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Nasal pressure recordings for automatic snoring detection

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An Erratum to this article was published on 27 October 2016

Abstract

This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4 % and a positive predictive value of 92.0 % were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.

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Acknowledgments

This work was supported by the Technology Innovation Program (10040408, Development of CPAP for sleep apnea) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).

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Authors and Affiliations

  1. Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Department of Surgery, College of Medicine, The University of Arizona, Tucson, AZ, 85724, USA

    Hyo-Ki Lee

  2. Division of Pulmonary and Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea

    Hojoong Kim

  3. Department of Biomedical Engineering, Yonsei University, 1 Yonseidae-gil, Wonju-si, Gangwon-do, 26493, Republic of Korea

    Kyoung-Joung Lee

Authors
  1. Hyo-Ki Lee
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  2. Hojoong Kim
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  3. Kyoung-Joung Lee
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Correspondence to Kyoung-Joung Lee.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s11517-016-1588-4.

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Lee, HK., Kim, H. & Lee, KJ. Nasal pressure recordings for automatic snoring detection. Med Biol Eng Comput 53, 1103–1111 (2015). https://doi.org/10.1007/s11517-015-1388-2

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  • DOI: https://doi.org/10.1007/s11517-015-1388-2

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