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The rapidly increasing prevalence of debilitating breathing disorders, suchas chronic obstructive pulmonary disease (COPD), calls for a meaningfulintegration of artificial intelligence (AI) into healthcare. While thispromises improved detection and monitoring of breathing disorders, AItechniques are almost invariably "data hungry" which highlights the importanceof generating physically meaningful surrogate data. Indeed, domain awaresurrogates would enable both an improved understanding of respiratory waveformchanges with different breathing disorders, and enhance the training of machinelearning algorithms. To this end, we introduce an apparatus comprising of PVCtubes and 3D printed parts as a simple yet effective method of simulating bothobstructive and restrictive respiratory waveforms in healthy subjects.Independent control over both inspiratory and expiratory resistances allows forthe simulation of obstructive breathing disorders through the whole spectrum ofFEV1/FVC spirometry ratios (used to classify COPD), ranging from healthy valuesto values seen in severe chronic obstructive pulmonary disease. Moreover,waveform characteristics of breathing disorders, such as a change ininspiratory duty cycle or peak flow are also observed in the waveformsresulting from use of the artificial breathing disorder simulation apparatus.Overall, the proposed apparatus provides us with a simple, effective andphysically meaningful way to generate faithful surrogate breathing disorderwaveforms, a prerequisite for the use of artificial intelligence in respiratoryhealth.

An Apparatus for the Simulation of Breathing Disorders: Physically Meaningful Generation of Surrogate Data