Deep learning for human breath research using infrared quantum cascade laser spectroscopy
Igor L. Fufurin, 1
Pavel V. Berezhanskiy, 2
Igor S. Golyak, 1
Dmitriy R. Anfimov, 1,
Anastasiya V. Scherbakova, 1
Pavel P. Demkin, 1
Olga A. Nebritova, 1
Andrey N. Morozov, 1
1 Bauman Moscow State Technical University, Moscow, Russia
2 Morozov Children’s Clinical Hospital, State Budgetary Healthcare Institution, Moscow Healthcare Pulmonology Department, Moscow, Russia
Abstract
Human breath research method based on infrared laser spectroscopy is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We chose type 1 diabetes mellitus as a studied disease. We have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed diabetes mellitus (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers allows to get an accuracy, recall and AUC score more than 99%.
Speaker
Igor L. Fufurin
Bauman Moscow State Technical University
Russia
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