Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review

Kun Qian, Christoph Janott, Maximilian Schmitt, Zixing Zhang, Clemens Heiser, Werner Hemmert, Yoshiharu Yamamoto, Bjorn W. Schuller

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations

Abstract

In the past three decades, snoring (affecting more than 30 % adults of the UK population) has been increasingly studied in the transdisciplinary research community involving medicine and engineering. Early work demonstrated that, the snore sound can carry important information about the status of the upper airway, which facilitates the development of non-invasive acoustic based approaches for diagnosing and screening of obstructive sleep apnoea and other sleep disorders. Nonetheless, there are more demands from clinical practice on finding methods to localise the snore sound's excitation rather than only detecting sleep disorders. In order to further the relevant studies and attract more attention, we provide a comprehensive review on the state-of-The-Art techniques from machine learning to automatically classify snore sounds. First, we introduce the background and definition of the problem. Second, we illustrate the current work in detail and explain potential applications. Finally, we discuss the limitations and challenges in the snore sound classification task. Overall, our review provides a comprehensive guidance for researchers to contribute to this area.

Original languageEnglish
Article number9152123
Pages (from-to)1233-1246
Number of pages14
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number4
DOIs
StatePublished - Apr 2021

Keywords

  • Deep learning
  • machine learning
  • obstructive sleep apnoea
  • physiological signals
  • snore sound

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