  The rapidly increasing prevalence of debilitating breathing disorders, such
as chronic obstructive pulmonary disease (COPD), calls for a meaningful
integration of artificial intelligence (AI) into healthcare. While this
promises improved detection and monitoring of breathing disorders, AI
techniques are almost invariably "data hungry" which highlights the importance
of generating physically meaningful surrogate data. Indeed, domain aware
surrogates would enable both an improved understanding of respiratory waveform
changes with different breathing disorders, and enhance the training of machine
learning algorithms. To this end, we introduce an apparatus comprising of PVC
tubes and 3D printed parts as a simple yet effective method of simulating both
obstructive and restrictive respiratory waveforms in healthy subjects.
Independent control over both inspiratory and expiratory resistances allows for
the simulation of obstructive breathing disorders through the whole spectrum of
FEV1/FVC spirometry ratios (used to classify COPD), ranging from healthy values
to values seen in severe chronic obstructive pulmonary disease. Moreover,
waveform characteristics of breathing disorders, such as a change in
inspiratory duty cycle or peak flow are also observed in the waveforms
resulting from use of the artificial breathing disorder simulation apparatus.
Overall, the proposed apparatus provides us with a simple, effective and
physically meaningful way to generate faithful surrogate breathing disorder
waveforms, a prerequisite for the use of artificial intelligence in respiratory
health.
