ArticleHNO2019
[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].
Article in HNO, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 6 citations in OpenAlex.
- An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector.Biomedical signal processing and control · 2021Article
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Authors and funding
8 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcoustic snoring sound analysis is a noninvasive method for diagnosis of the mechanical mechanisms causing snoring that can be performed during natural sleep. The objective of this work is development and evaluation of classification schemes for snoring sounds that can provide meaningful diagnostic support. MATERIALS AND
methodsBased on two annotated snoring noise databases with different classifications (s-VOTE with four classes versus ACLTE with five classes), identically structured machine classification systems were trained. The feature extractor openSMILE was used in combination with a linear support vector machine for classification.
resultsWith an unweighted average recall (UAR) of 55.4% for the s‑VOTE model and 49.1% for the ACLTE, the results are at a similar level. In both models, the best differentiation is achieved for epiglottic snoring, while velar and oropharyngeal snoring are more often confused.
conclusionAutomated acoustic methods can help diagnose sleep-disordered breathing. A reason for the restricted recognition performance is the limited size of the training datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.