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A Novel Sleep Respiratory Rate Detection Method for Obstructive Sleep Apnea Based on Characteristic Moment Waveform.

Yu Fang1, Zhongwei Jiang1, Haibin Wang2

  • 1Graduate School of Science and Engineering, Yamaguchi University, Yamaguchi, Japan.

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This study introduces a fast method to detect respiratory rate (RR) from breathing sounds for obstructive sleep apnea (OSA) monitoring. The novel Characteristic Moment Waveform (CMW) approach accurately identifies RR and sleep apnea events.

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Signal Processing

Background:

  • Obstructive sleep apnea (OSA) is a significant sleep disorder linked to health risks, including nocturnal death.
  • Monitoring respiratory rate (RR) during sleep is crucial for diagnosing and managing OSA.
  • Existing methods may lack the computational speed required for continuous, whole-night monitoring.

Purpose of the Study:

  • To propose a novel, high-speed method for detecting sleep respiratory rate (RR) using breathing sound signals.
  • To validate the accuracy and efficiency of the Characteristic Moment Waveform (CMW) method for OSA monitoring.
  • To enable detailed evaluation of OSA by detecting apnea events and breath segmentation.

Main Methods:

  • Acquisition of breathing sound signals using a portable, wearable device.
  • Application of amplitude contrast decreasing and Characteristic Moment Waveform (CMW) extraction.
  • Calculation of sleep RR based on the extreme points of the CMW.
  • Validation through experiments involving one OSA case and five healthy cases.

Main Results:

  • The proposed CMW method accurately detects sleep RR compared to manual counting.
  • The method demonstrates high computational speed for efficient whole-night monitoring.
  • Apnea sections can be identified using the detected sleep RR values and a defined threshold.
  • The duration of breath segmentation is calculable for in-depth OSA assessment.

Conclusions:

  • The novel CMW-based method provides an accurate and computationally efficient approach for sleep RR detection.
  • This technique is valuable for continuous OSA monitoring and detailed sleep breathing analysis.
  • The findings support further research into utilizing sleep breathing sound signals for diagnosing and managing sleep disorders.