Spectral analysis of human exhaled breath for early diagnosis of diseases using different machine learning methods
| Autor: | Elizaveta R. Kareva, Igor S. Golyak, Andrey N. Morozov, Pavel P. Demkin, Igor L. Fufurin, Dmitriy R. Anfimov, Anastasiya V. Scherbakova |
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| Rok vydání: | 2021 |
| Předmět: |
Physics
Neutral network Biomedical spectroscopy Artificial neural network business.industry Machine learning computer.software_genre law.invention law Principal component analysis Range (statistics) Spectral analysis Artificial intelligence Quantum cascade laser business computer Optical path length |
| Zdroj: | Saratov Fall Meeting 2020: Optical and Nanotechnologies for Biology and Medicine. |
| DOI: | 10.1117/12.2590835 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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