Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review
- 29 July 2020
- journal article
- review article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 25 (4), 1233-1246
- https://doi.org/10.1109/jbhi.2020.3012666
Abstract
In the past three decades, snoreing (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.Funding Information
- Zhejiang Lab's International Talent Fund for Young Professionals
- JSPS Postdoctoral Fellowship for Research in Japan (P19081)
- Japan Society for the Promotion of Science (JSPS), Japan
- Japan Society for the Promotion of Science (19F19081, 17H00878)
- Ministry of Education, Culture
- Sports, Science and Technology (115902)
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