Literature DB >> 17301093

Analysis of the interbeat interval increment to detect obstructive sleep apnoea/hypopnoea.

F Roche1, S Celle, V Pichot, J-C Barthélémy, E Sforza.   

Abstract

The prevalence of obstructive sleep apnoea/hypopnoea syndrome (OSAHS) is underestimated and its diagnosis is costly and restricted to specialised sleep laboratories. The frequency component of interbeat interval increment (III) has been proposed as a simple and inexpensive diagnostic tool in OSAHS. In a set of 150 patients with clinically suspected sleep-related breathing disorder, the actual predictive accuracy of the power spectral density of the III of the very low frequencies (%VLFI) was analysed by comparing with the apnoea/hypopnoea index (AHI), as assessed by synchronised polysomnography. OSAHS was defined in 100 patients according to an AHI>or=15 events.h(-1). Receiver operator characteristic curves built for %VLFI confirmed that this variable was able to separate OSAHS positive from OSAHS negative with statistical significance. Using an appropriate threshold (>4%), %VLFI demonstrated a positive predictive value of 80%. Misclassification of false-positive subjects occurred when the patient presented significant sleep discontinuity and sleep fragmentation (sleep fragmentation index>or=50 events.h(-1)) related to insomnia or periodic limb movements. A power spectral density of the interbeat interval increment of very low frequencies>4% allowed correct classification of obstructive sleep apnoea/hypopnoea syndrome when the clinical history suggested sleep-related breathing disorders and when moderate-to-severe cases are considered. Higher power spectral density of the interbeat interval increment of very low frequencies may also indicate disrupted sleep in the absence of clear clinical symptoms of sleep apnoea/hypopnoea syndrome.

Entities:  

Mesh:

Year:  2007        PMID: 17301093     DOI: 10.1183/09031936.00042606

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   16.671


  9 in total

1.  Sleep-related autonomic overactivity in a general elderly population and its relationship to cardiovascular regulation.

Authors:  Emilie Crawford-Achour; Frédéric Roche; Vincent Pichot; Sébastien Celle; Jean-Claude Barthélémy; Florian Chouchou
Journal:  Heart Vessels       Date:  2014-08-24       Impact factor: 2.037

2.  Pulse rate trends in obstructive sleep apnea: a reliable tool to predict long term response to CPAP?

Authors:  Javier Navarro-Esteva; Antonio Ravelo-García; Ibrahim Véliz-Flores; Guillermo Pérez-Mendoza; Antonio M Esquinas
Journal:  J Thorac Dis       Date:  2014-09       Impact factor: 2.895

3.  Levels of thioredoxin are related to the severity of obstructive sleep apnea: based on oxidative stress concept.

Authors:  Qian Guo; Yan Wang; Qing Yun Li; Min Li; Huan Ying Wan
Journal:  Sleep Breath       Date:  2012-03-22       Impact factor: 2.816

4.  Accuracy of ECG-based screening for sleep-disordered breathing: a survey of all male workers in a transport company.

Authors:  Junichiro Hayano; Teruomi Tsukahara; Eiichi Watanabe; Fumihiko Sasaki; Kiyohiro Kawai; Hiroki Sakakibara; Itsuo Kodama; Tetsuo Nomiyama; Keisaku Fujimoto
Journal:  Sleep Breath       Date:  2012-03-20       Impact factor: 2.816

5.  Interactive associations of depression and sleep apnea with adverse clinical outcomes after acute myocardial infarction.

Authors:  Junichiro Hayano; Robert M Carney; Eiichi Watanabe; Kiyohiro Kawai; Itsuo Kodama; Phyllis K Stein; Lana L Watkins; Kenneth E Freedland; James A Blumenthal
Journal:  Psychosom Med       Date:  2012-09-28       Impact factor: 4.312

6.  [Screening for sleep apnea in cardiovascular patients in clinical routine].

Authors:  W S Mäuser; S Sandrock; L Kotzott; H Bonnemeier
Journal:  Herzschrittmacherther Elektrophysiol       Date:  2012-03

7.  Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer.

Authors:  Ying-Shuo Hsu; Tien-Yu Chen; Dean Wu; Chia-Mo Lin; Jer-Nan Juang; Wen-Te Liu
Journal:  J Clin Sleep Med       Date:  2020-07-15       Impact factor: 4.062

8.  Ambulatory screening tool for sleep apnea: analyzing a single-lead electrocardiogram signal (ECG).

Authors:  Solveig Magnusdottir; Hugi Hilmisson
Journal:  Sleep Breath       Date:  2017-09-07       Impact factor: 2.816

9.  Changes in the heart rate variability in patients with obstructive sleep apnea and its response to acute CPAP treatment.

Authors:  Ernesto Kufoy; Jose-Alberto Palma; Jon Lopez; Manuel Alegre; Elena Urrestarazu; Julio Artieda; Jorge Iriarte
Journal:  PLoS One       Date:  2012-03-16       Impact factor: 3.240

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.