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Automatic Respiratory Phase Identification Using Tracheal Sounds and Movements During Sleep.

Nasim Montazeri Ghahjaverestan1,2, Muammar Kabir1,2, Shumit Saha1,2

  • 1Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.

Annals of Biomedical Engineering
|January 6, 2021
PubMed
Summary

This study developed an algorithm to automatically identify breathing phases from tracheal sounds and movements. This method accurately detects respiratory phases, aiding in sleep apnea assessment.

Keywords:
Airflow estimationRespiratory phasesSleep apneaTracheal movementsTracheal sounds

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Area of Science:

  • Biomedical Engineering
  • Respiratory Physiology
  • Sleep Medicine

Background:

  • Airflow assessment is crucial for respiratory function, particularly in sleep apnea patients.
  • Analyzing tracheal sounds and movements offers a convenient method for airflow estimation.
  • Accurate identification of respiratory phases is essential for this analysis.

Purpose of the Study:

  • To develop an automatic algorithm for analyzing tracheal sounds and movements.
  • To identify respiratory phases (inspiration/expiration) during sleep.
  • To improve airflow estimation for sleep apnea diagnosis.

Main Methods:

  • Collected tracheal sound and movement data using a wearable device during polysomnography.
  • Developed an adaptive algorithm to detect respiratory phases in tracheal sounds.
  • Extracted morphological features to classify phases into inspirations and expirations.

Main Results:

  • The algorithm achieved average phase detection errors of 7.62% (normal breathing), 8.95% (snoring), and 13.19% (respiratory events).
  • Average time delays for phase detection were 181 ms (normal), 194 ms (snoring), and 220 ms (respiratory events).
  • Classification accuracy reached 83.7% for inspirations and 75.0% for expirations.

Conclusions:

  • Tracheal sounds and movements can be accurately analyzed to identify respiratory phases during sleep.
  • The developed algorithm shows promise for non-invasive airflow estimation in sleep studies.
  • This technique could enhance the assessment of respiratory function in patients with sleep disorders.