‐related phenotyping

Ghorvei,

Karhu,

Hietakoste

et al. 2024

Journal of Sleep Research

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Performance Comparison of Standard Polysomnographic Parameters Used in the Diagnosis of Sleep Apnea

Arslan Tuncer,

Çiçek,

Tuncer

2024

Turkish Journal of Science and Technology

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Obstructive sleep apnea (OSAS), which is one of the leading sleep disorders and can result in death if not diagnosed and treated early, is most often confused with snoring. OSAS disease, the prevalence of which varies between 0.9% and 1.9% in Turkey, is a serious health problem that occurs as a result of complete or partial obstruction of the respiratory tract during sleep, resulting in sleep disruption, poor quality sleep, paralysis and even death in sleep. Polysomnography signal recordings (PSG) obtained from sleep laboratories are used for the diagnosis of OSAS, which is related to factors such as the individual's age, gender, neck diameter, smoking-alcohol consumption, and the occurrence of other sleep disorders. Polysomnography is used in the diagnosis and treatment of sleep disorders such as snoring, sleep apnea, parasomnia (abnormal behaviors during sleep), narcolepsy (sleep attacks that develop during the day) and restless legs syndrome. It allows recording various parameters such as brain waves, eye movements, heart and chest activity measurement, respiratory activities, and the amount of oxygen in the blood with the help of electrodes placed in different parts of the patient's body during night sleep. In this article, the performance of PSG signal data for the diagnosis of sleep apnea was examined on the basis of both signal parameters and the method used. First, feature extraction was made from PSG signals, then the feature vector was classified with artificial neural networks, Support Vector Machine (SVM), K-Nearest Neighbors (k-NN) and Logistic Regression (LR).

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Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

Cited by 2 publications

References 28 publications

A comparative analysis of unsupervised machine‐learning methods in PSG‐related phenotyping

A comparative analysis of unsupervised machine‐learning methods in PSG‐related phenotyping

Performance Comparison of Standard Polysomnographic Parameters Used in the Diagnosis of Sleep Apnea

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