Prediction of the obstruction sites in the upper airway in sleep-disordered breathing based on snoring sound parameters: a systematic review
Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential p...
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Published in | Sleep medicine Vol. 88; pp. 116 - 133 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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Netherlands
Elsevier B.V
01.12.2021
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Abstract | Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of reported prediction models of the obstruction sites.
The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associations between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound.
Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%.
Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection.
•The snoring sound parameters in the frequency domain are associated with the obstruction sites in the upper airway.•Associations between the obstruction sites and snoring sound parameters in the temporal and intensity domains are unclear.•It is promising to build a prediction model of the obstruction sites based on snoring sound parameters.•Snoring sound analysis so far does not seem to be a viable diagnostic modality for treatment selection. |
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AbstractList | BACKGROUNDIdentification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of reported prediction models of the obstruction sites. METHODSThe literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associations between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound. RESULTSOf the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%. CONCLUSIONSAvailable evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection. Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of reported prediction models of the obstruction sites. The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associations between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound. Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%. Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection. •The snoring sound parameters in the frequency domain are associated with the obstruction sites in the upper airway.•Associations between the obstruction sites and snoring sound parameters in the temporal and intensity domains are unclear.•It is promising to build a prediction model of the obstruction sites based on snoring sound parameters.•Snoring sound analysis so far does not seem to be a viable diagnostic modality for treatment selection. Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of reported prediction models of the obstruction sites. The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associations between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound. Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%. Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection. |
Author | Huang, Zhengfei den Haan, Chantal de Vries, Nico Aarab, Ghizlane Ravesloot, Madeline J.L. Zhou, Ning Lobbezoo, Frank Hilgevoord, Antonius A.J. Bosschieter, Pien F.N. van Selms, Maurits K.A. |
Author_xml | – sequence: 1 givenname: Zhengfei orcidid: 0000-0002-5656-2446 surname: Huang fullname: Huang, Zhengfei email: zf.huang@acta.nl organization: Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands – sequence: 2 givenname: Ghizlane surname: Aarab fullname: Aarab, Ghizlane organization: Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands – sequence: 3 givenname: Madeline J.L. orcidid: 0000-0001-5645-5811 surname: Ravesloot fullname: Ravesloot, Madeline J.L. organization: Department of Otorhinolaryngology - Head and Neck Surgery, OLVG, Amsterdam, the Netherlands – sequence: 4 givenname: Ning surname: Zhou fullname: Zhou, Ning organization: Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands – sequence: 5 givenname: Pien F.N. orcidid: 0000-0001-7964-3225 surname: Bosschieter fullname: Bosschieter, Pien F.N. organization: Department of Otorhinolaryngology - Head and Neck Surgery, OLVG, Amsterdam, the Netherlands – sequence: 6 givenname: Maurits K.A. orcidid: 0000-0002-0792-4930 surname: van Selms fullname: van Selms, Maurits K.A. organization: Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands – sequence: 7 givenname: Chantal orcidid: 0000-0002-2640-3946 surname: den Haan fullname: den Haan, Chantal organization: Medical Library, Department of Research and Education, OLVG, Amsterdam, the Netherlands – sequence: 8 givenname: Nico surname: de Vries fullname: de Vries, Nico organization: Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands – sequence: 9 givenname: Frank orcidid: 0000-0001-9877-7640 surname: Lobbezoo fullname: Lobbezoo, Frank organization: Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands – sequence: 10 givenname: Antonius A.J. surname: Hilgevoord fullname: Hilgevoord, Antonius A.J. organization: Department of Clinical Neurophysiology, OLVG, Amsterdam, the Netherlands |
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Keywords | Frequency Snoring sound Prediction model Machine learning Upper airway |
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Title | Prediction of the obstruction sites in the upper airway in sleep-disordered breathing based on snoring sound parameters: a systematic review |
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