Literature DB >> 36217088

Airflow Analysis in the Context of Sleep Apnea.

Verónica Barroso-García1,2, Jorge Jiménez-García3, Gonzalo C Gutiérrez-Tobal3,4, Roberto Hornero3,4,5.   

Abstract

The airflow (AF) is a physiological signal involved in the overnight polysomnography (PSG) that reflects the respiratory activity. This signal is able to show the particularities of sleep apnea and is therefore used to define apneic events. In this regard, a growing number of studies have shown the usefulness of employing the overnight airflow as the only or combined information source for diagnosing sleep apnea in both children and adults. Due to its easy acquisition and interpretation, this biosignal has been widely analyzed by means of different signal processing techniques. In this chapter, we review the main methodological approaches applied to characterize and extract relevant information from this signal. In view of the results, we can conclude that the overnight airflow successfully reflects the particularities caused by the occurrence of apneic and hypopneic events and provides useful information for obtaining relevant biomarkers that characterize this disease.
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.

Entities:  

Keywords:  Airflow; Automatic analysis; Sleep apnea; Sleep disorders

Mesh:

Year:  2022        PMID: 36217088     DOI: 10.1007/978-3-031-06413-5_14

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   3.650


  31 in total

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Authors:  J A Bennett; W J Kinnear
Journal:  Thorax       Date:  1999-11       Impact factor: 9.139

2.  Intelligent diagnosis of sleep apnea syndrome.

Authors:  Mariano Cabrero-Canosa; Elena Hernandez-Pereira; Vicente Moret-Bonillo
Journal:  IEEE Eng Med Biol Mag       Date:  2004 Mar-Apr

3.  Screening of obstructive sleep apnea using Hilbert-Huang decomposition of oronasal airway pressure recordings.

Authors:  P Caseiro; R Fonseca-Pinto; A Andrade
Journal:  Med Eng Phys       Date:  2010-05-05       Impact factor: 2.242

4.  Sleep apnea classification based on respiration signals by using ensemble methods.

Authors:  Cafer Avcı; Ahmet Akbaş
Journal:  Biomed Mater Eng       Date:  2015       Impact factor: 1.300

5.  Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis.

Authors:  Verónica Barroso-García; Gonzalo C Gutiérrez-Tobal; Leila Kheirandish-Gozal; Fernando Vaquerizo-Villar; Daniel Álvarez; Félix Del Campo; David Gozal; Roberto Hornero
Journal:  Comput Biol Med       Date:  2020-12-07       Impact factor: 4.589

6.  Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis.

Authors:  Verónica Barroso-García; Gonzalo C Gutiérrez-Tobal; Leila Kheirandish-Gozal; Daniel Álvarez; Fernando Vaquerizo-Villar; Pablo Núñez; Félix Del Campo; David Gozal; Roberto Hornero
Journal:  Comput Methods Programs Biomed       Date:  2019-09-18       Impact factor: 5.428

7.  Reliability of home respiratory polygraphy for the diagnosis of sleep apnea in children.

Authors:  María Luz Alonso-Álvarez; Joaquin Terán-Santos; Estrella Ordax Carbajo; José Aurelio Cordero-Guevara; Ana Isabel Navazo-Egüia; Leila Kheirandish-Gozal; David Gozal
Journal:  Chest       Date:  2015-04       Impact factor: 9.410

8.  Accelerometry-derived respiratory index estimating apnea-hypopnea index for sleep apnea screening.

Authors:  Aurélien Bricout; Julie Fontecave-Jallon; Jean-Louis Pépin; Pierre-Yves Guméry
Journal:  Comput Methods Programs Biomed       Date:  2021-05-31       Impact factor: 5.428

9.  Automated detection of sleep apnea and hypopnea events based on robust airflow envelope tracking in the presence of breathing artifacts.

Authors:  Marcin Ciołek; Maciej Niedźwiecki; Stefan Sieklicki; Jacek Drozdowski; Janusz Siebert
Journal:  IEEE J Biomed Health Inform       Date:  2014-05-23       Impact factor: 5.772

10.  Envelope analysis of the airflow signal to improve polysomnographic assessment of sleep disordered breathing.

Authors:  Javier A Díaz; José M Arancibia; Alejandro Bassi; Ennio A Vivaldi
Journal:  Sleep       Date:  2014-01-01       Impact factor: 5.849

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