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Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
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Breathing rate estimation during sleep using audio signal analysis.

E Dafna, T Rosenwein, A Tarasiuk

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    Summary

    This study introduces a novel, non-contact method using microphones to accurately measure breathing rate (BR) during sleep. This comfortable approach offers a promising alternative to traditional respiratory monitoring sensors.

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    Area of Science:

    • Respiratory Physiology
    • Biomedical Engineering
    • Sleep Medicine

    Background:

    • Sleep significantly alters respiratory rate and ventilation patterns.
    • Current methods for monitoring breathing rate (BR) during sleep, such as respiratory belts, can be uncomfortable and intrusive.
    • There is a need for simplified, non-contact methods for sleep respiration monitoring.

    Purpose of the Study:

    • To develop and validate a novel, non-contact method for estimating breathing rate (BR) during sleep using ambient microphone recordings.
    • To assess the feasibility and accuracy of an audio-based BR estimation algorithm compared to traditional polysomnography (PSG) methods.

    Main Methods:

    • A signal processing algorithm was developed to enhance breathing sounds and extract relevant features from ambient microphone data.
    • Breathing rate was estimated using the developed audio-based algorithm.
    • The audio-based BR estimates were compared against gold-standard respiratory belts during in-laboratory polysomnography (PSG) on 204 subjects.

    Main Results:

    • A high Pearson's correlation (R=0.97) was observed for averaged BR between the audio-based method and respiratory belts.
    • Epoch-by-epoch (30-second intervals) BR comparison showed a mean relative error of 2.44% and a Pearson's correlation of 0.68.
    • The non-contact audio-based method demonstrated reliable and promising results for sleep BR estimation.

    Conclusions:

    • Non-contact audio-based breathing rate estimation is a feasible and accurate method for sleep monitoring.
    • This technology offers a more comfortable and simplified alternative to existing respiratory monitoring systems.
    • Further research can explore the clinical applications of this non-contact sleep respiration monitoring technique.