Respiratory airflow estimation from lung sounds based on regression

The aim of this work is the estimation of respiratory flow from lung sound recordings, i.e. acoustic airflow estimation. With a 16-channel lung sound recording device, we simultaneously record the respiratory flow and the lung sounds on the posterior chest from six lung-healthy subjects in supine position. For the recordings of four selected sensor positions, we extract linear frequency cepstral coefficient (LFCC) features and map these on the airflow signal. We use multivariate polynomial regression to fit the features to the airflow signal. Compared to most of the previous approaches, the proposed method uses lung sounds instead of trachea sounds. Furthermore, our method masters the estimation of the airflow without prior knowledge of the respiratory phase, i.e. no additional algorithm for phase detection is required. Another benefit is the avoidance of time-consuming calibration. In experiments, we evaluate the proposed method for various selections of sensor positions in terms of mean squared error (MSE) between estimated and actual airflow. Moreover, we show the accuracy of the method regarding a frame-based breathing-phase detection.