Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Clinical Neurophysiology, OLVG, Amsterdam, the Netherlands.
Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Sleep Med. 2021 Dec;88:116-133. doi: 10.1016/j.sleep.2021.10.015. Epub 2021 Oct 21.
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.
在上气道中识别阻塞部位有助于选择治疗睡眠呼吸障碍患者的方法。由于现有技术的局限性,人们一直在寻找更可行的方法。打鼾声音参数被假设为阻塞部位的潜在预测指标。因此,本综述旨在:i)研究打鼾声音参数与阻塞部位之间的关联;ii)分析报告的阻塞部位预测模型的方法学。
文献检索在 PubMed、Embase.com、CENTRAL、Web of Science 和 Scopus 中进行,并与医学图书馆员合作。如果研究调查了打鼾声音参数与阻塞部位之间的关系,以及/或报告了基于打鼾声音的阻塞部位预测模型,则符合纳入标准。
在检索到的 1016 篇参考文献中,有 28 篇符合纳入标准。结果发现,下气道阻塞产生的特征频率成分高于上气道阻塞。预测模型主要基于频域中的打鼾声音参数构建。报告的准确性范围从 60.4%到 92.2%。
现有证据表明,频域中的打鼾声音参数与上气道中的阻塞部位之间存在关联。基于打鼾声音参数和参与者特征构建阻塞部位预测模型是有希望的,但到目前为止,打鼾声音分析似乎不是一种可行的治疗选择的诊断方式。