TY - GEN
T1 - Pilot Study for Validation and Differentiation of Alveolar and Esophageal Air
AU - Santos, Paulo
AU - Vassilenko, Valentina
AU - Conduto, Carolina
AU - Fernandes, Jorge M.
AU - Moura, Pedro C.
AU - Bonifácio, Paulo
N1 - Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - Breath analysis is an expanding scientific field with great potential for creating personalized and non-invasive health screening and diagnostics techniques. However, the wide range of contradictory results in breath analysis is explained by the lack of an optimal standard procedure for selective breath sampling. Recently we developed novel instrumentation for selective breath sampling, enabling the precise collection of a pre-determined portion of exhaled air using AI (Machine Learning) algorithm. This work presents pilot study results for validation of developed technology by differentiation of alveolar and oesophagal air obtained from the healthy population (n = 31). The samples were analyzed in-situ by Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) apparatus, and obtained spectra were processed with proper multivariate classification tools. The results show a promising performance of proposed AI-based technology for breath sampling adapted to users’ age, genre, and physiological conditions.
AB - Breath analysis is an expanding scientific field with great potential for creating personalized and non-invasive health screening and diagnostics techniques. However, the wide range of contradictory results in breath analysis is explained by the lack of an optimal standard procedure for selective breath sampling. Recently we developed novel instrumentation for selective breath sampling, enabling the precise collection of a pre-determined portion of exhaled air using AI (Machine Learning) algorithm. This work presents pilot study results for validation of developed technology by differentiation of alveolar and oesophagal air obtained from the healthy population (n = 31). The samples were analyzed in-situ by Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) apparatus, and obtained spectra were processed with proper multivariate classification tools. The results show a promising performance of proposed AI-based technology for breath sampling adapted to users’ age, genre, and physiological conditions.
KW - Alveolar air
KW - Breath sampling
KW - Machine learning
KW - Medical instrumentation
KW - Principal Component Analysis
KW - Selective air acquisition
UR - https://www.scopus.com/pages/publications/85112000704
U2 - 10.1007/978-3-030-78288-7_32
DO - 10.1007/978-3-030-78288-7_32
M3 - Conference Publication
AN - SCOPUS:85112000704
SN - 9783030782870
T3 - IFIP Advances in Information and Communication Technology
SP - 331
EP - 338
BT - Technological Innovation for Applied AI Systems - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferreira, Pedro
A2 - Brito, Guilherme
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021
Y2 - 7 July 2021 through 9 July 2021
ER -