Sleep apnea-hypopnea quantification by cardiovascular data analysis.

Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep a...

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Autores principales:	Sabrina Camargo, Maik Riedl, Celia Anteneodo, Jürgen Kurths, Thomas Penzel, Niels Wessel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/148a3d03d85841eeb9c4bfb534ff607e
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spelling	oai:doaj.org-article:148a3d03d85841eeb9c4bfb534ff607e2021-11-25T06:00:35ZSleep apnea-hypopnea quantification by cardiovascular data analysis.1932-620310.1371/journal.pone.0107581https://doaj.org/article/148a3d03d85841eeb9c4bfb534ff607e2014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0107581https://doaj.org/toc/1932-6203Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration L. We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration <L>, as well as the average variance <σ2>, are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude S* is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least 79%. Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure.Sabrina CamargoMaik RiedlCelia AnteneodoJürgen KurthsThomas PenzelNiels WesselPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e107581 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sabrina Camargo Maik Riedl Celia Anteneodo Jürgen Kurths Thomas Penzel Niels Wessel Sleep apnea-hypopnea quantification by cardiovascular data analysis.
description	Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration L. We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration <L>, as well as the average variance <σ2>, are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude S* is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least 79%. Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure.
format	article
author	Sabrina Camargo Maik Riedl Celia Anteneodo Jürgen Kurths Thomas Penzel Niels Wessel
author_facet	Sabrina Camargo Maik Riedl Celia Anteneodo Jürgen Kurths Thomas Penzel Niels Wessel
author_sort	Sabrina Camargo
title	Sleep apnea-hypopnea quantification by cardiovascular data analysis.
title_short	Sleep apnea-hypopnea quantification by cardiovascular data analysis.
title_full	Sleep apnea-hypopnea quantification by cardiovascular data analysis.
title_fullStr	Sleep apnea-hypopnea quantification by cardiovascular data analysis.
title_full_unstemmed	Sleep apnea-hypopnea quantification by cardiovascular data analysis.
title_sort	sleep apnea-hypopnea quantification by cardiovascular data analysis.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/148a3d03d85841eeb9c4bfb534ff607e
work_keys_str_mv	AT sabrinacamargo sleepapneahypopneaquantificationbycardiovasculardataanalysis AT maikriedl sleepapneahypopneaquantificationbycardiovasculardataanalysis AT celiaanteneodo sleepapneahypopneaquantificationbycardiovasculardataanalysis AT jurgenkurths sleepapneahypopneaquantificationbycardiovasculardataanalysis AT thomaspenzel sleepapneahypopneaquantificationbycardiovasculardataanalysis AT nielswessel sleepapneahypopneaquantificationbycardiovasculardataanalysis
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Sleep apnea-hypopnea quantification by cardiovascular data analysis.

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