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A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

Author

Listed:
  • Shota Hayashi

    (Department of Respiratory Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan)

  • Meiyo Tamaoka

    (Department of Respiratory Physiology and Sleep Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan)

  • Tomoya Tateishi

    (Department of Respiratory Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan)

  • Yuki Murota

    (Yamaha Corporation Electronic Devices Division, Shizuoka 438-0192, Japan)

  • Ibuki Handa

    (Yamaha Corporation Electronic Devices Division, Shizuoka 438-0192, Japan)

  • Yasunari Miyazaki

    (Department of Respiratory Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan)

Abstract

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

Suggested Citation

  • Shota Hayashi & Meiyo Tamaoka & Tomoya Tateishi & Yuki Murota & Ibuki Handa & Yasunari Miyazaki, 2020. "A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis," IJERPH, MDPI, vol. 17(8), pages 1-10, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2951-:d:350152
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    References listed on IDEAS

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    1. Hiroyuki Ishiyama & Daichi Hasebe & Kazumichi Sato & Yuki Sakamoto & Akifumi Furuhashi & Eri Komori & Hidemichi Yuasa, 2019. "The Efficacy of Device Designs (Mono-block or Bi-block) in Oral Appliance Therapy for Obstructive Sleep Apnea Patients: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 16(17), pages 1-15, August.
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