Respiratory airflow estimation by time varying autoregressive modeling.

This study proposes an approach to estimate airflow from respiratory sounds using time varying autoregressive modeling. Exploiting the well-established relationship between respiratory sounds and airflow, the sound signals from posterior chest and trachea are modeled using TVAR with a Fourier basis set. It is hypothesized that TVAR coefficients will give us an estimate of the amount of respiratory airflow. The procedure was tested on a group of healthy subjects and good correlation coefficients between the estimated and actual airflow were obtained.