Implementation of Machine Learning for Breath Collection

Economic and technologic progresses states the analysis of human’s exhaled air as a promising tool for medical diagnosis and therapy monitoring. Challenges of most pulmonary breath acquisition devices are related to the substances’ concentrations that are source (oral cavity, esophageal and alveolar) dependent and their low values (in ppbv - pptv range). We introduce a prototype that is capable of collecting samples of exhaled air according to the respiratory source and independent of the metabolic production of carbon dioxide. It also allows to access the breathing cycle in real-time, detects the optimized sampling instants and selects the collection pathway through the implementation of an algorithm containing a machine learning process. A graphical interface allows the interaction between the operator/user and the process of acquisition making it easy, quick and reliable. The imposition of breath rhythm led to improvements in accuracy of obtaining samples from specific parts of the respiratory tract and it should be adapted according to their age and physiological/health condition. The technology implemented in the proposed system should be taken into consideration for further studies, since the prototype is suitable for selectively sampling exhaled air from persons according to its age, genre and physiological condition.