TY - GEN
T1 - Pilot Study for Validation and Differentiation of Alveolar and Esophageal Air
AU - Santos, Paulo Henrique da Costa
AU - Vassilenko, Valentina
AU - Conduto, Carolina
AU - Fernandes, Jorge
AU - Moura, Pedro Rafael Catalão
AU - Bonifácio, Paulo Jorge dos Santos
N1 - Funding Information:
The authors thank all volunteers for participating in the study. The work benefitted from the continuous support of the combined effort of NOVA School of Science and Technology and NMT, S.A. Partial support came from Funda??o para a Ci?ncia e Tecnologia (FCT, Portugal) through the PhD grant (PD/BDE/114550/2016).
Funding Information:
Acknowledgments. The authors thank all volunteers for participating in the study. The work benefitted from the continuous support of the combined effort of NOVA School of Science and Technology and NMT, S.A. Partial support came from Fundação para a Ciência e Tecnologia (FCT, Portugal) through the PhD grant (PD/BDE/114550/2016).
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 - http://www.scopus.com/inward/record.url?scp=85112000704&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78288-7_32
DO - 10.1007/978-3-030-78288-7_32
M3 - Conference contribution
SN - 978-3-030-78287-0
SN - 978-3-030-78290-0
T3 - IFIP Advances in Information and Communication Technology
SP - 331
EP - 338
BT - Technological Innovation for Applied AI Systems. DoCEIS 2021
A2 - Camarinha-Matos, Luis M.
A2 - Ferreira, Pedro
A2 - Brito, Guilherme
PB - Springer
CY - Cham
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 -