  The lack of large video databases obtained from real patients with
respiratory disorders makes the design and optimization of video-based
monitoring systems quite critical. The purpose of this study is the development
of suitable models and simulators of breathing behaviors and disorders, such as
respiratory pauses and apneas, in order to allow efficient design and test of
video-based monitoring systems. More precisely, a novel Continuous-Time Markov
Chain (CTMC) statistical model of breathing patterns is presented. The
Respiratory Rate (RR) pattern, estimated by measured vital signs of
hospital-monitored patients, is approximated as a CTMC, whose states and
parameters are selected through an appropriate statistical analysis. Then, two
simulators, software- and hardware-based, are proposed. After validation of the
CTMC model, the proposed simulators are tested with previously developed
video-based algorithms for the estimation of the RR and the detection of apnea
events. Examples of application to assess the performance of systems for
video-based RR estimation and apnea detection are presented. The results, in
terms of Kullback-Leibler divergence, show that realistic breathing patterns,
including specific respiratory disorders, can be accurately described by the
proposed model; moreover, the simulators are able to reproduce practical
breathing patterns for video analysis. The presented CTMC statistical model can
be strategic to describe realistic breathing patterns and devise simulators
useful to develop and test novel and effective video processing-based
monitoring systems.
