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Tracheal sounds acquisition using smartphones.

Bersain A Reyes1, Natasa Reljin2, Ki H Chon3

  • 1Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA. bareyes@wpi.edu.

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Summary
This summary is machine-generated.

Smartphone microphones can accurately measure airflow and respiratory rates by analyzing tracheal sounds. This non-invasive method shows high correlation with spirometry, enabling automated breath detection and reliable respiratory rate estimation.

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Area of Science:

  • Biomedical Engineering
  • Respiratory Physiology
  • Acoustic Signal Processing

Background:

  • Non-invasive estimation of ventilation parameters is crucial for respiratory monitoring.
  • Tracheal sounds offer a promising avenue for assessing airflow and breath characteristics.
  • Existing methods often require specialized equipment, limiting accessibility.

Purpose of the Study:

  • To assess the feasibility of using smartphone microphones to extract airflow data from tracheal sounds.
  • To automate the detection of breath-phase onset using acoustic signals.
  • To determine the accuracy of smartphone-based respiratory rate estimation compared to spirometry.

Main Methods:

  • Acquired tracheal sounds from 9 healthy volunteers using Samsung Galaxy S4 and iPhone 4s.
  • Measured airflow (0.5–2.5 L/s) and respiratory rates using a spirometer as a reference.
  • Analyzed the amplitude-airflow relationship and breath-phase onset timing.
  • Validated smartphone-derived respiratory rates against spirometry data.

Main Results:

  • Smartphone-acquired sound amplitude strongly correlated with airflow, following a power law relationship.
  • Breath-phase onset detection showed minimal differences (52 ± 51 ms for Galaxy S4, 51 ± 48 ms for iPhone 4s) compared to spirometry.
  • Respiratory rate estimation demonstrated high accuracy (r² = 0.9693 for Galaxy S4, r² = 0.9672 for iPhone 4s) with low bias.

Conclusions:

  • Smartphones can accurately estimate airflow and respiratory rates non-invasively through tracheal sound analysis.
  • Automated breath-phase onset detection is feasible with high precision using smartphone acoustics.
  • Both tested smartphone models demonstrated comparable performance, highlighting their potential as accessible respiratory monitoring tools.