Towards an autonomous system with exhaled breath separation for cleaner condensed air samples

F. Carlos Pizarro, Cristobal J. Nettle, Oscar F. Araneda, Marcelo Tuesta

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

The acquisition of condensed samples of exhaled air is a well-known noninvasive method for analyzing the healthiness of the lungs. Unlike other invasive methods as induced sputum or bronchoscopy, the condensate collection is faster and non-aggressive. During breathing, it is interesting to fractionate the sample in two differentiated portions as the first exhaled portion comes from a section of the respiratory way known as Dead Space, while the lasting exhaled portion comes from a section known as Alveolar Space. The liquid collected from the Dead Space contains a low-to-none density of biomarkers, which are mainly contained in the Alveolar Space's breathed air. Novel procedures have shown that separating the samples results in a more precise analysis of the state of the patient, improving the collected data for enhancing the current description of associated diseases. Here we describe a novel device and the associated theoretical bases for detecting and separating the exhaled air based on the source area. The implemented system integrates a closed loop for pressure control which operates on a three-ways balloon valve, based on the instantaneous exhaled amount of carbon dioxide following a proven methodology that dynamically fits the expected measured range a patient, providing a reliable cut-off among air spaces. The device, available in an open repository, besides being far less expensive than commercial devices provides a simpler and shorter method for acquiring samples.

Original languageEnglish
Pages (from-to)24-30
Number of pages7
JournalCEUR Workshop Proceedings
Volume2564
Publication statusPublished - 1 Jan 2020
Event5th Congress on Robotics and Neuroscience, CRoNe 2019 - Valparaiso, Chile
Duration: 27 Feb 202029 Feb 2020

ASJC Scopus subject areas

  • Computer Science(all)

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