Airflow recovery from thoracic and abdominal movements using synchrosqueezing transform and locally stationary Gaussian process regression

A wealth of information about respiratory system is encoded in the airflow signal. While direct measurement of airflow via spirometer with an occlusive seal is the gold standard, this may not be practical for ambulatory monitoring of patients. Advances in sensor technology have made measurement of motion of the thorax and abdomen feasible with small inexpensive devices, but estimating airflow from these time series is challenging due to the presence of complicated nonstationary oscillatory signals. To properly extract the relevant oscillatory features from thoracic and abdominal movement, a nonlinear-type time-frequency analysis tool, the synchrosqueezing transform, is employed; these features are then used to estimate the airflow by a locally stationary Gaussian process regression. It is shown that, using a dataset that contains respiratory signals under normal sleep conditions, accurate airflow out-of-sample predictions, and hence the precise estimation of an important physiological quantity, inspiration respiration ratio, can be achieved by fitting the proposed model both in the intra- and inter-subject setups. The method is also applied to a more challenging case, where subjects under general anesthesia underwent transitions from pressure support to unassisted ventilation to further demonstrate the utility of the proposed method.

Duke Scholars

Author Hau-Tieng Wu Mathematics