  Airflow signal encodes rich information about respiratory system. While the
gold standard for measuring airflow is to use a spirometer with an occlusive
seal, this is not 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 estimation of airflow from these
time series is challenging. We propose to use the nonlinear-type time-frequency
analysis tool, synchrosqueezing transform, to properly represent the thoracic
and abdominal movement signals as the features, which are used to recover the
airflow by the locally stationary Gaussian process. We show that, using a
dataset that contains respiratory signals under normal sleep conditions, an
accurate prediction can be achieved by fitting the proposed model in the
feature space both in the intra- and inter-subject setups. We also apply our
method 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.
