Sleep apnea is the most common sleep disturbance and it is an important risk
factor for cardiovascular disorders. Its detection relies on a polysomnography,
a combination of diverse exams.
  In order to detect changes due to sleep disturbances such as sleep apnea
occurrences, without the need of combined recordings, we mainly analyze
systolic blood pressure signals (maximal blood pressure value of each beat to
beat interval). Nonstationarities in the data are uncovered by a segmentation
procedure, which provides local quantities that are correlated to
apnea-hypopnea events. Those quantities are the average length and average
variance of stationary patches. By comparing them to an apnea score previously
obtained by polysomnographic exams, we propose an apnea quantifier based on
blood pressure signal.
  This furnishes an alternative procedure for the detection of apnea based on a
single time series, with an accuracy of 82%

Anteneodo, Celia

Camargo, Sabrina

Kurths, Juergen

Penzel, Thomas

Riedl, Maik

Wessel, Niels

PLoS ONE

English

arXiv

Article Accepted Date: August 11, 2014 Copyright: © 2014 Camargo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Funding provided by CNPq (Brazilian agency) http://www.cnpq.br/ and Deutsche Forschungsgemeinschaft, grant numbers DFG RI2016/2-1, WE 2834/5-1, KU837/29-2, and KU837/35-1. http://www.dfg.de/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe

Aberdeen University Research

Sleep Apnea-Hypopnea Quantification by Cardiovascular Data Analysis

Sabrina Camargo

Maik Riedl

Celia Anteneodo

Jürgen Kurths

Thomas Penzel

Niels Wessel

Crossref

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 [sigma(2)], 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.CNPq (Brazilian agency); Deutsche Forschungsgemeinschaft [DFG RI2016/2-1, WE 2834/5-1, KU837/29-2, KU837/35-1

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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 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 .
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 , as well as the average variance , 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 is also correlated with AHI. All these quantities allow to separate
apneic individuals, with an accuracy of at least . Therefore, they provide
alternative criteria to detect sleep apnea based on a single time series, the
systolic blood pressure

Kurths, Jürgen

Institutional Repository of the Freie Universität Berlin

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

Camargo, S.

Riedl, M.

Anteneodo, C.

Kurths, J.

Penzel, T.

Wessel, N.

Repositorium für Naturwissenschaften und Technik  (TIB Hannover)

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 , as well as the average variance , 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

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Sleep apnea-hypopnea quantification by cardiovascular data analysis.

https://aura.abdn.ac.uk/bitstream/2164/3744/5/Sleep_Apnea_Hypopnea_Quantification_by_Cardiovascular_Data_Analysis.pdf

Sleep apnea-hypopnea quantification by cardiovascular data analysis

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Aberdeen University Research

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FGV Repositório Institucional Acadêmico (Fundação Getulio Vargas)

Institutional Repository of the Freie Universität Berlin

Repositorium für Naturwissenschaften und Technik (TIB Hannover)

Directory of Open Access Journals