Spectral analysis of human exhaled breath for early diagnosis of diseases using different machine learning methods

Igor S. Golyak; Igor L. Fufurin; Elizaveta R. Kareva; Dmitriy R. Anfimov; Anastasiya V. Scherbakova; Andrey N. Morozov; Pavel P. Demkin

doi:10.1117/12.2590835

4 May 2021 Spectral analysis of human exhaled breath for early diagnosis of diseases using different machine learning methods

Igor S. Golyak, Igor L. Fufurin, Elizaveta R. Kareva, Dmitriy R. Anfimov, Anastasiya V. Scherbakova, Andrey N. Morozov, Pavel P. Demkin

Author Affiliations +

Proceedings Volume 11845, Saratov Fall Meeting 2020: Optical and Nanotechnologies for Biology and Medicine; 118450R (2021) https://doi.org/10.1117/12.2590835
Event: Saratov Fall Meeting 2020, 2020, Saratov, Russian Federation

Abstract

In this work, the possibility of using machine learning in the spectral analysis of exhaled breath for early diagnosis of diseases is considered. Experimental setup consists of a quantum cascade laser with a tuning range of 5.4-12.8 μm and Herriot astigmatic gas cell. A shallow convolutional neutral network and principal component analysis is used to identify biomarkers and its mixtures. A minimum detectable concentration for acetone and ethanol at sub-ppm level is obtained for optical path length up to 6 m and signal-to-noise less than 3. It is shown that neural networks in comparison with statistical methods give a lower detection limits for the same signal-to-noise ratio in the measured spectrum.

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Igor S. Golyak, Igor L. Fufurin, Elizaveta R. Kareva, Dmitriy R. Anfimov, Anastasiya V. Scherbakova, Andrey N. Morozov, and Pavel P. Demkin "Spectral analysis of human exhaled breath for early diagnosis of diseases using different machine learning methods", Proc. SPIE 11845, Saratov Fall Meeting 2020: Optical and Nanotechnologies for Biology and Medicine, 118450R (4 May 2021); https://doi.org/10.1117/12.2590835

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