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 (ρ = 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.

Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement

“…In addition, owing to the mature image processing techniques, visible imaging sensors have attracted much attention for breathing evaluations. 16,17 Shao et al determined the breathing patterns using the cameras in the visible region to track the small shoulder movements associating with breathing. 18 Although the random body movements can be corrected by the motion-tracking algorithm, breathing rate estimation based on visible imaging is by nature sensitive to the slight movements, thus not being appropriate for the long-term monitoring.…”

Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features

“…These CNN models with DS convolution layers (DS-CNN) then empower those edge devices with no GPUs and limited computational power to achieve higher efficiency for CNN model inference. The development of automatic lung auscultation systems on low-cost hardware devices has drawn a lot of attention [19,27,28]. How to better exploit the value of DS-CNN for developing remote automatic lung auscultation systems still remains to be explored.…”

Convolutional Neural Network for Breathing Phase Detection in Lung Sounds

“…More recently, Reyes et al (2016) proposed the automatic classification of breathing phases from a smartphone optical recording of the chest movements acquired simultaneously with tracheal sounds. This was tested in 13 healthy adults and a 100% accuracy was found [6]. However, these methods were heavily dependent on tracheal sounds and it is well known that the characteristics of tracheal and chest sounds are distinct.…”