101 research outputs found
Determination of component concentrations in models of exhaled air samples using principal component analysis and canonical correlation analysis
We consider the problem of finding concentrations of molecular gases in the models of exhaled air samples in terms of their absorption spectra. We introduce model spectra describing the exhaled air samples as linear combinations of the absorption spectra of individual molecular gases with given coefficients. The absorption spectra are calculated on the basis of the database HITRAN. The concentrations are determined using Principal Component Analysis and Canonical Correlation Analysis
Kalman filtering in the problem of noise reduction in the absorption spectra of exhaled air
We examined possibilities of the Kalman filter for reducing the noise effects in the analysis of absorption spectra of gas samples, in particular, for samples of the exhaled air. It has been shown that when comparing groups of patients with broncho-pulmonary diseases on the basis of the absorption spectra analysis of exhaled air samples the data preprocessing with the Kalman filtering can improve the classification sensitivity using a support vector kernel with mpl
Possibilities of laser spectroscopy for monitoring the profile dynamics of the volatile metabolite in exhaled air
In this work we studied applicability of the laser spectroscopy for fixing differences in composition of exhaled air depending on the position of the body in different physical states. Using principal component analysis we show that the use of the laser spectroscopy methods is sufficiently effective to solve this problem and provide additional opportunities for the comprehensive study of the human condition
The classification of the patients with pulmonary diseases using breath air samples spectral analysis
Technique of exhaled breath sampling is discussed. The procedure of wavelength auto-calibration is proposed and tested. Comparison of the experimental data with the model absorption spectra of 5% CO2 is conducted. The classification results of three study groups obtained by using support vector machine and principal component analysis methods are presented
Predictive models for COVID-19 detection using routine blood tests and machine learning
The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning
Diagnostics of oral lichen planus based on analysis of volatile organic compounds in saliva
The ability of diagnostics of oral lichen planus (OLP) based on spectral analysis of saliva using the THz spectroscopy is presented. The study included 8 patients with clinically proven OLP. The comparison group consisted of 8 healthy volunteers. Absorption spectra of the saliva was measured using time-domain spectrometer T-spec (EXPLA) in the range 0.2-3THz and have been considered as the feature vectors of the state. The spatial distribution of the objects under study in the feature space was analyzed using principle component analysis. The groups under study were shown to separate in full. Thus, the saliva analysis by the THz spectroscopy technique can be potentially used as a method of noninvasive diagnostics of the OLP
Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application
In this work results of classification of patients with broncho-pulmonary diseases based on analysis of exhaled air samples are presented. These results obtained by application of laser photoacoustic spectroscopy method and intellectual data analysis ones (Principal Component Analysis, Support vector machines, neural networks). Absorption spectra of exhaled air of gathered volunteers were registered; data preparation for classification procedure of absorption spectra of exhaled air of healthy and sick people was made. Also error matrices for neural networks and sensitivity/specificity values in case of classification with SVM method were obtained. This work was partially supposed by the Federal Target Program for Research and Development, Contract No. 14.578.21.0082 (unique identifier of applied scientific research and experimental development RFMEFI57814X0082)
Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique
This study presents the results of the classification of diffuse reflectance (DR) spectra and multiexcitation autofluorescence (AF) spectra that were collected in vivo from precancerous and benign skin lesions at three different source detector separation (SDS) values. Spectra processing pipeline consisted of dimensionality reduction, which was performed using principal component analysis (PCA), followed by classification step using such methods as support vector machine (SVM), multilayered perceptron (MLP), linear discriminant analysis (LDA), and random forest (RF). In order to increase the efficiency of lesion classification, several data fusion methods were applied to the classification results: majority voting, stacking, and manual optimization of weights. The results of the study showed that in most of cases the use of data fusion methods increased the average multiclass classification accuracy from 2% up to 4%. The highest accuracy of multiclass classification was obtained using the manual optimization of weights and reached 94.41%
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