  Continuous monitoring of respiratory activity is desirable in many clinical
applications to detect respiratory events. Non-contact monitoring of
respiration can be achieved with near- and far-infrared spectrum cameras.
However, current technologies are not sufficiently robust to be used in
clinical applications. For example, they fail to estimate an accurate
respiratory rate (RR) during apnea. We present a novel algorithm based on
multispectral data fusion that aims at estimating RR also during apnea. The
algorithm independently addresses the RR estimation and apnea detection tasks.
Respiratory information is extracted from multiple sources and fed into an RR
estimator and an apnea detector whose results are fused into a final
respiratory activity estimation. We evaluated the system retrospectively using
data from 30 healthy adults who performed diverse controlled breathing tasks
while lying supine in a dark room and reproduced central and obstructive apneic
events. Combining multiple respiratory information from multispectral cameras
improved the root mean square error (RMSE) accuracy of the RR estimation from
up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for
classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also
improved. Furthermore, the independent consideration of apnea detection led to
a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may
represent a step towards the use of cameras for vital sign monitoring in
medical applications.
