• Bersain A Reyes, Natasa Reljin, and Ki H Chon.
• Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA. bareyes@wpi.edu.
• Sensors (Basel). 2014 Jul 30; 14 (8): 13830-50.

AbstractTracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds' amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r² = 0.9693, 0.11 (-1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r² = 0.9672, 0.097 (-1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found.

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