Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones

Abstract

Correct labeling of breath phases is useful in the automatic analysis of respiratory sounds, where airflow or volume signals are commonly used as temporal reference. However, such signals are not always available. The development of a smartphone-based respiratory sound analysis system has received increased attention. In this study, we propose an optical approach that takes advantage of a smartphone's camera and provides a chest movement signal useful for classification of the breath phases when simultaneously recording tracheal sounds. Spirometer and smartphone-based signals were acquired from N = 13 healthy volunteers breathing at different frequencies, airflow and volume levels. We found that the smartphone-acquired chest movement signal was highly correlated with reference volume (rho = 0.960 +/- 0.025, mean +/- SD). A simple linear regression on the chest signal was used to label the breath phases according to the slope between consecutive onsets. 100% accuracy was found for the classification of the analyzed breath phases. We found that the proposed classification scheme can be used to correctly classify breath phases in more challenging breathing patterns, such as those that include non-breath events like swallowing, talking, and coughing, and alternating or irregular breathing. These results show the feasibility of developing a portable and inexpensive phonopneumogram for the analysis of respiratory sounds based on smartphones.