A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases
Corresponding Author
Igor Semenovich Golyak
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Correspondence
Igor Semenovich Golyak, Department of Physics, Bauman Moscow State Technical University, 105005, Moscow, Russia.
Email: [email protected]
Search for more papers by this authorDmitriy Romanovich Anfimov
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorPavel Pavlovich Demkin
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorPavel Vyacheslavovich Berezhanskiy
Sechenov First Moscow State Medical University, Moscow, Russia
Search for more papers by this authorOlga Aleksandrovna Nebritova
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorAndrey Nikolaevich Morozov
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorIgor Leonidovich Fufurin
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorCorresponding Author
Igor Semenovich Golyak
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Correspondence
Igor Semenovich Golyak, Department of Physics, Bauman Moscow State Technical University, 105005, Moscow, Russia.
Email: [email protected]
Search for more papers by this authorDmitriy Romanovich Anfimov
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorPavel Pavlovich Demkin
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorPavel Vyacheslavovich Berezhanskiy
Sechenov First Moscow State Medical University, Moscow, Russia
Search for more papers by this authorOlga Aleksandrovna Nebritova
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorAndrey Nikolaevich Morozov
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorIgor Leonidovich Fufurin
Department of Physics, Bauman Moscow State Technical University, Moscow, Russia
Search for more papers by this authorAbstract
Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm (780–1890 cm−1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.
CONFLICT OF INTEREST STATEMENT
The authors declare no financial or commercial conflicts of interest exist.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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